U.S. patent application number 16/124689 was filed with the patent office on 2019-03-14 for systems and methods for tremor detection and quantification.
The applicant listed for this patent is Randall Davis, Catherine Medlock, Bruce Musicus, Alan V. Oppenheim, Dana L. Penney, William A. Souillard-Mandar. Invention is credited to Randall Davis, Catherine Medlock, Bruce Musicus, Alan V. Oppenheim, Dana L. Penney, William A. Souillard-Mandar.
Application Number | 20190076078 16/124689 |
Document ID | / |
Family ID | 63794606 |
Filed Date | 2019-03-14 |
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United States Patent
Application |
20190076078 |
Kind Code |
A1 |
Davis; Randall ; et
al. |
March 14, 2019 |
SYSTEMS AND METHODS FOR TREMOR DETECTION AND QUANTIFICATION
Abstract
Systems and methods are disclosed for detecting tremors in a
subject. One method comprises receiving data from a digital device,
the data comprising a plurality of digital device positions and a
plurality of timestamps, each timestamp in the plurality of
timestamps being associated with a digital device position in the
plurality of digital device positions. The method further comprises
determining a plurality of frequencies of hand movements of the
subject based on the plurality of digital device positions and
plurality of timestamps. The method further comprises determining a
subportion of the data corresponding to frequencies of hand
movements above a low tremor threshold, and determining a magnitude
of tremors of the subject's hand based, at least in part, on the
subportion of the data.
Inventors: |
Davis; Randall; (Weston,
MA) ; Medlock; Catherine; (Lexington, MA) ;
Musicus; Bruce; (Lexington, MA) ; Oppenheim; Alan
V.; (Cambridge, MA) ; Penney; Dana L.;
(Weston, MA) ; Souillard-Mandar; William A.;
(Cambridge, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Davis; Randall
Medlock; Catherine
Musicus; Bruce
Oppenheim; Alan V.
Penney; Dana L.
Souillard-Mandar; William A. |
Weston
Lexington
Lexington
Cambridge
Weston
Cambridge |
MA
MA
MA
MA
MA
MA |
US
US
US
US
US
US |
|
|
Family ID: |
63794606 |
Appl. No.: |
16/124689 |
Filed: |
September 7, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62555940 |
Sep 8, 2017 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/04883 20130101;
H04L 67/06 20130101; A61B 5/725 20130101; G06F 3/03545 20130101;
G16H 50/20 20180101; A61B 5/1101 20130101; H04L 67/02 20130101;
A61B 5/1124 20130101; A61B 5/4088 20130101; A61B 2562/0219
20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; G06F 3/0488 20060101 G06F003/0488; G06F 3/0354 20060101
G06F003/0354; A61B 5/11 20060101 A61B005/11; G16H 50/20 20060101
G16H050/20 |
Claims
1. A computer-implemented method for detecting tremors in a
subject, comprising: receiving data from a digital device, the data
comprising a plurality of digital device positions and a plurality
of timestamps, each timestamp in the plurality of timestamps being
associated with a digital device position in the plurality of
digital device positions; determining a plurality of frequencies of
hand movements of the subject based on the plurality of digital
device positions and plurality of timestamps; determining a
subportion of the data corresponding to frequencies of hand
movements above a low tremor threshold; and determining a magnitude
of tremors of the hand of the subject based, at least in part, on
the subportion of the data.
2. The computer-implemented method of claim 1, further comprising:
determining sections of data received from the digital device with
missing samples or a non-uniform sampling rate; and resampling the
data to a uniform sampling rate by interpolating the missing
position information based on the plurality of digital device
positions in the data.
3. The computer-implemented method of claim 1, further comprising:
determining low-pass data by passing the data through a low-pass
filter; and determining an estimate of a direction and a speed of
the digital device based, at least in part, on the low-pass
data.
4. The computer-implemented method of claim 1, wherein determining
a magnitude of tremors of the hand of the subject based, at least
in part, on the subportion of the data further comprises:
determining a bandpass or high-pass filtered velocity vector signal
by passing the subportion of the data through a first derivative
filter; and determining the magnitude of tremors of the hand of the
subject based, at least in part, on analysis of the bandpass or
high-pass filtered velocity vector signal.
