U.S. patent application number 15/987804 was filed with the patent office on 2019-07-18 for waveform visualization tool for facilitating medical diagnosis.
The applicant listed for this patent is Neural Analytics, Inc.. Invention is credited to Nicolas Canac, Robert Hamilton, Michael O'Brien, Mina Ranjbaran, Corey Thibeault, Samuel Thorpe.
Application Number | 20190216421 15/987804 |
Document ID | / |
Family ID | 62223297 |
Filed Date | 2019-07-18 |
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United States Patent
Application |
20190216421 |
Kind Code |
A1 |
Hamilton; Robert ; et
al. |
July 18, 2019 |
WAVEFORM VISUALIZATION TOOL FOR FACILITATING MEDICAL DIAGNOSIS
Abstract
A tool for facilitating medical diagnosis is disclosed herein,
including an ultrasound device configured to collect ultrasound
data from a patient, a display device, and a processing circuit
configured to generate a cerebral blood flow velocity (CBFV)
waveform based on the ultrasound data, determine morphology
indicators identifying attributes of the CBFV waveform, and
configure the display device to display the CBFV waveform and the
morphology indicators.
Inventors: |
Hamilton; Robert; (Los
Angeles, CA) ; Thibeault; Corey; (Los Angeles,
CA) ; O'Brien; Michael; (Los Angeles, CA) ;
Ranjbaran; Mina; (Los Angeles, CA) ; Thorpe;
Samuel; (Los Angeles, CA) ; Canac; Nicolas;
(Los Angeles, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Neural Analytics, Inc. |
Los Angeles |
CA |
US |
|
|
Family ID: |
62223297 |
Appl. No.: |
15/987804 |
Filed: |
May 23, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15971260 |
May 4, 2018 |
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15987804 |
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62619015 |
Jan 18, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 8/4281 20130101;
A61B 8/5207 20130101; A61B 8/4236 20130101; G01S 15/8979 20130101;
A61B 8/0808 20130101; A61B 8/466 20130101; A61B 8/4477 20130101;
A61B 8/5223 20130101; A61B 8/488 20130101; A61B 8/0891 20130101;
A61B 8/4218 20130101; A61B 8/4245 20130101; A61B 8/0816 20130101;
A61B 8/06 20130101; A61B 8/4227 20130101; A61B 8/5292 20130101;
A61B 8/463 20130101; A61B 8/461 20130101 |
International
Class: |
A61B 8/06 20060101
A61B008/06; A61B 8/00 20060101 A61B008/00; A61B 8/08 20060101
A61B008/08 |
Goverment Interests
STATEMENT OF GOVERNMENT SUPPORT
[0002] This invention was made with government support under Grant
No. 1556110 awarded by the National Science Foundation. The
government has certain rights in the invention.
Claims
1. A tool for identifying a waveform pulse of a first signal
representing a physiological characteristic of a subject,
comprising: an ultrasound device configured to receive and collect
the first signal representing the physiological characteristic of
the subject; and a processing circuit configured to: generate a
second signal based on the first signal, the second signal
representing change within the first signal; identify a sharp
upslope of the first signal based on the second signal; determine a
search window along the first signal that begins around the
identified sharp upslope; and identify, within the search window,
an onset of the waveform pulse.
2. The tool of claim 1, wherein the second signal reflects a net
change within the first signal over a predetermined time
interval.
3. The tool of claim 1, wherein the second signal is a slope sum
function (SSF) signal of the first signal.
4. (canceled)
5. The tool of claim 1, wherein the processing circuit is
configured to determine the search window along the first signal
by: determining a threshold; detecting a crossing point between a
location along the second signal and the threshold, the crossing
point corresponding to a first time value; and defining the search
window as being between the first time value and a second time
value prior to the first time value.
6. The tool of claim 5, wherein the second time value corresponds
to a feature of a previously detected waveform pulse of the first
signal that immediately precedes the first time value.
7. The tool of claim 6, wherein the feature comprises a peak of the
previously detected waveform pulse.
8. The tool of claim 5, wherein the second time value precedes the
first time value by a predetermined time interval.
9. The tool of claim 5, wherein the processing circuit is
configured to determine the threshold based on a predetermined
number of preceding peaks of the second signal.
10. The tool of claim 9, wherein the threshold is a percentage of
an average of the predetermined number of the preceding peaks of
the second signal.
11. The tool of claim 5, wherein the processing circuit is
configured to determine the threshold based on all preceding peaks
in the second signal that exceed a peak threshold.
12. The tool of claim 11, wherein the peak threshold is a multiple
of an average of the second signal.
13. The tool of claim 5, wherein the processing circuit is further
configured to: apply a refractory time period starting at the first
time value; and refrain from detecting new threshold crossing
points until after the refractory time period.
14. The tool of claim 5, wherein the processing circuit is
configured to identify the onset by: identifying one or more
valleys of the first signal within the search window; and selecting
one of the valleys as the onset.
15. The tool of claim 14, wherein the processing circuit is
configured to select the one of the valleys as the onset by
determining that the selected one of the valleys is a closest
valley of the one or more valleys to the first time value.
16. The tool of claim 15, wherein the processing circuit is further
configured to select the one of the valleys as the onset by
determining that a peak value of a pulse of the first signal minus
a value of the selected one of the valleys is greater or equal to a
factor multiplied by a peak value of the second signal.
17. The tool of claim 1, wherein the processing circuit is further
configured to: determine corresponding length values of a plurality
of identified waveform pulses of the first signal; perform a
comparison of a first one of the determined length values to a
reference value; and detect whether the first one of the determined
length value is a long waveform pulse or a short waveform pulse
based on the comparison.
18. The tool of claim 17, wherein the processing circuit is further
configured to determine the reference value based on the determined
length values.
19. The tool of claim 17, wherein the reference value is a median
value of the determined length values.
20. The tool of claim 17, wherein the processing circuit is further
configured to, in response to detecting the long waveform pulse,
identify another onset within the long waveform pulse.
21. The tool of claim 17, wherein the processing circuit is further
configured to, in response to determining the short waveform pulse,
combine the short waveform pulse with an adjacent waveform pulse to
the short waveform pulse.
22. The tool of claim 1, wherein the first signal comprises a
continuous cerebral blood flow velocity (CBFV) signal from the
subject.
23. The tool of claim 1, wherein the processing circuit is further
configured to adjust at least one of a position or an orientation
of the ultrasound device based on the waveform pulse.
24. (canceled)
25. The tool of claim 1, further comprising a display device,
wherein the processing circuit is further configured to display the
waveform pulse at the display device.
26. The tool of claim 25, wherein the processing circuit is further
configured to determine a morphology indicator configured to
visually indicate an attribute of the waveform pulse and display
the morphology indicator at the display device.
27. The tool of claim 26, wherein the morphology indicator
indicates at least one of a first peak of the waveform pulse, a
second peak of the waveform pulse, or a distance between the first
peak and the second peak.
28. The tool of claim 1, wherein the processing circuit is further
configured to: receive a third signal from at least one of an
electrocardiogram device, a pulse oximetry device, or a heartrate
monitor; and confirm that the identified waveform pulse is accurate
based on the third signal.
29. A method for identifying a waveform pulse of a first signal
representing a physiological characteristic of a subject,
comprising: receiving and collecting the first signal representing
the physiological characteristic of the subject; generating a
second signal based on the first signal, the second signal
representing change within the first signal; identifying a sharp
upslope of the first signal based on the second signal; determining
a search window along the first signal that begins around the
identified sharp upslope; and identifying, within the search
window, an onset of the waveform pulse.
30. A non-transitory processor-readable medium storing
processor-readable instructions such that, when executed, causes a
processor to identify a waveform pulse of a first signal
representing a physiological characteristic of a subject by:
receiving and collecting the first signal representing the
physiological characteristic of the subject; generating a second
signal based on the first signal, the second signal representing
change within the first signal; identifying a sharp upslope of the
first signal based on the second signal; determining a search
window along the first signal that begins around the identified
sharp upslope; and identifying, within the search window of the
first signal, an onset of the waveform pulse.
31. The tool of claim 5, wherein the search window is defined by
window locations; the window locations comprise a start window
location and an end window location; the start window location
corresponds to the second time value; and the end window location
corresponds to the first time value.
32. The tool of claim 1, wherein the sharp upslope is identified
based on a relatively large change within a portion of the first
signal compared to a remainder of the first signal.
Description
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS
[0001] This application is a Continuation of U.S. patent
application Ser. No. 15/971,260, filed May 4, 2018, which claims
priority to, and the benefit of, U.S. provisional patent
application Ser. No. 62/619,015, titled WAVEFORM VISUALIZATION TOOL
FOR FACILITATING MEDICAL DIAGNOSIS, and filed on Jan. 18, 2018,
which are both incorporated herein by reference in their
entirety.
FIELD
[0003] Subject matter described herein relates generally to medical
devices, and more particularly to a headset including a transducer
and an output device for diagnosing medical conditions.
BACKGROUND
[0004] Clinical guidelines recommend monitoring for medical
conditions including stroke, emboli, stenosis, vasospasm as well as
elevated intracranial pressure (ICP) which may alter cerebral blood
flow. For instance, monitoring is performed for patients with
severe traumatic brain injury (TBI), subarachnoid hemorrhage (SAH),
and other conditions with a considerable risk of elevated ICP,
because elevated ICP can lead to death or serious injury.
Conventionally, a reliable method for monitoring a patient's ICP is
a neurosurgeon invasively placing a pressure probe into the brain
tissue or cerebral ventricles. Such method is costly, invasive,
prone to infection, and is limited to in-hospital usage. As a
result, ICP monitoring is infrequently performed.
[0005] Transcranial Doppler (TCD) devices can perform non-invasive,
cerebral blood flow monitoring using ultrasound which can be used
for a number of medical conditions including those listed above.
However, displays and screens on conventional TCDs show simple
waveforms without any diagnostic visualization that can assist a
physician with equipment calibration or diagnosis in real-time.
[0006] Acquiring the cerebral blood flow velocity (CBFV) signals
using TCD requires placement of a transducer within a specific
region of the skull thin enough for the ultrasound waves to
penetrate, locating a signal of the artery of interest, and
maintaining a steady position for sufficient sample size. The
location of these narrow windows varies significantly from person
to person. Additionally, reading and interpreting the scans once
complete is difficult because subtle features and changes in the
CBFV waveforms that indicate neurological disorders are not easily
discernible using traditional TCD analysis or visual inspection.
These requirements make insonating (i.e., exposing to ultrasound)
the desired blood vessel difficult, thus restricting TCD use to
major hospitals with expensive, on staff expert human sonographers
to operate the device as well as reducing the overall utility of
the device through utilization of only simple analysis.
[0007] With respect to stroke detection, interventional (e.g.,
stentriever) and pharmaceutical (e.g., tissue plasminogen activator
(tPA)) treatments for large vessel occlusion (LVO) need to be
administered within a short duration from symptom onset.
Conventional standards for stroke diagnosis involves computed
tomography angiography (CTA) machines, which are limited to
in-hospital uses and a small number of stroke ambulances, due to
high cost, requirement of expert operators, and intravenous (IV)
injection of iodine-rich contrast material.
SUMMARY
[0008] In some arrangements, a tool for facilitating medical
diagnosis includes an ultrasound device, wherein the ultrasound
device is configured to collect ultrasound data from a patient, a
display device, and a processing circuit configured to generate a
CBFV waveform based on the ultrasound data, determine morphology
indicators identifying attributes of the CBFV waveform, and
configure the display device to display the CBFV waveform and the
morphology indicators.
[0009] In some arrangements, the display device is configured to
display the CBFV waveform and the morphology indicators in real
time or semi-real time as the ultrasound data is being
collected.
[0010] In some arrangements, the processing circuit generates the
CBFV waveform based on the ultrasound data by generating a
plurality of CBFV waveforms based on the ultrasound data, each CBFV
waveform corresponding to a pulse, and the CBFV waveform used for
morphology calculation is derived from the plurality of CBFV
waveforms.
[0011] In some arrangements, configuring the display device to
display the CBFV waveform and the morphology indicators includes
configuring the display device to display the plurality of CBFV
waveforms in a first display window.
[0012] In some arrangements, configuring the display device to
display the CBFV waveform and the morphology indicators includes
configuring the display device to display the CBFV waveform and the
morphology indicators in a second display window.
[0013] In some arrangements, the tool further includes deriving the
CBFV waveform from the plurality of CBFV waveforms by one or more
of filtering the plurality of CBFV waveforms and averaging the
plurality of CBFV waveforms.
[0014] In some arrangements, determining the morphology indicators
identifying the attributes of the CBFV waveform includes segmenting
a plurality detected CBFV waveforms into distinct CBFV waveforms,
and identifying the attributes for the CBFV waveform that is an
average of the distinct CBFV waveforms.
[0015] In some arrangements, the attributes include at least one
peak on the CBFV waveform.
[0016] In some arrangements, configuring the display device to
display the CBFV waveform and the morphology indicators includes
configuring the display device to display a peak indicator
corresponding to each of the at least one peak of the CBFV
waveform.
[0017] In some arrangements, the processing circuit is further
configured to use machine learning to determine that the patient is
experiencing a medical condition based on the morphology
indicators, and configure the display device to display a
notification related to the medical condition.
[0018] In some arrangements, in response to determining that the
patient is experiencing the medical condition, the processing
circuit is further configured to send an email, a page, or a short
message service (SMS) message to an operator, or call the
operator.
[0019] In some arrangements, in response to determining that the
patient is experiencing the medical condition, the processing
circuit further configures a medical device to administer a drug to
treat the medical condition.
[0020] In some arrangements, the processing circuit is further
configured to determine that a probe of the ultrasound device is
misaligned based on the morphology indicators, and automatically
adjust a position of the probe.
[0021] In some arrangements, the processing circuit determines that
the probe of the ultrasound device is misaligned based on machine
learning.
[0022] In some arrangements, a method for facilitating medical
diagnosis, includes collecting, with an ultrasound device,
ultrasound data from a patient, generating a CBFV waveform based on
the ultrasound data, determining morphology indicators identifying
attributes of the CBFV waveform, and displaying the CBFV waveform
and the morphology indicators.
[0023] In some arrangements, the CBFV waveform and the morphology
indicators are displayed in real time or semi-real time as the
ultrasound data is being collected.
[0024] In some arrangements, the CBFV waveform is generated by
generating a plurality of CBFV waveforms based on the ultrasound
data, each CBFV waveform corresponding to a pulse, and deriving the
CBFV waveform from the plurality of CBFV waveforms.
[0025] In some arrangements, displaying the CBFV waveform and the
morphology indicators includes displaying the plurality of CBFV
waveforms in a first display window, and displaying the CBFV
waveform and the morphology indicators in a second display
window.
[0026] In some arrangements, determining the morphology indicators
identifying the attributes of the CBFV waveform includes segmenting
a plurality detected CBFV waveforms into distinct CBFV waveforms,
and identifying the attributes for the CBFV waveform that is an
average of the distinct CBFV waveforms.
[0027] In some arrangements, the attributes include at least one
peak on the CBFV waveform, and displaying the CBFV waveform and the
morphology indicators includes displaying a peak indicator
corresponding to each of the at least one peak of the CBFV
waveform.
[0028] In some arrangements, a non-transitory processor-readable
medium storing processor-readable instructions such that, when
executed, causes a processor to facilitate medical diagnosis by
collecting ultrasound data from a patient, generating a CBFV
waveform based on the ultrasound data, determining morphology
indicators identifying attributes of the CBFV waveform, and
displaying the CBFV waveform and the morphology indicators.
BRIEF DESCRIPTION OF THE FIGURES
[0029] Features, aspects, and advantages of the present invention
will become apparent from the following description and the
accompanying example arrangements shown in the drawings, which are
briefly described below.
[0030] FIG. 1 is a schematic diagram illustrating a waveform
visualization system according to various arrangements.
[0031] FIG. 2 is a schematic block diagram illustrating a waveform
visualization system according to various arrangements.
[0032] FIG. 3 is a processing flow diagram illustrating a method
for facilitating medical diagnosis using the waveform visualization
system (FIG. 1) according to various arrangements.
[0033] FIG. 4 is a display interface showing a CBFV output diagram
and a CBFV waveform diagram of the patient (FIG. 1) according to
various arrangements.
[0034] FIG. 5A is a CBFV waveform diagram of a healthy individual
according to various arrangements.
[0035] FIG. 5B is a CBFV waveform diagram of a patient suffering
from idiopathic intracranial hypertension (IIH) according to
various arrangements.
[0036] FIG. 5C is a CBFV waveform diagram of a healthy individual
(left) and a CBFV waveform diagram of a patient suffering from LVO
(right) according to various arrangements.
[0037] FIG. 6A is a display interface showing CBFV waveform
diagrams associated with a left middle cerebral artery (LMCA) of a
patient and CBFV waveform diagrams associated with a right middle
cerebral artery (RMCA) of the patient according to various
arrangements.
[0038] FIG. 6B is a display interface showing a CBFV waveform
diagram associated with an LMCA of a patient and a CBFV waveform
diagram associated with an RMCA of the patient superimposed on one
another according to various arrangements.
[0039] FIG. 6C is a display interface showing an RMCA velocity
versus an LMCA velocity diagram associated with a patient according
to various arrangements.
[0040] FIG. 7 is a display interface showing a trending window
according to various arrangements.
[0041] FIG. 8 is a processing flow diagram illustrating a method
for extracting CBFV waveforms according to various
arrangements.
[0042] FIG. 9 is a CBFV output diagram showing an example CBFV
output and a slope sum function (SSF) corresponding to the CBFV
output according to various arrangements.
[0043] FIG. 10 is a display interface showing an attribute
distribution associated with a number of CBFV waveforms according
to various arrangements.
DETAILED DESCRIPTION
[0044] The detailed description set forth below in connection with
the appended drawings is intended as a description of various
configurations and is not intended to represent the only
configurations in which the concepts described herein may be
practiced. The detailed description includes specific details for
providing a thorough understanding of various concepts. However, it
will be apparent to those skilled in the art that these concepts
may be practiced without these specific details. In some instances,
well-known structures and components are shown in block diagram
form in order to avoid obscuring such concepts.
[0045] In the following description of various arrangements,
reference is made to the accompanying drawings which form a part
hereof and in which are shown, by way of illustration, specific
arrangements in which the arrangements may be practiced. It is to
be understood that other arrangements may be utilized, and
structural changes may be made without departing from the scope of
the various arrangements disclosed in the present disclosure.
[0046] Arrangements described herein relate to apparatuses,
systems, methods, and non-transitory computer-readable medium that
provide affordable, non-invasive TCD devices in hospital and
field-based (pre-hospital) settings. Such TCD devices can be used
for continuously monitoring CBFV, among other parameters. As a
diagnostic tool that assists a physician with equipment calibration
(e.g., probe positioning) or diagnosis in real-time or semi-real
time, arrangements described herein include a TCD ultrasound device
configured to measure CBFV. The TCD ultrasound device is
operatively coupled to a display screen configured to display
visual indicators that identify the morphology of the CBFV
waveforms in the CBFV output in real-time or semi-real time, to
assist an operator with equipment calibration (e.g., probe
positioning) and diagnosis. Such arrangements are directed to
improving TCD devices by presenting useful morphological
information to the operator. The operator conventionally uses his
or her human judgment to determine whether a CBFV waveform as a
whole appears to be problematic, without being able to identify
morphological attributes for detailed analysis in real-time or
semi-real-time.
[0047] In addition, the equipment calibration and diagnosis based
on CBFV waveform indicators can be automatically executed, in
addition or alternative to displaying the visual indicators to the
operator. No conventional medical devices can perform automated
equipment calibration or diagnosis based on the CBFV waveform
indicators. Thus, such arrangements automate a process not
previously automated.
[0048] Arrangements described herein relate to apparatuses,
systems, methods, and non-transitory computer-readable medium that
provide a standardized, quantitative, and non-invasive diagnostic
tool capable of providing improved large vessel occlusion (LVO)
identification in hospital and field-based (pre-hospital) settings.
Such a diagnostic tool includes TCD devices coupled with machine
learning for rapid stroke diagnosis and allows a patient to be
monitored while en route to a hospital, thus bridging a gap between
incidence detection and hospital treatment.
[0049] FIG. 1 is a schematic diagram illustrating a waveform
visualization system 100 according to various arrangements.
Referring to FIG. 1, the waveform visualization system 100 includes
at least a headset device 110, a controller 130, and an output
device 140.
[0050] The headset device 110 is a TCD ultrasound device configured
to emit and measure acoustic energy in a head 102 of a patient 101.
An example of the headset device 110 is a supine headset device.
The headset device 110 includes at least one probe 105 (e.g., at
least one ultrasound probe) configured to emit and measure
ultrasound acoustic energy in the head 102. For example, the probe
105 includes at least one TCD scanner, which can automatically
locate the middle cerebral artery (MCA) in some arrangements. At
least one probe 105 can be positioned in a temporal window region
(temple) of the head 102 to collect the ultrasound data. In other
arrangements, the probe can be positioned over different acoustic
windows such as the transorbital window or the suboccipital window.
In some arrangements, headset 110 includes two ultrasound probes
105, which can be placed on the temporal window region on both
sides of the head 102. A headband, strap, Velcro.RTM., hat, helmet,
or another suitable wearable structure of the like connects the two
probes in such arrangements. A lubricating gel can be applied
between the head 102 and the probe 105 to improve acoustic
transmission.