5. The computer-implemented method of claim 4, wherein determining
a magnitude of tremors of the hand of the subject based, at least
in part, on the subportion of the data further comprises:
determining a bandpass or high-pass filtered acceleration signal by
passing the bandpass or high-pass filtered velocity vector signal
through a second derivative filter; and determining the magnitude
of tremors of the hand of the subject based, at least in part, on
analysis of the bandpass or high-pass filtered velocity vector
signal and the bandpass or high-pass filtered acceleration
signal.
6. The computer-implemented method of claim 4, wherein determining
a magnitude of tremors of the hand of the subject based, at least
in part, on the subportion of the data further comprises:
determining low-pass data by passing the data through a low-pass
filter; and determining the magnitude of tremors based, at least in
part, on the portion of the bandpass or high-pass filtered velocity
vector signal that is at a predetermined angle relative to the
low-pass data.
7. The computer-implemented method of claim 1, wherein the data
corresponds to the subject drawing a clockface.
8. The computer-implemented method of claim 7, further comprising:
determining one or more perimeter lines in the data corresponding
to a perimeter of the clockface; and determining the magnitude of
tremors based, at least in part, on the one or more perimeter lines
in the data.
9. A system for detecting tremors in a subject, the system
comprising: a data storage device storing instructions for
detecting tremors in a subject; and a processor configured to
execute the instructions to perform a method including: receiving
data from a digital device, the data comprising a plurality of
digital device positions and a plurality of timestamps, each
timestamp in the plurality of timestamps being associated with a
digital device position in the plurality of digital device
positions; determining a plurality of frequencies of hand movements
of the subject based on the plurality of digital device positions
and plurality of timestamps; determining a subportion of the data
corresponding to frequencies of hand movements above a low tremor
threshold; and determining a magnitude of tremors of the hand of
the subject based, at least in part, on the subportion of the
data.
10. The system of claim 9, the method further comprising:
determining sections of data received from the digital device with
missing samples or a non-uniform sampling rate; and resampling the
data to a uniform sampling rate by interpolating the missing
position information based on the plurality of digital device
positions in the data.
11. The system of claim 9, the method further comprising:
determining low-pass data by passing the data through a low-pass
filter; and determining an estimate of a direction and a speed of
the digital device based, at least in part, on the low-pass
data.
12. The system of claim 9, wherein determining a magnitude of
tremors of the hand of the subject based, at least in part, on the
subportion of the data further comprises: determining a bandpass or
high-pass filtered velocity vector signal by passing the subportion
of the data through a first derivative filter; and determining the
magnitude of tremors of the hand of the subject based, at least in
part, on analysis of the bandpass or high-pass filtered velocity
vector signal.
13. The system of claim 12, wherein determining a magnitude of
tremors of the hand of the subject based, at least in part, on the
subportion of the data further comprises: determining a bandpass or
high-pass filtered acceleration signal by passing the bandpass or
high-pass filtered velocity vector signal through a second
derivative filter; and determining the magnitude of tremors of the
hand of the subject based, at least in part, on analysis of the
bandpass or high-pass filtered velocity vector signal and the
bandpass or high-pass filtered acceleration signal.
14. The system of claim 12, wherein determining a magnitude of
tremors of the hand of the subject based, at least in part, on the
subportion of the data further comprises: determining low-pass data
by passing the data through a low-pass filter; and determining the
magnitude of tremors based, at least in part, on the portion of the
bandpass or high-pass filtered velocity vector signal that is at a
predetermined angle relative to the low-pass data.
15. The system of claim 9, wherein the data corresponds to the
subject drawing a clockface.
16. The system of claim 15, the method further comprising:
determining one or more perimeter lines in the data corresponding
to a perimeter of the clockface; and determining the magnitude of
tremors based, at least in part, on the one or more perimeter lines
in the data.