[0051] The controller 130 is configured to receive the ultrasound
data outputted by the headset device 110 and to generate CBFV
waveforms that correspond to the ultrasound data. In that regard,
the probe 110 is operatively coupled to the controller 130 via a
suitable network 120 to send the ultrasound data to the controller
130. The network 120 can be wired or wireless (e.g., 802.11X,
ZigBee, Bluetooth.RTM., Wi-Fi, or the like). The controller 130 can
further perform signal processing functions to determine and
display morphological indicators corresponding to the CBFV
waveforms to facilitate a physician, clinician, technician, or care
provider with diagnosis and/or to adjust the positioning of the
headset device 110 and the probe 105. Further, as described, the
headset device 110 can automatically adjust the position and
orientation of the probe 105 responsive to determination that the
probe 105 is not optimally placed based on the morphological
indicators in the manner described herein. In some arrangements,
the controller 130, the output device 140, and a portion of the
network 120 are incorporated into a single device (e.g., a
touchscreen tablet device).
[0052] In some arrangements, the output device 140 includes any
suitable device configured to display information, results,
messages, and the like to an operator (e.g., a physician,
clinician, technician, or care provider) of the waveform
visualization system 100. For example, the output device 140
includes but is not limited to, a monitor, a touchscreen, or any
other output device configured to display the CBFV waveforms, the
morphology indicators, and the like for facilitating diagnosis
and/or the positioning of the headset device 110 and the probe 105
relative to the head 102 in the manner described.
[0053] FIG. 2 is a schematic block diagram illustrating the
waveform visualization system 100 (FIG. 1) according to various
arrangements. Referring to FIGS. 1-2, the headset device 110
includes the probe 105 as described. Further disclosure regarding
examples of the probe 105 that can be used in conjunction with the
waveform visualization system 100 described herein can be found in
non-provisional patent application Ser. No. 15/399,648, titled
ROBOTIC SYSTEMS FOR CONTROL OF AN ULTRASONIC PROBE, and filed on
Jan. 5, 2017, which is incorporated herein by reference in its
entirety. In some arrangements, the headset device 110 includes
manually operated probes, as opposed to automatically or
robotically-operated probes.
[0054] In some arrangements, the headset device 110 includes
robotics 214 configured to control positioning of the probe 105.
For example, the robotics 214 are configured to translate the probe
105 along a surface of the head 102 and to move the probe 105 with
respect to (e.g., toward and away from) the head 102 along various
axes in the Cartesian, spherical, and rotational coordinate
systems. In particular, the robotics 214 can include a multiple
degree of freedom (DOF) TCD transducer positioning system with
motion planning. In some embodiments, the robotics 214 are capable
of supporting two, three, four, five, or six DOF movements of the
probe 105 with respect to the head 102. In some instances, the
robotics 214 can translate in X and Y axes (e.g., along a surface
of the head 102) to locate a temporal window region in
translational axes, and in Z axis with both force and position
feedback control to both position, and maintain the appropriate
force against the skull/skin to maximize signal quality by
maintaining appropriate contact force. Two angular DOF (e.g., pan
and tilt) may be used to maximize normal insonation of blood
vessels to maximize velocity signals.
[0055] In some arrangements, an end of the probe 105 is operatively
coupled to or otherwise interfaces with the robotics 214. The
robotics 214 include components, such as but not limited to a motor
assembly and the like for controlling the positioning of the probe
105 (e.g., controlling z-axis pressure, normal alignment, or the
like of the probe 105). In some arrangements, the registration of
the probe 105 against the head 105 is accomplished using the
robotics 214 to properly position and align the probe 105 in the
manner described.
[0056] In some arrangements, the probe 105 includes a first end and
a second end that is opposite to the first end. In some
arrangements, the first end includes a concave surface that is
configured to be adjacent to or contact a scanning surface on the
head 102. The concave surface is configured with a particular pitch
to focus generated energy towards the scanning surface. In some
arrangements, the headset device 110 is a TCD apparatus such that
the first end of the probe 105 is configured to be adjacent to or
contact and align along a side of the head 102. The first end of
the probe 105 is configured to provide ultrasound wave emissions
from the first end and directed into the head 102 (e.g., toward the
brain). For example, the first end of the probe 105 can include a
transducer (such as, but not limited to, an ultrasound transducer,
TCD, transcranial color-coded sonography (TCCS), or acoustic
ultrasound transducer array such as sequential arrays or phased
arrays) that emits acoustic energy capable of penetrating windows
in the skull/head or neck. In other arrangements, the probe 105 is
configured to emit other types of waves during operation, such as,
but not limited to, infrared (IR), near-infrared spectroscopy
(NIRS), electro-magnetic, x-rays, or the like.
[0057] In some arrangements, the second end of the probe 105 is
coupled to the robotics 214. In some arrangements, the second end
of the probe 105 includes a threaded section along a portion of the
body of the probe 105. The second end is configured to be secured
in the robotics 214 via the threads (e.g., by being screwed into
the robotics 214). In other arrangements, the probe 105 is secured
in the robotics 214 by any other suitable connecting means, such as
but not limited to welding, adhesive, one or more hooks and
latches, one or more separate screws, press fittings, or the
like.
[0058] The headset device 110 can further include a structural
support 216 configured to support the head 102 of the patient 101
and/or to support the headset device 110 on the head 102 or other
parts of a body of the patient 101. In some examples, the
structural support 216 includes a platform (e.g., a baseplate) that
allows the patient 101 to lay down on a flat surface in a reclined
or supine position while the headset device 110 is operational.
Further disclosure regarding such implementation of the structural
support 216 that can be used in conjunction with the waveform
visualization system 100 described herein can be found in
non-provisional patent application Ser. No. 15/853,433, titled
HEADSET SYSTEM, and filed on Dec. 22, 2017, which is incorporated
herein by reference in its entirety. In other examples, the
structural support 216 includes one or more of a mount, cradle,
headband, strap, Velcro.RTM., hat, helmet, or another suitable
wearable structure of the like such that the patient 101 can wear
the headset device 110 on the head 102, shoulders, neck, and/or the
like when the patient 101 is sitting, standing, or lying down. The
structural support 216 can be made from any suitably malleable
material that allows for flexing, such as, but not limited to,
flexible plastics, polyethylene, urethanes, polypropylene, ABS,
nylon, fiber-reinforced silicones, structural foams, or the
like.
[0059] While the headset device 110 is shown and described as a
headset such that the headset device 110 is lightweight and
portable, one of ordinary skill in the art recognizes that the
headset device 110 can be implemented with other types of TCD
devices.
[0060] In some arrangements, the waveform visualization system 100
includes an input device 250. The input device 250 includes any
suitable device configured to allow an operator, physician, or care
provider personnel to input information or commands into the
waveform visualization system 100. In some arrangements, the input
device 250 includes but is not limited to, a keyboard, a keypad, a
mouse, a joystick, a touchscreen display, or any other input device
performing a similar function. In some arrangements, the input
device 250 and the output device 140 can be a same input/output
device (e.g., a touchscreen display device).
[0061] In some arrangements, the network interface 260 is
structured for sending and receiving data (e.g., results,
instructions, requests, software or firmware updates, and the like)
over a communication network. Accordingly, the network interface
260 includes any of a cellular transceiver (for cellular
standards), local wireless network transceiver (for 802.11X,
ZigBee, Bluetooth.RTM., Wi-Fi, or the like), wired network
interface, a combination thereof (e.g., both a cellular transceiver
and a Bluetooth transceiver), and/or the like. In some examples,
the network interface 260 includes any method or device configured
to send data from the headset device 110 to the controller 130. In
that regard, the network interface 260 may include Universal Serial
Bus (USB), FireWire, serial communication, and the like.
[0062] In some arrangements, the input device 250, the output
device 140, the network interface 260, and the controller 130 form
a single computing system that resides on a same node on the
network 120, and the headset device 110 is connected to the
computing system via the network 120, the network interface 260 is
configured to communicate data to and from the headset device 110
via the network 120. In such arrangements, the headset device 110
includes a similar network interface (not shown) to communicate
data to and from the computing device via the network 120. In other
arrangements in which the headset device 110, the controller 130,
the output device 140, the input device 250, and the network
interface 260 all reside in a same computing device on a same node
of a network, the network interface 260 is configured to
communicate data with another suitable computing system (e.g.,
cloud data storage, remote server, and the like).
[0063] In some arrangements, the controller 130 is configured for
controlling operations, processing data, executing input commands,
providing results, and the like with respect to the waveform
visualization system 100, and in particular, in relation to the
morphology indicators as described herein. For example, the
controller 130 is configured to receive input data or instructions
from the input device 250 or the network interface 260, to control
the waveform visualization system 100 to execute the commands, to
receive data from the headset device 110, to provide information
(e.g., the CBFV waveforms and the morphology indicators) to the
output device 140 or network interface 260, and so on.
[0064] The controller 130 includes a processing circuit 232 having
a processor 234 and a memory 236. In some arrangements, the
processor 234 can be implemented as a general-purpose processor and
is coupled to the memory 236. The processor 234 includes any
suitable data processing device, such as a microprocessor. In the
alternative, the processor 234 includes any suitable electronic
processor, controller, microcontroller, or state machine. In some
arrangements, the processor 234 is implemented as a combination of
computing devices (e.g., a combination of a Digital Signal
Processor (DSP) and a microprocessor, a plurality of
microprocessors, at least one microprocessor in conjunction with a
DSP core, or any other such configuration). In some arrangements,
the processor 234 is implemented as an Application Specific
Integrated Circuit (ASIC), one or more Field Programmable Gate
Arrays (FPGAs), a Digital Signal Processor (DSP), a group of
processing components, or other suitable electronic processing
components.
[0065] In some arrangements, the memory 236 includes a
non-transitory processor-readable storage medium that stores
processor-executable instructions. In some arrangements, the memory
236 includes any suitable internal or external device for storing
software and data. Examples of the memory 236 include but are not
limited to, Random Access Memory (RAM), Read-Only Memory (ROM),
Non-Volatile RAM (NVRAM), flash memory, floppy disks, hard disks,
dongles or other Recomp Sensor Board (RSB)-connected memory
devices, or the like. The memory 236 can store an Operating System
(OS), user application software, and/or executable instructions.
The memory 236 can also store application data, such as an array
data structure. In some arrangements, the memory 236 stores data
and/or computer code for facilitating the various processes
described herein.
[0066] As used herein, the term "circuit" can include hardware
structured to execute the functions described herein. In some
arrangements, each respective circuit can include machine-readable
media for configuring the hardware to execute the functions
described herein. The circuit can be embodied as one or more
circuitry components including, but not limited to, processing
circuitry, network interfaces, peripheral devices, input devices,
output devices, sensors, etc. In some arrangements, a circuit can
take the form of one or more analog circuits, electronic circuits
(e.g., integrated circuits (IC), discrete circuits, system on a
chip (SOCs) circuits, etc.), telecommunication circuits, hybrid
circuits, and any other suitable type of circuit. In this regard,
the circuit can include any type of component for accomplishing or
facilitating achievement of the operations described herein. For
example, a circuit as described herein can include one or more
transistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR,
etc.), resistors, multiplexers, registers, capacitors, inductors,
diodes, wiring, and so on.
[0067] The circuit can also include one or more processors
communicatively coupled to one or more memory or memory devices. In
this regard, the one or more processors can execute instructions
stored in the memory or can execute instructions otherwise
accessible to the one or more processors. In some arrangements, the
one or more processors can be embodied in various ways. The one or
more processors can be constructed in a manner sufficient to
perform at least the operations described herein. In some
arrangements, the one or more processors can be shared by multiple
circuits (e.g., a first circuit and a second circuit can comprise
or otherwise share the same processor which, in some example
arrangements, can execute instructions stored, or otherwise
accessed, via different areas of memory). Alternatively, or
additionally, the one or more processors can be structured to
perform or otherwise execute certain operations independent of one
or more co-processors. In other example arrangements, two or more
processors can be coupled via a bus to enable independent,
parallel, pipelined, or multi-threaded instruction execution. Each
processor can be implemented as one or more general-purpose
processors, ASICs, FPGAs, DSPs, or other suitable electronic data
processing components structured to execute instructions provided
by memory. The one or more processors can take the form of a single
core processor, multi-core processor (e.g., a dual core processor,
triple core processor, quad core processor, etc.), microprocessor,
etc. In some arrangements, the one or more processors can be
external to the apparatus, for example, the one or more processors
can be a remote processor (e.g., a cloud-based processor).
Alternatively, or additionally, the one or more processors can be
internal and/or local to the apparatus. In this regard, a given
circuit or components thereof can be disposed locally (e.g., as
part of a local server, a local computing system, etc.) or remotely
(e.g., as part of a remote server such as a cloud-based server). To
that end, a circuit, as described herein can include components
that are distributed across one or more locations.
[0068] The circuit can also include electronics for emitting and
receiving acoustic energy such as a power amplifier, a receiver, a
low noise amplifier or other transmitter receiver components. In
some embodiments, the electronics are an ultrasound system. In some
embodiments, the system is comprised of a headset which is used to
adjust the position of a probe such as a TCD ultrasound probe. The
headset can be configured manually or use an automated robotic
system to position the probe over a desired location on the head.
The probe transmits and receives acoustic energy which is
controlled by an electronic circuit. The electronic circuit has an
analog circuit component such as a power amplifier which sends a
signal to the probe. The probe than receives the signal which is
amplified by an analog low noise amplifier either within the probe
or in the analog circuit. Both the transmitted and received signals
may be digitized by the circuit. In some embodiments, the send and
receive chain may be made up of entirely digital components.
[0069] An example system for implementing the overall system or
portions of the arrangements can include a general-purpose
computer, including a processing unit, a system memory, and a
system bus that couples various system components including the
system memory to the processing unit. Each memory device can
include non-transient volatile storage media, non-volatile storage
media, non-transitory storage media (e.g., one or more volatile
and/or non-volatile memories), etc. In some arrangements, the
non-volatile media may take the form of ROM, flash memory (e.g.,
flash memory such as NAND, 3D NAND, NOR, 3D NOR, etc.),
Electrically Erasable Programmable Read-Only Memory (EEPROM),
Magnetoresistive Random Access Memory (MRAM), magnetic storage,
hard discs, optical discs, etc. In other arrangements, the volatile
storage media can take the form of RAM, Thyristor Random Access
Memory (TRAM), Z-Capacitor Random Access Memory (ZRAM), etc.
Combinations of the above are also included within the scope of
machine-readable media. In this regard, machine-executable
instructions comprise, for example, instructions and data which
cause a general-purpose computer, special purpose computer, or
special purpose processing machines to perform a certain function
or group of functions. Each respective memory device can be
operable to maintain or otherwise store information relating to the
operations performed by one or more associated circuits, including
processor instructions and related data (e.g., database components,
object code components, script components, etc.), in accordance
with the example arrangements described herein.
[0070] The controller 130 further includes a signal processing
circuit 238, which can be implemented with the processing circuit
232 or another dedicated processing circuit. The signal processing
circuit 238 receives the ultrasound data from the headset device
110 and generates the CBFV waveforms in the manner described. The
signal processing circuit 238 can further determine the morphology
indicators for the CBFV waveforms or the average thereof. The
signal processing circuit 238 can configure the output device 140
to display the CBFV waveforms, the average thereof, and the
morphology indicators.
[0071] The controller 130 further includes a robotic control
circuit 240, which can be implemented with the processing circuit
232 or another dedicated processing circuit. The robotic control
circuit 240 is configured to control the robotics 214 based on the
morphology of the CBFV waveforms during the operation of the
visualization system 100 in the manner described. In particular,
the robotic control circuit 240 is configured to control the
positioning of the probe 105 using information regarding the
morphology of the waveforms.
[0072] FIG. 3 is a processing flow diagram illustrating a method
300 for facilitating medical diagnosis using the waveform
visualization system 100 (FIG. 1) according to various
arrangements. Referring to FIGS. 1-3, at 310, the robotics 214 can
initially position the probe 105 and/or the headset device 110 in a
setup phase, before signal acquisition is performed. The robotics
214 represent a kinematic mechanism that positions the transducer
of the probe 105 at an acoustic window (e.g., the temporal window
region) adjacent to the head 102. The robotics 214 can
automatically position the probe 105 based on prior knowledge of
the human anatomy and cerebral hemodynamics in some arrangements.
In some arrangements, the robotics 214 can initially position the
probe 105 based on user input received by the input device 250. In
some arrangements, a human operator can physically position the
probe 105 at the acoustic window.
[0073] At 320, the headset device 110 (e.g., the probe) acquires
signals (e.g., ultrasound data) during an operation phase. The
ultrasound data is indicative of CBFV. The signals are streamed,
via the network 120, to the controller 130 for processing.
[0074] At 330, the signal processing circuit 238 is configured to
extract CBFV waveforms based on the signals. The streamed data can
be processed and plotted (e.g., CBFV versus time) to generate a
continuous CBFV output (which can be displayed in the manner
described with respect to a CBFV output diagram 420 of FIG. 4).
Extracting the CBFV waveforms refers to dividing the continuous
CBFV into multiple CBFV waveforms, each of which corresponds to a
pulse or a heartbeat. Using the continuous CBFV output as a
starting point, the signal processing circuit 238 can extract the
CBFV waveforms associated with the continuous CBFV output. In some
examples, extracting the CBFV waveforms can be performed using a
method 800 shown in FIG. 8. In another example, determining the
CBFV waveforms can be performed by performing heartbeat or pulse
segmentation 332 and feature (morphology attribute) identification
334. Examples of the manner in which the signal processing circuit
238 determines the CBFV waveforms, including heartbeat or pulse
segmentation 332 and feature (morphology attribute) identification
334, can be found in non-provisional patent application Ser. No.
15/399,710, titled SYSTEMS AND METHODS FOR DETERMINING CLINICAL
INDICATIONS, and filed on Jan. 5, 2017, which is incorporated
herein by reference in its entirety.
[0075] At 340, the signal processing circuit 238 is configured to
determine a derived (e.g., average) CBFV waveform. The average CBFV
waveform is an average (e.g., mean or median) of the CBFV waveforms
in a predetermined time interval. For example, the average CBFV
waveform may be a moving average or a moving mean of the CBFV
waveforms in a predetermined time interval. The CBFV waveforms are
determined per 330. The CBFV waveforms determined per 330 may be
filtered to remove noise, before the CBFV waveforms are averaged.
One of ordinary skill in the art appreciates that filtering and
averaging described herein are examples of how the derived or
average CBFV can be derived from the CBFV waveforms determined per
330. The predetermined time interval can correspond to the periodic
refresh rate of the CBFV output as presented by the output device
140. The predetermined time interval and/or the refresh rate of the
CBFV output can depend on the heartrate of the patient 101, display
screen size, processing latency/delay, user settings, and the like.
An example of the periodic refresh rate is a periodic refresh rate
of a first window 410 of FIG. 4. The predetermined time interval
can be determined in other suitable manners.
[0076] At 350, the signal processing circuit 238 determines
morphology indicators for the derived (e.g., average) CBFV
waveform. The morphology indicators correspond to morphological
attributes of the average CBFV waveform. Thus, determining the
morphology indicators includes determining the morphological
attributes of the average CBFV waveform. In some arrangements,
given that the average CBFV waveform is an average of the CBFV
waveforms within the predetermined time interval, the morphological
attributes of the average CBFV waveform can be an average of
corresponding morphological attributes of the CBFV waveforms within
the predetermined time interval. For example, a first
characteristic peak of an average CBFV waveform may have an
x-coordinate equal to an average (e.g., mean or median) of time
values indicative of when the first characteristic peaks of the
CBFV waveforms occur, and a y-coordinate equal to an average of
CBFV values (e.g., mean or median) of the first characteristic
peaks of the CBFV waveforms. In other arrangements, the morphology
attributes of the average CBFV waveform is determined in a manner
similar to the manner in which the corresponding morphology
attributes of the CBFV waveforms within the predetermined time
interval are determined. Alternatively, the time interval may be
determined dynamically, for example, based on signal quality. In
particular, the better the signal quality (e.g., high
signal-to-noise ratio), the shorter the time interval needs to
be.
[0077] Examples of the morphological attributes include but are not
limited to, peaks, valleys, width of peaks, slopes, integrals and
the like. The morphology indicators include but are not limited to,
dots, lines, highlights, arrows, boxes, brackets, texts, numbers,
sounds, tactile feedback, and the like.
[0078] At 360, the signal processing circuit 238 configures the
output device 140 to display the derived (e.g., average) CBFV with
the morphology indicators. Accordingly, the derived CBFV displayed
by the output device 140 is analyzed by the controller 130. The
morphology indicators (e.g., a dot) can identify a position of a
morphological attribute (e.g., a peak) on the average CBFV waveform
diagram/graph. The morphology indicators are visual indicators that
define a shape or morphology of the average CBFV waveform, thus
visually enhancing the average CBFV waveform by visually presenting
the extracted physiological data that have been previously ignored
by care providers. In some arrangements, the signal processing
circuit 238 compares the morphology indicators with those of a
healthy individual for reference and diagnostic purposes.
[0079] Given that the morphology of a CBFV waveform can be quite
subtle, and that the morphology can change rapidly within a short
period of time, a physician, clinician, technician, or care
provider may not be able to identify the morphology or may not have
the time to do so. With the morphological indicators, the
physician, clinician, technician, or care provider can immediately
understand the morphology of a CBFV waveform and the medical
considerations associated therewith. Diagnosis of the patient 101
in real-time or semi-real-time can be achieved as the morphology
indicators are displayed. As such, the morphology indicators can
assist in diagnosing and treating the patient 101 by presenting
useful information to the operator or by automatically identifying
issues corresponding to the morphology attributes.
[0080] Beyond displaying of morphology indicators, the signal
processing circuit 238 can automatically detect medical conditions
or can diagnose the patient 101 using the morphology
indicators/attributes. Machine learning can be implemented to use
heuristic data of known medical conditions and associated CBFV
waveforms (or changes thereof over time) as learning examples.
Based on such learning examples, morphology attributes of interest
(e.g., peaks, valleys, width of the peaks, or other defined or
undefined morphology attributes) can be extracted as representative
criteria due to the correlation with a certain medical condition.