17. A non-transitory computer readable medium for use on a computer
system containing computer-executable programming instructions for
performing a method for detecting tremors in a subject, the method
comprising: receiving data from a digital device, the data
comprising a plurality of digital device positions and a plurality
of timestamps, each timestamp in the plurality of timestamps being
associated with a digital device position in the plurality of
digital device positions; determining a plurality of frequencies of
hand movements of the subject based on the plurality of digital
device positions and plurality of timestamps; determining a
subportion of the data corresponding to frequencies of hand
movements above a low tremor threshold; and determining a magnitude
of tremors of the hand of the subject based, at least in part, on
the subportion of the data.
18. The computer readable medium of claim 17, the method further
comprising: determining sections of data received from the digital
device with missing samples or a non-uniform sampling rate; and
resampling the data to a uniform sampling rate by interpolating the
missing position information based on the plurality of digital
device positions in the data.
19. The computer readable medium of claim 17, the method further
comprising: determining low-pass data by passing the data through a
low-pass filter; and determining an estimate of a direction and a
speed of the digital device based, at least in part, on the
low-pass data.
20. The computer readable medium of claim 17, wherein determining a
magnitude of tremors of the hand of the subject based, at least in
part, on the subportion of the data further comprises: determining
a bandpass or high-pass filtered velocity vector signal by passing
the subportion of the data through a first derivative filter; and
determining the magnitude of tremors of the hand of the subject
based, at least in part, on analysis of the bandpass or high-pass
filtered velocity vector signal.
21. A computer-implemented method for detecting tremors in a
subject, comprising: receiving data from a digital device, the data
comprising a plurality of digital device positions and a plurality
of timestamps, each timestamp in the plurality of timestamps being
associated with a digital device position in the plurality of
digital device positions; determining a magnitude of tremors of the
hand of the subject based, at least in part, on the data comprising
the plurality of digital device positions and the plurality of
timestamps; and determining a degree of cognitive impairment of the
subject based, at least in part, on the data comprising the
plurality of digital device positions and the plurality of
timestamps.
22. The computer implemented method of claim 21, the method further
comprising: determining a plurality of frequencies of hand
movements of the subject based on the plurality of digital device
positions and the plurality of timestamps; determining a subportion
of the data corresponding to frequencies of hand movements of the
subject above a lower tremor threshold; and determining the
magnitude of tremors of the hand of the subject based in part on
the subportion of data.
23. The computer implemented method of claim 22, the method further
comprising: determining sections of data received from the digital
device with missing samples or a non-uniform sampling rate; and
resampling the data to a uniform sampling rate by interpolating the
missing position information based on the plurality of digital
device positions in the data.
24. The computer-implemented method of claim 22, further
comprising: determining low-pass data by passing the data through a
low-pass filter; and determining an estimate of a direction and a
speed of the digital device based, at least in part, on the
low-pass data.
25. The computer-implemented method of claim 22, wherein
determining a magnitude of tremors of the hand of the subject based
in part on the subportion of the data further comprises:
determining a bandpass or high-pass filtered velocity vector signal
by passing the subportion of the data through a first derivative
filter; and determining the magnitude of tremors of the subject's
hand based, at least in part, on analysis of the bandpass or
high-pass filtered velocity vector signal.
26. The computer-implemented method of claim 25, wherein
determining a magnitude of tremors of the hand of the subject based
in part on the subportion of the data further comprises:
determining a bandpass or high-pass filtered acceleration signal by
passing the bandpass or high-pass filtered velocity vector signal
through a second derivative filter; and determining the magnitude
of tremors of the subject's hand based, at least in part, on
analysis of the bandpass or high-pass filtered velocity vector
signal and the bandpass or high-pass filtered acceleration
signal.
27. The computer-implemented method of claim 25, wherein
determining a magnitude of tremors of the hand of the subject based
in part on the subportion of the data further comprises:
determining low-pass data by passing the data through a low-pass
filter; and determining the magnitude of tremors based, at least in
part, on the portion of the bandpass or high-pass filtered velocity
vector signal that is at a predetermined angle relative to the
low-pass data.
28. The computer-implemented method of claim 21, wherein the data
corresponds to the subject drawing a clockface.
29. The computer-implemented method of claim 28, further
comprising: determining one or more perimeter lines in the data
corresponding to a perimeter of the clockface; and determining the
magnitude of tremors based, at least in part, on the one or more
perimeter lines in the data.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of priority from U.S.
Provisional Patent Application No. 62/555,940, filed Sep. 8, 2017,
which is incorporated herein by reference in its entirety.
FIELD OF THE DISCLOSURE
[0002] Various embodiments of the present disclosure relate
generally to cognitive assessment and tremor measurement. More
specifically, particular embodiments of the present disclosure
relate to systems and methods for detecting tremors in a patient
using the same data that is used for cognitive assessment. Other
embodiments may detect only tremor.
BACKGROUND
[0003] The Clock Drawing Test (CDT) is used as a way of determining
an individual's cognitive status, such as healthy or with cognitive
impairments. Impairments may include memory impairment disorders,
vascular cognitive disorders, and Parkinson's disease, for example.
The CDT may be given as follows: the individual is asked to draw,
on a piece of paper, a clock containing a clockface and all the
digits, showing a time such as ten past eleven (the Command clock).
The individual is then asked to copy a printed clock showing the
same time (the Copy clock).
[0004] Traditional tremor measuring drawing tests are hand-scored
by clinicians. As a result, existing widely accepted tremor
measurement scales are based only on quantities that can be
measured with the naked eye. Given the coarseness of subjective
visual judgments, a one-point increase in the tremor score assigned
using the Fahn-Tolosa-Marin scale, for example, may correspond to
an increase in tremor amplitude--as measured by an
accelerometer--by as much as a factor of two.
[0005] The foregoing general description and the following detailed
description are exemplary and explanatory only and are not
restrictive of the disclosure.
SUMMARY
[0006] According to certain aspects of the present disclosure,
systems and methods are disclosed for tremor detection and
quantification, which may be used in combination to gauge cognitive
impairment. As will be discussed herein, in at least certain
embodiments of the present disclosure, quantitative measurement of
tremor utilizing sophisticated signal processing and/or machine
learning technology may provide more precise identification of
tremor than subjective human scoring.
[0007] One method comprises receiving data from a digital device,
the data comprising a plurality of digital device positions and a
plurality of timestamps, each timestamp in the plurality of
timestamps being associated with a digital device position in the
plurality of digital device positions. The method further comprises
determining a plurality of frequencies of hand movements of the
subject based on the plurality of digital device positions and
plurality of timestamps. The method further comprises determining a
subportion of the data corresponding to frequencies of hand
movements above a low tremor threshold, and determining a magnitude
of tremors of the subject's hand based, at least in part, on the
subportion of the data.
[0008] In accordance with another embodiment, a system for
detecting tremors in a subject comprises: a data storage device
storing instructions for detecting tremors in a subject; and a
processor configured to execute a method comprising: receiving data
from a digital device, the data comprising a plurality of digital
device positions and a plurality of timestamps, each timestamp in
the plurality of timestamps being associated with a digital device
position in the plurality of digital device positions. The method
further comprises determining a plurality of frequencies of hand
movements of the subject based on the plurality of digital device
positions and plurality of timestamps. The method further comprises
determining a subportion of the data corresponding to frequencies
of hand movements above a low tremor threshold, and determining a
magnitude of tremors of the subject's hand based, at least in part,
on the subportion of the data.
[0009] In accordance with another embodiment, a non-transitory
computer readable medium for use on a computer system containing
computer-executable programming instructions for performing a
method of detecting tremors in a subject, the method comprising:
receiving data from a digital device, the data comprising a
plurality of digital device positions and a plurality of
timestamps, each timestamp in the plurality of timestamps being
associated with a digital device position in the plurality of
digital device positions. The method further comprises determining
a plurality of frequencies of hand movements of the subject based
on the plurality of digital device positions and plurality of
timestamps. The method further comprises determining a subportion
of the data corresponding to frequencies of hand movements above a
low tremor threshold, and determining a magnitude of tremors of the
subject's hand based, at least in part, on the subportion of the
data.
[0010] Additional objects and advantages of the disclosed
embodiments will be set forth in part in the description that
follows, and in part will be apparent from the description, or may
be learned by practice of the disclosed embodiments. The objects
and advantages of the disclosed embodiments will be realized and
attained by means of the elements and combinations particularly
pointed out in the appended claims.