Various categories can be created, including but are not limited
to, normal, medical condition type A, medical condition type B, . .
. , and medical condition type N. A database (not shown) stores the
categories and the morphology indicators/attributes associated
therewith. To identify a medical condition that the patient 101 is
experiencing, the signal processing circuit 238 can implement a
classifier to classify the average CBFV waveform, the morphological
attributes, and/or changes thereof over time into one of the
various categories. An example of the classifier is a kernel-based
classifier, such as but not limited to a support vector machine
(SVM) and spectral regression kernel discriminant analysis
(SR-KDA).
[0081] The signal processing circuit 238 can configure the output
device 140 to initiate visual display, audio output, or tactile
feedback to notify the operator of the medical condition
automatically detected based on the morphology
indicators/attributes. The signal processing circuit 238 can
configure the network interface 260 to send an email, a page, an
SMS message, or call the operator to notify the operator of the
detected medical condition. For example, the signal processing
circuit 238 can configure the network interface 260 to notify the
operator at Electronic Health Record (HER) interfaces, patient
monitors, patient alarms, and the like. This is can be extremely
useful in a continuous monitoring scenario in which the patient 101
is continuously monitored for medical conditions (e.g., increased
ICP) and a care provider may not be present all the time. Such
automated diagnosis based on CBFV waveforms were not implemented
conventionally, nor does an operator interpret the waveform in the
manner described in real-time or semi-real time. Therefore, such
arrangements improve the field of medical diagnosis by automating a
process that is not previously automated.
[0082] Moreover, the waveform visualization system 100 can include
or otherwise operatively coupled to other medical devices capable
of actuating medical operations automatically based on the medical
conditions automatically detected based on the morphology
indicators/attributes. For example, responsive to determining that
the patient 101 is experiencing increased ICP, the signal
processing circuit 238 can configure the network interface 260 to
send a command to an intravenous (IV) injection machine or device
to automatically administer a drug (e.g., Mannitol, Acetazolamife,
and the like) of a suitable dosage to treat the increased ICP. In
some examples, the dosage depends on the amount of ICP increased.
The amount of ICP increased and the corresponding dosage can also
be determined based on machine learning.
[0083] In some arrangements, responsive to determining that a point
on the waveform or a difference between two points on the waveform
are below or above a threshold, ultrasound beam emitted from the
probe 105 can be adjusted by the signal processing circuit 238. The
adjustments can include but are not limited to, adjusting
measurement depth, adjusting beam power, adjusting sample size or
volume, and adjusting measurement time. For example, responsive to
determining that one or more of the peaks 510a, 520a, or 530a are
below a first threshold or responsive to determining that a
difference between two or more of the peaks 510a, 520a, or 530a are
below a threshold, the signal processing circuit 238 can perform
one or more of increasing beam power, increasing sample size, and
increasing measurement time. On the other hand, responsive to
determining that one or more of the peaks 510a, 520a, or 530a being
above a second threshold, the signal processing circuit 238 can
perform one or more of decreasing beam power, decreasing sample
size, or decreasing measurement time. The first and second
thresholds can be defined using machine learning. Machine learning
can be implemented to use heuristic data of known ultrasound beam
characteristics (including but not limited to, measurement depth,
beam power, sample size or volume, and measurement time) and
associated CBFV waveforms (or changes thereof over time) as
learning examples. Based on such learning examples, morphology
attributes of interest (e.g., peaks, valleys, width of the peaks,
or other defined or undefined morphology attributes) can be
extracted as the first and second thresholds. A database (not
shown) stores the thresholds and the morphology
indicators/attributes associated therewith.
[0084] In addition, the morphology indicators can assist in
equipment calibration and test setup, including repositioning of
the headset device 110 and/or the probe 105 to improve data
accuracy. By reviewing the morphology indicators, a physician,
clinician, technician, or care provider can determine equipment
misalignment or setup issues/inaccuracies.
[0085] An operator can perform actions such as but not limited to,
adjusting a tilt of tilt table, adjusting the probe 105 on the head
102, and applying more gel on the head 102. In some examples, the
operator can use the input device 250 to define parameters based on
which the robotics 214 can translate the probe 105 along a surface
of the head 102 and to move the probe 105 with respect to (e.g.,
toward and away from) the head 102.
[0086] Furthermore, equipment calibration or test setup can be
performed automatically using the robotic control circuit 240 and
the robotics 214. For instance, at 370, the signal processing
circuit 238 determines whether there is a position issue with
respect to the probe 105 based on the morphology
attributes/indicators. For instance, certain morphology
attributes/indicators or changes to the morphology
attributes/indicators over time correspond to particular
misalignment of the headset device 110 and/or the probe 105 with
the head 102, or a lack of gel to improve transmission. In some
examples, responsive to determining that a point (e.g., the peaks
510a, 520a, or 530a) on the waveform or responsive to determining
that a difference between two points (e.g., two of the peaks 510a,
520a, or 530a) on the waveform are below or above a threshold, a
position issue or a lack of gel is detected.
[0087] Machine learning can be likewise implemented to use
heuristic data of known misalignment types and associated CBFV
waveforms (or changes thereof over time) as learning examples.
Based on such learning examples, morphology attributes of interest
(e.g., peaks, valleys, width of the peaks, and the like) can be
extracted as representative criteria due to the correlation with a
certain type of misalignment. Various categories can be created,
including but are not limited to, no misalignment issue,
misalignment issue type A, misalignment issue type B, . . . , and
misalignment issue type N. The categories can be defined with
respect to physical attributes of the patient 101, which include
parameters or ranges for an age, gender, weight, head size,
preexisting medical conditions, and the like. This provides further
granularity in defining the categories. A database (not shown)
stores the categories, the physical attributes associated
therewith, and the morphology indicators/attributes associated
therewith in the form of templates. An operator can use the input
device 250 to define the physical attributes of the patient 101.
Based on those parameters or ranges, a template associated
therewith can be retrieved and compared with the morphology of the
waveform. To determine whether a misalignment has occurred, the
signal processing circuit 238 can implement a classifier to
classify the average CBFV waveform, the morphological attributes,
and/or changes thereof over time into one of the various categories
associated with the physical attributes of the patient 101.
[0088] Responsive to determining that there are no position issues
(370:NO), the method 300 ends. On the other hand, responsive to
determining that there is a position issue (370:YES), the robotic
control circuit 240 configures the robotics 214 to reposition the
probe 105 based on the morphology indicators/attributes, at
380.
[0089] In some arrangements, either displaying the morphology
indicators (360) or automatically adjusting the probe 105 (370 and
380) is performed. In other arrangements, both displaying the
morphology indicators and automatically adjusting the probe 105 are
performed in any suitable sequence or simultaneously.
[0090] FIG. 4 is a display interface 400 showing a CBFV output
diagram 420 and a CBFV waveform diagram 440 of the patient 101
(FIG. 1) according to one example. Referring to FIGS. 1-4, the
display interface 400 is an example of an interface displayed by
the output device 140 at 360. The output device 140 displays the
CBFV output diagram 420 in a first window 410 of the display
interface 400. The output device 140 displays the CBFV waveform
diagram 440 in a second window 430 of the display interface 400.
The second window 430 can be referred to as a morphology display
window. The vertical axes in the diagrams 420 and 440 correspond to
blood flow velocity (in cm/s or cm/ms), and the horizontal axes in
the diagrams 420 and 440 correspond to time (in s or ms). The CBFV
output diagram 420 can be displayed in real-time or semi-real time
as the signals (e.g., ultrasound data) are collected at 320.
Displaying of the CBFV output diagram 420 may be delayed due to
signal processing. Some methods of heartbeat or pulse segmentation
332 and feature (morphology attribute) identification 334 may not
be used in real-time with streaming data as future knowledge of the
signals are needed for more accurate processing. Accordingly, in
some arrangements, the controller 130 introduces a reporting
latency. The CBFV output diagram 420 is continuously updated or
periodically updated as new signals are collected at 320.
[0091] As shown, the CBFV output diagram 420 visually presents
multiple continuous CBFV waveforms for a given time interval as
determined at 330. The CBFV output is pulsatile, driven by the
cardiac cycle of the patient 101. The CBFV output appears to be
periodic in nature, with each distinct CBFV waveform (each period)
corresponding to a pulse or heartbeat. The CBFV waveforms shown in
the diagram 420 appear to have morphological features such as but
not limited to peaks and valleys. However, given the irregularities
of the CBFV output and that the CBFV output diagram 420 is
constantly updated to account for new data, it is difficult to
diagnose based on the CBFV output diagram 420 without assistance
from visual indicators that visually identify and emphasize the
morphological features to allow an operator to perceive what the
CBFV waveforms mean immediately.
[0092] The CBFV waveform diagram 440 displays the derived (e.g.,
average) CBFV waveform determined at 340. The average CBFV waveform
is the average of the multiple waveforms displayed in the CBFV
output diagram 420. By displaying an average CBFV waveform,
negative effects, such as but not limited to, noise and fluctuation
in the raw signals acquired at 320 can be reduced. The CBFV
waveform diagram 440 can also display an average CBFV waveform that
has been graphically processed (such as but not limited to,
smoothed, enlarged, and scaled) to emphasize certain morphology
features. In other arrangements, the CBFV waveform diagram 440
displays a waveform selected by the signal processing circuit 238
from multiple waveforms captured for the predetermined period of
time.
[0093] In some arrangements, the CBFV waveform diagram 440 displays
the derived (e.g., average) CBFV waveform with at least one
previous average CBFV waveform, all superimposed on each other in a
same diagram or displayed adjacent to each other to illustrate
changes of the average CBFV waveforms over time. In an example in
which the CBFV output diagram 420 is updated periodically such that
an average CBFV waveform is determined for each period, each of the
at least one previous average CBFV waveform corresponds to a
previous period that is no longer displayed.
[0094] In some arrangements, the CBFV waveform diagram 440 displays
the derived (e.g., average) CBFV waveform with at least one of the
CBFV waveforms displayed in the CBFV output diagram 420,
superimposed on each other in a same diagram or displayed adjacent
to each other. In some arrangements, the CBFV waveform diagram 440
displays two or more CBFV waveforms displayed in the CBFV output
diagram 420 (without displaying the derived CBFV), all superimposed
on each other in a same diagram or displayed adjacent to each
other. Aligning any CBFV waveforms can be achieve due to beat
segmentation, which identifies a starting point and an end point of
a particular CBFV waveform.
[0095] In the arrangements in which the CBFV waveform diagram 440
displays multiple CBFV waveforms, the morphology indicators for one
of the CBFV waveforms are displayed to avoid visual crowding and
confusion. In other arrangements, the morphology indicators for two
or more of the CBFV waveforms are displayed.