[0011] It is to be understood that both the foregoing general
description and the following detailed description are exemplary
and explanatory only and are not restrictive of the disclosed
embodiments, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate various
exemplary embodiments, and together with the description, serve to
explain the principles of the disclosed embodiments.
[0013] FIG. 1 is a high-level block diagram illustrating an example
system for detecting tremor in patients in accordance with
techniques presented herein.
[0014] FIG. 2 is an example drawing of a clockface drawn by a
healthy patient.
[0015] FIGS. 3A-3D depict example metrics corresponding to the
drawing of a clockface.
[0016] FIG. 4 depicts an example clockface composed by a patient
exhibiting Essential Tremor.
[0017] FIGS. 5A-5D depict example metrics corresponding to the
drawing of a clockface by a patient exhibiting Essential
Tremor.
[0018] FIG. 6 depicts an example graph of a Fourier transform
magnitude squared of the X and Y coordinates of a clockface drawn
by a healthy patient.
[0019] FIG. 7 depicts an example graph of a Fourier Transform
magnitude squared of the X and Y coordinates of a clockface drawn
by a patient exhibiting Essential Tremor.
[0020] FIG. 8 depicts an example block diagram of signal processing
in accordance with techniques presented herein.
[0021] FIG. 9 depicts an example low-pass filter design in
accordance with techniques presented herein.
[0022] FIG. 10 depicts an example differentiator filter design in
accordance with techniques presented herein.
[0023] FIG. 11 depicts an example bandpass filter design in
accordance with techniques presented herein.
[0024] FIGS. 12A-12D depict graphs of a bandpass filtered position,
velocity, acceleration, and low-pass filtered velocity of the
clockface pen stroke drawn by a healthy individual, in accordance
with techniques presented herein.
[0025] FIGS. 13A-13D depict graphs of bandpass filtered position,
velocity, acceleration, and low-pass filtered velocity of the
clockface pen stroke drawn by a patient with Essential Tremor, in
accordance with techniques presented herein.
[0026] FIGS. 14A-14C depict low-pass filtered velocity, bandpass
filtered velocity, and bandpass filtered acceleration,
respectively, in accordance with techniques presented herein.
[0027] FIGS. 15A-15C depict low-pass filtered velocity, bandpass
filtered velocity, and bandpass filtered acceleration associated
with a patient exhibiting tremor, in accordance with techniques
presented herein.
[0028] FIGS. 16A-16C depict histograms of patients with Essential
Tremor and healthy population, in accordance with techniques
presented herein.
[0029] FIG. 17 depicts an example graph of receiver operating
characteristic (ROC) curves for three tremor metrics in healthy vs.
Essential Tremor patients.
[0030] FIG. 18 depicts an example graph of PnSpdPerpLogEnergy vs.
PnCurvLogEnergy in healthy and tremor-exhibiting patients, in
accordance with techniques presented herein.
[0031] FIG. 19 depicts an example clock drawing made by a patient
with Mild Tremor.
[0032] FIGS. 20A-20D depict example metrics associated with the
clock drawing made by a patient with Mild Tremor, in accordance
with techniques presented herein.
[0033] FIG. 21 depicts an example graph of tremor signals in a
patient with Mild Tremor, in accordance with techniques presented
herein.
[0034] FIG. 22 is a block diagram of an exemplary method of
measuring and quantifying tremor, according to an exemplary
embodiment of the present disclosure.
DESCRIPTION OF THE EMBODIMENTS
[0035] Reference will now be made in detail to the exemplary
embodiments of the disclosure, examples of which are illustrated in
the accompanying drawings. Wherever possible, the same reference
numbers will be used throughout the drawings to refer to the same
or like parts.
[0036] This application discloses systems and techniques for
detection and/or quantification of patient tremor, possibly in
conjunction with screening or diagnosing cognitive impairment. One
embodiment may require a subject to draw, for example, a clock
using a digitizing pen, although other drawing, writing, and pen
stroke capture techniques may be utilized. Some tests may ask the
subject to follow a template spiral or other assigned pattern while
holding his/her arm off the table, while other tests disclosed
herein, such as the digital Clock Drawing Test, may allow the
subject to hold the pen comfortably and write freehand on a sheet
of paper.