[0096] FIGS. 5A and 5B show a non-limiting example of a manner in
which morphology or changes in morphology as evidenced by the
morphology indicators can be used to detect medical conditions,
such as increased ICP. FIG. 5A is a CBFV waveform diagram 500a of a
healthy individual according to one example. FIG. 5B is a CBFV
waveform diagram 500b of a patient (of comparable physical
characteristics such as gender, age, and race) suffering from
idiopathic intracranial hypertension (IIH) according to one
example. Referring to FIGS. 1-5B, the vertical axes in the diagrams
500a and 500b correspond to blood flow velocity (in cm/s or cm/ms),
and the horizontal axes in the diagrams 500a and 500b correspond to
time (in s or ms). The diagrams 500a and 500b may or may not be
displayed with an underlying CBFV output diagram (e.g., the CBFV
output diagram 420). The CBFV waveforms shown in the diagrams 500a
and 500b can be derived from (e.g., filtered from, an average (mean
or median) of, and the like) the underlying CBFV output for a
predetermined time interval in the manner described. Alternatively,
the CBFV waveforms shown in the diagrams 500a and 500b can be
selected by the signal processing circuit 238 from multiple
waveforms captured for the predetermined time interval.
[0097] The CBFV waveform diagrams 500a and 500b, including
morphology indicators 510a-530a and 510b-530b, can be displayed by
the output device 140 to assist a physician, clinician, technician,
or care provider with diagnosis, in some arrangement for increased
or high ICP. FIG. 5A and 5B show a case where traditional CBFV
metrics such mean velocity, systolic velocity, and diastolic
velocity with respect to the CBFV waveform diagrams 500a and 500b
are equal. As such, the traditional CBFV metrics do not provide
insight for diagnosis, however, the morphological indicators
might.
[0098] In the CBFV waveform diagram 500a, a second characteristic
peak (visually identified by the morphology indicator 520a) is a
first distance away from a first characteristic peak (visually
identified by the morphology indicator 510a). In the CBFV waveform
diagram 500b, a second characteristic peak (visually identified by
the morphology indicator 520b) is a second distance away from a
first characteristic peak (visually identified by the morphology
indicator 510b). The second distance is considerably shorter than
the first distance. The distance between the first characteristic
peak and the second characteristic peak can be used to determine
increased or high ICP, given that the distance between the first
characteristic peak and the second characteristic peak can
correlate with ICP. Specifically, shorter distance between the
first characteristic peak and the second characteristic peak is
typically associated with higher ICP.
[0099] As such, by displaying the morphology indicators 510a-530a
and 510b-530b, a physician, clinician, technician, or care provider
can immediately perceive the relationships between morphology of
the CBFV waveforms shown in the diagrams 500a and 500b in
real-time, as such, measurements are taking place, to diagnose the
patient and to take actions. In some arrangements, these
measurements are from two different people at two different times.
It may be possible to used stored, normative data of that range and
compare it. It also may be possible to compare waveforms from two
sides. Or, it may be possible to compare to stored waveforms of
that subject. To further notify an operator of the morphology of
the CBFV waveforms shown in the diagrams 500a and 500b, additional
morphology indicators 540a and 540b can be used to visually
emphasis the first distance and the second distance, respectively.
Other forms of visual or audio notifications, warnings, or tactile
feedback can be provided if the distance between the first
characteristic peak and the second characteristic peak falls below
a predetermined threshold. The predetermined threshold can be an
absolute length (e.g., in cm) or a percentage (e.g., a 5%, 10%,
15%, 20%, or the like of the blood flow velocity of the first
characteristic peak or of the second characteristic peak). In other
examples, the predetermined threshold corresponds to the value of
the second characteristic peak exceeding the value of the first
characteristic peak.
[0100] FIGS. 5A and 5B show an exemplary connection between
specific CBFV waveform morphology attributes and ICP. One of
ordinary skill in the art can appreciate that other connections
between CBFV waveform morphology attributes and other medical
conditions exist and can be likewise visually presented (e.g.,
identified or highlighted by morphology indicators) to assist a
physician, clinician, technician, or care provider with diagnosis
of those medical conditions. To name a few, CBFV waveform
morphology attributes are linked to vasodilatation,
vasoconstriction, capillary bed expansion, and the like.
[0101] While FIGS. 4-5B show morphology indicators 450-470,
510a-530a, and 510b-530b that correspond to peaks, one of ordinary
skill in the art can appreciate that morphology indicators
corresponding to other morphological features (e.g., valleys,
slopes at peaks, slopes at valleys, width of a peak, and the like)
of the CBFV waveforms can be likewise displayed. Such morphology
indicators/attributes can be likewise implemented for machine
learning.
[0102] With respect to stroke analysis, a trained operator
typically examines dampened signal, blunted signal, minimal signal,
or absent signal of a CBFV waveform to detect stroke. This relies
on an operator's skill and interpretation, which is subjective. The
dampened signal, blunted signal, minimal signal, and absent signal
also correspond to an overall feel of the CBFV waveform and does
not relate to particular morphological attributes. Arrangements
disclosed herein relate to graphically presenting the morphological
attributes using suitable indicators to assist an operator in
detecting and analyzing stroke. Additional arrangements allow
automated detection of stroke, a CBFV waveform-based process that
had not been previously automated.
[0103] FIG. 5C is a CBFV waveform diagram 500c of a healthy
individual (left) and a CBFV waveform diagram 500d of a patient
suffering from LVO (right) according to one example. FIG. 5C shows
non-limiting examples of a manner in which morphology or changes in
morphology as evidenced by the morphology indicators can be used to
detect medical conditions, such as LVO. Referring to FIGS. 1-5C,
the vertical axes in the diagrams 500c and 500d correspond to blood
flow velocity (in cm/s or cm/ms), and the horizontal axes in the
diagrams 500c and 500d correspond to time (in s or ms). The
diagrams 500c and 500d may be displayed by the output device 140.
The diagrams 500c and 500d may be displayed with an underlying CBFV
output diagram (e.g., the CBFV output diagram 420). The CBFV
waveforms shown in the diagrams 500c and 500d can be derived from
(e.g., filtered from, an average (mean or median) of, and the like)
the underlying CBFV output for a predetermined time interval in the
manner described. Alternatively, the CBFV waveforms shown in the
diagrams 500c and 500d can be selected by the signal processing
circuit 238 from multiple waveforms captured for the predetermined
time interval.
[0104] Curvature of a CBFV waveform can be used to diagnose LVO.
Curvature is a robust metric for assessing the presence of LVO,
conferring various advantages over traditional heuristic
procedures. Traditional heuristic procedures require acquisition of
CBFV waveforms and power m-mode (PMD) waveforms from multiple
vessels in each hemisphere, thus requiring highly trained personnel
with advanced anatomical knowledge for data acquisition and
analysis. On the other hand, arrangements disclosed herein utilize
curvature, which possesses powerful predictive utility even as
measured from a single brief recording of MCA flow. This can be
significantly enhanced by a paired bilateral recording, regardless
of inter-hemispheric depth disparity, and occlusion location. The
arrangements can be performed in real-time. The displaying of the
morphology indicators (e.g., colors, highlights, pointers,
notifications, warnings, and the like) can be easily understood and
communicated in real-time by care providers with minimal
training.
[0105] First, curvature for each waveform can be determined in
suitable manners. In a non-limiting example, for an exemplary
waveform denoted x(t), below, local curvature (k(t)) can be
computed at each time point (t) via the following expression:
k ( t ) = x '' ( t ) ( 1 + x '2 ( t ) ) 3 2 ( 1 ) ##EQU00001##
The signal processing circuit 238 can determine a single curvature
metric for each waveform by summing local curvature (e.g.,
determined using expression (1)) over all time points, including
time points associated with a beat "canopy." The beat canopy is
defined as a set of time points corresponding to velocities that
exceed a given threshold (e.g., 25%) of a total diastolic-systolic
range of the waveform. In other words, the beat canopy refers to
all time points (t) such that:
x ( t ) x ( t d ) + x ( t s ) - x ( t d ) 4 , ##EQU00002##
where t.sub.d and t.sub.s represent time points corresponding to a
diastolic minimum and a systolic maximum, respectively.
[0106] Next, the curvature for each waveform can be graphically
presented via the output device 140 using suitable morphology
indicators to enable real-time observation and decision-making by
care providers. Curvature is a subtle morphology feature often not
distinguishable by an operator, especially when the diagrams 500c
and 500d are presented in real-time and updated frequently. In the
non-limiting example shown in diagrams 500c and 500d, areas of
relatively high curvature are denoted with circles while areas with
relatively low curvatures are denoted with triangles. As shown, the
diagram 500c of a healthy individual shows high curvature, at or
approximately close to peaks. On the other hand, the diagram 500d
of a patient with LVO exhibits low curvature, even at the
peaks.
[0107] Machine learning can be implemented to use heuristic data of
known medical conditions and associated curvature of CBFV waveforms
(or changes of the curvature over time) as learning examples. Based
on such learning examples, curvature and associated locations of
the curvature can be extracted as representative criteria due to
the correlation with a certain medical condition. Various
categories can be created, including but are not limited to,
normal, medical condition type A, medical condition type B, . . . ,
and medical condition type N. A database (not shown) stores the
categories and the curvature information associated therewith. To
identify a medical condition that the patient 101 is experiencing,
the signal processing circuit 238 can implement a classifier to
classify the curvature information of the CBFV waveform and/or
changes thereof over time into one of the various categories.
[0108] FIG. 6A is a display interface 600a showing CBFV waveform
diagrams 610a-660a associated with an LMCA of a patient and CBFV
waveform diagrams 610b-660b associated with an RMCA of a patient
according to one example. Referring to FIGS. 1-6A, the display
interface 600a can be displayed by the output device 140. Each of
the waveform in the diagrams 610a-660a and 610b-660b can be derived
from (e.g., filtered from, an average (mean or median) of, and the
like) multiple waveforms in a continuous CBFV output for a
predetermined period of time. Each row of diagrams correspond to a
particular depth (e.g., 50 mm, 52 mm, . . . , 60 mm) at which the
signals are gathered by the probe 105. Thus, for each depth, a CBFV
waveform diagram associated with LMCA and another CBFV waveform
diagram associated with RMCA are displayed adjacent to one another
to allow juxtaposition of similar diagrams. This allows an operator
to clearly see the differences between LMCA and RMCA at a
particular depth.
[0109] The differences can be used to diagnose stroke. In some
examples, consistent and significant differences in curvature
across the different depths between LMCA and RMCA can be used as an
indication of LVO. Consistency can be evaluated on a threshold
basis. For example, significant differences above a set threshold
number (e.g., 50%, 60%, 75%, and the like) of the depths measured
correlates with actual LVO. In the display interface 600a, the left
likely has LVO given that with respect to all of the depths
measured, the LMCA is associated with a lesser degree of curvature
as compared to that of corresponding points or peaks on the RMCA.
In some examples, progressive differences between waveforms (e.g.,
differences between peak values) can be used to determine a depth
at which LVO occurs. As shown in the display interface 600a, the
difference between corresponding peaks in LMCA and RMCA is most
pronounced at 50 mm. This indicates that the LVO is likely
occurring at 50 mm. The morphologies (e.g., curvature and peak
values) between LMCA and RMCA diverge the greatest at 50 mm as
compared to other depths.
[0110] FIG. 6B is a display interface 600b showing a CBFV waveform
diagram 610c associated with an LMCA of a patient and a CBFV
waveform diagram 610d associated with an RMCA of the patient
superimposed on one another according to one example. Referring to
FIGS. 1-6B, the display interface 600b can be displayed by the
output device 140. Each of the waveforms in the diagrams 610c and
610d can be an average (e.g., mean or median) of multiple waveforms
in a continuous CBFV output for a predetermined period of time.
Instead of displaying the diagrams side-by-side similar to the
display interface 600a, the display interface 600b displays the
diagrams 610c and 610d in a same diagram, being superimposed on one
another. Additional graphical indicators such as colors, arrows,
text, and the like can be implemented to distinguish the two plots.
For example, the diagrams 610c and 610d can be shown in different
colors.
[0111] In some arrangements, morphological indicators such as those
described herein can be added to the diagrams 610a-660a, 610b-660b,
610c, and 610d.
[0112] FIG. 6C is a display interface 600c showing an RMCA velocity
versus LMCA velocity diagram 610e associated with a patient
according to one example. Referring to FIGS. 1-6C, the display
interface 600c can be displayed by the output device 140. The
display interface 600c can be another display interface to organize
the underlying data of the interface 600a. Each dot on the diagram
610e represents the RMCA velocity versus the LMCA velocity for a
particular depth. The depth can be differentiated by different
colors or other visual distinctions such as shapes of the dots. At
least one extrapolation line can be used to show trend.
[0113] While FIGS. 6A-6C are concerned with comparing RMCA and
LMCA, one of ordinary skill in the art can appreciate that the
interfaces 600a-600c can be similarly used to juxtapose any two
comparable CBFV waveforms, such as one from a healthy individual
(control group) with another from a patient with a disease or
suspected to have a disease. The two CBFV waveforms can be
displayed side-by-side based on depths (similar to interface 600a),
superimposed (similar to interface 600b), or have the associated
velocities plotted against each other (similar to interface
600c).
[0114] FIG. 7 is a display interface showing a trending window 700
according to one example. Referring to FIGS. 1-7, the trending
window 700 can be displayed with one or more other interfaces
described herein to provide additional information to assist a
physician, clinician, technician, or care provider with diagnosis
and/or to adjust the positioning of the headset device 110 and the
probe 105. The trending window 700 can be used to trend various
parameters related to the CBFV waveforms including but are not
limited to, curvature, CBFV, transformations of the CBFV (e.g.,
those used to emphasize the upslope of a segmented CBFV waveform),
SSF of the CBFV, and the like. In particular, the trending window
700 trends a parameter 710. The trending window 700 can include
limits 720a and 720b for visual assistance of tracking the
parameter 710.
[0115] FIG. 8 is a processing flow diagram illustrating a method
800 for extracting CBFV waveforms according to one example.
Referring to FIGS. 1-8, the method 800 can be implemented to
extract individual pulses and the CBFV waveforms associated thereof
from the continuous signals (continuous CBFV output) acquired at
320. The extracted waveforms can be used as visual diagnosis aides
to an operator and/or can be used to adjust positions/orientations
of the probe 105 in the manner described. Thus, the CBFV waveform
analysis as described herein depends on reliable pulse onset
detection. A pulse onset defines a beginning of a pulse or a
heartbeat. Accurate CBFV waveform extraction presents a significant
challenge for a number of reasons. For one, TCD measurements are
affected by signal attenuation due to the skull, thus resulting in
a relatively low signal-to-noise ratio. TCD is highly
operator-dependent and relies on the operator's ability to locate
the acoustic window and to insonate the appropriate vessel within a
cerebrovasculature, which varies among individual patients.
Additionally, the CBFV signals are particularly prone to noise
artifacts as a result of motion of the probe 105 and/or the patient
101. Furthermore, a large variety of possible waveform morphologies
can further make CBFV waveform extraction difficult due to lack of
predictability. The method 800 addresses such technical issues. In
some arrangements, in the absence of any TCD devices or in
conjunction with TCD devices, beat start and stop points can be
identified using at least another physiological parameter of the
heart including but not limited to, Electrocardiogram (EKG), pulse
oximetry, heartrate monitors, and the like.
[0116] At 810, the signal processing circuit 238 applies a
band-pass filter to the signals acquired at 320. In some examples,
the band-pass filter is configured to filter out signals outside of
a desired range to filter out noise. Examples of the designed range
include but are not limited to, 0.5-10 Hz.
[0117] At 820, the signal processing circuit 238 enhances at least
one sharp upslope that can define a start of a CBFV waveform. In
one arrangement, enhancement of the sharp upslope can be achieved
by applying a windowed slope sum function (SSF) to the filtered
signals generated as a result of 810. The windowed SSF effectively
measures a net change in the continuous CBFV output shown in graph
900a over a time interval. A non-limiting example of the SSF (Z)
is:
z i = k = i - w i y k - y k - 1 ( 2 ) ##EQU00003##
where w is a length of an analyzing window. In addition, y.sub.k
and y.sub.k-1 are adjacent filtered CBFV output signals. In some
examples, a length of the analyzing window is equal to,
approximately equal to, slightly less than a length of an initial
upslope of a typical pulse. Examples of the length of the analyzing
window include but are not limited to, 100 ms, 110 ms, 120 ms, 125
ms, 130 ms, and 145 ms. In other arrangements, a difference between
a highest point and a lowest point of the CBFV waveform is the net
change.
[0118] FIG. 9 is a CBFV output diagram showing an exemplary CBFV
output 900a and a SSF signals 900b corresponding to the CBFV output
900a according to one example. Referring to FIGS. 1-9, the CBFV
output 900a and the SSF signals 900b are presented in normalized
graphs. The CBFV output 900a and the SSF signals 900b are
time-aligned. The SSF signals 900b shows the SSF signals
corresponding to the signals shown in the CBFV output 900a. The SSF
signals 900b can be determined from the signals of the CBFV output
900a using the expression (2) or another suitable method.
[0119] At 830, the signal processing circuit 238 determines window
locations based on the SSF signals. The window locations define
windows in which a pulse onset is likely to occur. To achieve this,
the signal processing circuit 238 determines thresholds for the SSF
signals. In some examples, the threshold can be established at 60%
of an average (mean or median) of a predetermined number (e.g., 10
or a number of peaks identified if the number is less than the
predetermined number) of preceding peaks in the SSF signals. A peak
is defined as a maximum value of an upslope of a CBFV pulse.
[0120] At an initialization phase in which no preceding peaks can
be used to establish a threshold, all peaks exceeding a peak
threshold are identified by the signal processing circuit 238. An
example of the peak threshold is 3 times the average (mean or
median) of the SSF signals over a first 10 seconds of the data
acquired at 320. The signal processing circuit 238 can set an
initial threshold at 60% of an average (mean or median) value of
the identified peaks. Responsive to the initial threshold being
determined, a threshold line 950 is generated to be horizontally
transverse the SSF signals of the CBFV output diagram 900a.
Threshold crossing points 910b-940b are points on the diagram 900b
that intersect with the threshold line 950. Vertical lines can be
generated at the threshold crossing points 910b-940b to be
vertically transverse to the diagrams 900a and 900b. A search
window is defined as a time interval between a threshold crossing
point (e.g., 920b) and a peak (e.g., 920a) of a last-detected pulse
immediately preceding a new search window. The new search window
can be defined in a manner similar to disclosed with the search
window. For a very first onset, the search window is defined as a
time interval between a very first threshold crossing point and a
beginning of the SSF signals.
[0121] In order to avoid locating multiple threshold crossing
points immediately adjacent to one another, a refractory period is
enforced by the signal processing circuit 238. Within the short
refractory period, the signal processing circuit 238 refrains from
defining new threshold crossing points. Exemplary lengths of the
refractory period include but are not limited to, 150 ms. One of
ordinary skill in the art can appreciate that other suitable
lengths of the refractory period can be likewise implemented, as
long as the refractory period is longer than a pulse upslope time
and significantly shorter than an entire pulse length.
[0122] The peaks 920a, 940a, 960a, and 980a of each beat should
occur close to the threshold crossing points 910b, 920b, 930b and
940b, respectively. In some arrangements, the peaks 920a, 940a,
960a, and 980a are determined by locating a maximum value that
occurs within a predetermined time interval (such as but not
limited to, about 150 ms) of the corresponding threshold crossing
points 910a, 920b, 930b and 940b, respectively. In some
arrangements, peak finding can occur as separately from onset
locating, responsive to all onsets being located.
[0123] At 840, the signal processing circuit 238 performs onset
identification. Responsive to a search window being identified,
valleys (e.g., 910a, 930a, 950a, and 970a) in the original filtered
signals (shown in the diagram 900a) that occur within the search
window are identified. A valley that is both closest to a threshold
crossing point and satisfies a condition such as but not limited
to, CBFV.sub.peak-CBFV.sub.valley.gtoreq.A(SSF.sub.peak) is
designated as a pulse onset. CBFV.sub.peak is a peak value of a
CBFV pulse. CBFV.sub.valley is the value of the candidate valley.
SSF.sub.peak is a peak value of the SSF signals for this search
window. Factor A is included to avoid falling into valleys that
appear in the upslope due to noise artifacts or pathological
morphologies. Examples of factor A include but are not limited to,
about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, and about
0.5-0.9. Examples of the onsets as shown in diagram 900b include
the valleys 910a, 930a, 950a, and 970a. As such, initial estimates
for the waveform onsets are accordingly determined.
[0124] At 850, the signal processing circuit 238 analyzes beat
length to address outliners. After the output 900a has been scanned
in its entirety, and the initial onsets are determined per 840, the
outliners are addressed based on beat length. The initial processes
810-840 may result in two mistakes, "long beats" and "short beats."
Long beats typically occur when a beat is missed, resulting in two
beats detected as a single beat. This result may be due to some
abnormality in the upslope of the beat, either because the upslope
is not sufficiently steep and fails to cross the threshold line
(e.g., 950) or because the upslope contains some noise artifacts
that suppress the SSF signals. Short beats typically occur as noise
causes a sharp upslope based on which a new beat is detected, thus
dividing what should be a single beat into two or more shorter
beats.
[0125] In a non-limiting example, beats are determined to be
outliers using a length-based median absolute deviation (MAD)
method. For each point in the SSF signals 900b, MAD can be computed
using the following expression:
MAD=median(|X.sub.i-median(X)|) (3)
where X is a univariate data set of the SSF signals 900b, having
elements X.sub.i. Mad can be converted into a proxy for standard
deviation by including a scale factor, such as:
{circumflex over (.sigma.)}=B(MAD) (4)
where an example of B is about 1.4826. One of ordinary skill in the
art can appreciate that other suitable examples of the scale factor
B and outliner detection mechanism can be likewise implemented.
[0126] In some arrangements, short beats can be defined as beats
with a length l that satisfies a condition
l<l.sub.median-C({circumflex over (.sigma.)}.sub.length). In
some arrangements, long beats can be defined as beats with a length
l that satisfies a condition l<l.sub.median+C({circumflex over
(.sigma.)}.sub.length). C is a constant such as but not limited to,
about 3.5. C can be any suitable conservative criterion for
classifying outliers.
[0127] In some arrangements, the signal processing circuit 238 can
address the long beats before the short beats. Global beat
detection in the manner described with respect to 830-840 can be
applied on a smaller scale to address the long beats, with
progressively relaxed thresholds. First, a search window is defined
with respect to the CBFV signals from the beginning of a peak of an
identified long beat to the end of the long beat. The SSF is
determined for this segment of CBFV signals. A threshold is set at
60% of the average (mean or median) of all the peaks located in the
original global SSF signals during a first pass (e.g., 810-840).
The original global SSF signals include the SSF corresponding to
the long beat, regular beats, and outer outliner long or short
beats. The window locations and onset locations are determined in a
same manner as disclosed with respect to 830 and 840, for the SSF
signals corresponding to the long beat using such threshold. If new
onsets are located, those onset locations are saved. The method
proceeds to a next long beat, if any. If no new onsets are located,
the threshold (initially at 60%) is incrementally relaxed
(decreased). The onset detection is repeated with each iteration
associated with relaxed threshold until new onsets are located. For
example, for a next iteration, the threshold is set at an increment
(e.g., 5%) less than the previous threshold. If no new onsets are
found after reducing the threshold value to an increment before the
threshold value reaches 0, the long beat is left alone.
[0128] Short beats are dealt with after all the long beats have
been addressed in some arrangements. The short beats are addressed
by viewing each short beat along with its immediate adjacent
neighbors to determine whether the short beat should be combined
with either of its neighboring beats. If a merger of the short beat
with a neighbor beat results in a new beat with a length closer to
the average beat length than the original beats, then the merger is
performed. In an exemplary arrangement, four lengths related to a
short beat are determined: l.sub.before, l.sub.short, l.sub.after,
and l.sub.median. In some examples, l.sub.before defines a length
of a beat adjacent to and before the short beat. l.sub.short
defines a length of the short beat itself. l.sub.after defines a
length of the beat adjacent to and after the short beat.
l.sub.median is an average (mean or median) beat length of all
beats that have been found in the CBFV signals, including beats
other than the short beat and its neighbors. A length of a beat is
defined to be a time interval between consecutive onsets. The
signal processing circuit 238 first checks whether combining
l.sub.short with l.sub.after produces a new beat with a length
closer to l.sub.median than l.sub.after. Responsive to determining
that the length of the new beat is closer to l.sub.median, then the
beats are combined responsive to determining that a correlation
distance between the beats is greater than a threshold, such as but
not limited to about 0.1. This is because merging beats involves
deleting a beat onset, which should be handled very conservatively.
After merging the beats, the method proceeds to a next short beat.
If combining l.sub.short with l.sub.after fails to produce a new
beat with a length closer to l.sub.median than l.sub.after or if
the correlation distance between the beats is not greater than the
threshold, then the signal processing circuit 238 checks whether
combining l.sub.short with l.sub.before produces a new beat with a
length closer to l.sub.median than l.sub.before. Responsive to
determining that the length of the new beat is closer to
l.sub.median, and that a correlation distance between the beats is
greater than the threshold, the beats are combined. This algorithm
can be performed for all short beats until no short beats are
remaining.
[0129] A single pass often may not address all long and short beats
due to the fact that as the beats are added and/or subtracted,
statistics (e.g., average peak, l.sub.median, and the like) may
change. Thus, the beat length analysis at 850 ends responsive to
determining that no new beats are added and/or subtracted during a
single iteration. In some instances, an oscillating solution may be
reached, such that a maximum number of iterations (e.g., 10) should
be enforced to avoid the ping-pong effect of shifting
statistics.
[0130] In some arrangements, actionable information can be
extracted from a distribution of certain attributes of CBFV
waveforms. Examples of such attributes include but are not limited
to, an average velocity, skew, curvature, kurtosis, and the like of
each waveform or of a given peak (e.g., a first peak) of each
waveform. Such information can be determined by the controller 130
and displayed on an interface provided by the output device 140.
FIG. 10 is a display interface 1000 showing a diagram of an
attribute distribution associated with a number of CBFV waveforms
according to various arrangements. As shown, an x-axis of the
diagram corresponds to an attribute (e.g., curvature) of a first
peak of each waveform. A y-axis of the diagram corresponds to a
number of occurrences of a particular attribute value (e.g., 2.5,
5, 7.5, 10, 12.5, and the like) among the number of CBFV waveforms.
For example, 1 CBFV waveform has a curvature of approximately 2.5,
2 CBFV waveforms have a curvature of approximately 5, 4 CBFV
waveforms have a curvature of approximately 7.5, and 3 CBFV
waveforms have a curvature of approximately 10.
[0131] While curvature is used as a non-limiting example, one of
ordinary skill in the art can appreciate other the distribution of
other attributes can be similarly graphed. For instance, the x-axis
of the graph define values of the attribute while the y-axis of the
graph define occurrences of that attribute among the CBFV waveforms
or among peaks (e.g., first peaks) of the CBFV waveforms. In
addition, the output device 140 can similarly display a
distribution of a certain attribute of a given subject being
compared (e.g., overlaid) with the distributions of the same
attribute of other subjects or with an average distribution across
a population (e.g., a general population, a segmented population,
and the like).
[0132] The above used terms, including "held fast," "mount,"
"attached," "coupled," "affixed," "connected," "secured," and the
like are used interchangeably. In addition, while certain
arrangements have been described to include a first element as
being "coupled" (or "attached," "connected," "fastened," etc.) to a
second element, the first element may be directly coupled to the
second element or may be indirectly coupled to the second element
via a third element.
[0133] The previous description is provided to enable any person
skilled in the art to practice the various aspects described
herein. Various modifications to these aspects will be readily
apparent to those skilled in the art, and the generic principles
defined herein may be applied to other aspects. Thus, the claims
are not intended to be limited to the aspects shown herein, but is
to be accorded the full scope consistent with the language claims,
wherein reference to an element in the singular is not intended to
mean "one and only one" unless specifically so stated, but rather
"one or more." Unless specifically stated otherwise, the term
"some" refers to one or more. All structural and functional
equivalents to the elements of the various aspects described
throughout the previous description that are known or later come to
be known to those of ordinary skill in the art are expressly
incorporated herein by reference and are intended to be encompassed
by the claims. Moreover, nothing disclosed herein is intended to be
dedicated to the public regardless of whether such disclosure is
explicitly recited in the claims. No claim element is to be
construed as a means plus function unless the element is expressly
recited using the phrase "means for."
[0134] It is understood that the specific order or hierarchy of
steps in the processes disclosed is an example of illustrative
approaches. Based upon design preferences, it is understood that
the specific order or hierarchy of steps in the processes may be
rearranged while remaining within the scope of the previous
description. The accompanying method claims present elements of the
various steps in a sample order, and are not meant to be limited to
the specific order or hierarchy presented.
[0135] The previous description of the disclosed implementations is
provided to enable any person skilled in the art to make or use the
disclosed subject matter. Various modifications to these
implementations will be readily apparent to those skilled in the
art, and the generic principles defined herein may be applied to
other implementations without departing from the spirit or scope of
the previous description. Thus, the previous description is not
intended to be limited to the implementations shown herein but is
to be accorded the widest scope consistent with the principles and
novel features disclosed herein.
[0136] The various examples illustrated and described are provided
merely as examples to illustrate various features of the claims.
However, features shown and described with respect to any given
example are not necessarily limited to the associated example and
may be used or combined with other examples that are shown and
described. Further, the claims are not intended to be limited by
any one example.
[0137] The foregoing method descriptions and the process flow
diagrams are provided merely as illustrative examples and are not
intended to require or imply that the steps of various examples
must be performed in the order presented. As will be appreciated by
one of skill in the art the order of steps in the foregoing
examples may be performed in any order. Words such as "thereafter,"
"then," "next," etc. are not intended to limit the order of the
steps; these words are simply used to guide the reader through the
description of the methods. Further, any reference to claim
elements in the singular, for example, using the articles "a," "an"
or "the" is not to be construed as limiting the element to the
singular.
[0138] The various illustrative logical blocks, modules, circuits,
and algorithm steps described in connection with the examples
disclosed herein may be implemented as electronic hardware,
computer software, or combinations of both. To clearly illustrate
this interchangeability of hardware and software, various
illustrative components, blocks, modules, circuits, and steps have
been described above generally in terms of their functionality.
Whether such functionality is implemented as hardware or software
depends upon the particular application and design constraints
imposed on the overall system. Skilled artisans may implement the
described functionality in varying ways for each particular
application, but such implementation decisions should not be
interpreted as causing a departure from the scope of the present
disclosure.
[0139] The hardware used to implement the various illustrative
logics, logical blocks, modules, and circuits described in
connection with the examples disclosed herein may be implemented or
performed with a general purpose processor, a DSP, an ASIC, an FPGA
or other programmable logic device, discrete gate or transistor
logic, discrete hardware components, or any combination thereof
designed to perform the functions described herein. A
general-purpose processor may be a microprocessor, but, in the
alternative, the processor may be any conventional processor,
controller, microcontroller, or state machine. A processor may also
be implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration. Alternatively, some
steps or methods may be performed by circuitry that is specific to
a given function.
[0140] In some exemplary examples, the functions described may be
implemented in hardware, software, firmware, or any combination
thereof. If implemented in software, the functions may be stored as
one or more instructions or code on a non-transitory
computer-readable storage medium or non-transitory
processor-readable storage medium. The steps of a method or
algorithm disclosed herein may be embodied in a
processor-executable software module which may reside on a
non-transitory computer-readable or processor-readable storage
medium. Non-transitory computer-readable or processor-readable
storage media may be any storage media that may be accessed by a
computer or a processor. By way of example but not limitation, such
non-transitory computer-readable or processor-readable storage
media may include RAM, ROM, EEPROM, FLASH memory, CD-ROM or other
optical disk storage, magnetic disk storage or other magnetic
storage devices, or any other medium that may be used to store
desired program code in the form of instructions or data structures
and that may be accessed by a computer. Disk and disc, as used
herein, includes compact disc (CD), laser disc, optical disc,
digital versatile disc (DVD), floppy disk, and blu-ray disc where
disks usually reproduce data magnetically, while discs reproduce
data optically with lasers. Combinations of the above are also
included within the scope of non-transitory computer-readable and
processor-readable media. Additionally, the operations of a method
or algorithm may reside as one or any combination or set of codes
and/or instructions on a non-transitory processor-readable storage
medium and/or computer-readable storage medium, which may be
incorporated into a computer program product.
[0141] The preceding description of the disclosed examples is
provided to enable any person skilled in the art to make or use the
present disclosure. Various modifications to these examples will be
readily apparent to those skilled in the art, and the generic
principles defined herein may be applied to some examples without
departing from the spirit or scope of the disclosure. Thus, the
present disclosure is not intended to be limited to the examples
shown herein but is to be accorded the widest scope consistent with
the following claims and the principles and novel features
disclosed herein.
* * * * *