[0037] Features derived from the recorded data may be processed by
algorithms into scores that correlate with different types of
cognitive impairment. Cognitive impairment can be inferred by
incorrect positioning or omission of clock digits, incorrect
positioning or omission of the clock hands, and so forth.
Techniques may further include using a digitizing pen that records
its position on the page as a function of time. Many more features
can be accurately measured in this manner, such as pauses between
drawing different portions of the clockface, timing of strokes,
precise positioning of features relative to the nominal center of
the drawing, and so forth. Using the digital clock test data,
classifiers may be created that function either as screening or
diagnostic tests for multiple types of cognitive impairment.
[0038] Another neurological disorder that may be monitored is
tremor, especially as this is often associated with serious medical
conditions such as Parkinson's disease. Some methods of measuring
tremor may require subjects to draw spirals, loops, or cursive
letters, or use a biomedical sensor such as an accelerometer.
[0039] Traditional tremor measuring drawing tests are hand-scored
by clinicians. As a result, existing widely accepted tremor
measurement scales are based only on quantities that can be
measured with the naked eye. Given the coarseness of subjective
visual judgments, a one-point increase in the tremor score assigned
using the Fahn-Tolosa-Marin scale, for example, may correspond to
an increase in tremor amplitude--as measured by an
accelerometer--by as much as a factor of two. Techniques presented
herein that instrument a tremor measurement drawing task with a
digital pen can improve on the precision of the measurement of mild
as well as large tremors.
[0040] Thus, in at least certain embodiments of this disclosure,
novel systems and methods instrument a digital drawing test
intended for screening or diagnosing neurological impairment, such
as a Digital Clock Drawing Test, and re-use all or a portion of the
recorded data to measure tremor as well. This avoids having to
present the patient with two separate tests, which speeds up the
testing procedure, while delivering useful scores for both
cognitive impairment as well as tremor severity. The tremor test
can also be administered by itself, as techniques presented herein
may require relatively little patient time or effort, and yet
produces tremor ratings that correlate well with ratings by medical
professionals.
[0041] In addition, the present analysis, according to exemplary
embodiments, may derive low-pass, bandpass velocity, and bandpass
acceleration measures and components of those entities in specific
directions, possibly related to the direction of pen motion, any of
which may be derived from one or more pen strokes within the
drawing (for example, the "clockface" circle). These measures may
be used to compute several robust tremor quantification features
that correlate strongly with tremor. Although the Digital Clock
Test is described in connection with many of the embodiments, other
types of tests and drawings may be used and fall within the scope
of the inventions.
[0042] On a particular subset of labeled data from subjects
diagnosed as either "Healthy" or "Essential Tremor," receiver
operating characteristic (ROC) curves were produced with area under
the curve (AUC) values above 0.97. (AUC is a quality measure with a
range from 0.5 (poor) to 1.0 (perfect)). Feature scores agree well
with medical neurologist movement disorder specialists who examined
the same selected set of pen strokes, and agreement between
specialists and features scores remains even when the raters are
allowed to examine the entire clock drawing, not just the enclosing
clockface circle. These algorithms and metrics produce results that
are highly correlated with, or superior to, clinician judgment, and
allow for the detection and differentiation of various conditions,
such as distinguishing Healthy from Essential Tremor, a disorder
that causes involuntary and rhythmic shaking.
[0043] Referring now to the figures, FIG. 1 is a high-level block
diagram of a computing environment 100 for detecting tremors in
patients according to one embodiment. The computing environment 100
may include one or more servers 110, and any number of client
devices 120. The servers 110 and/or client devices 120 may be
communicatively coupled by an electronic network 130, such as the
Internet. In one embodiment, the one or more servers 110 may be web
servers, and an application 140 may execute one or more techniques
presented herein via a web browser. In another embodiment, the
servers 110 may be application servers that provide an instance of
one or more applications 140 to the client device 120. In another
embodiment, the servers 110 may provide data, for example from a
data store 160, to support the execution of the one or more
applications 140 on the client device 120. In yet another
embodiment, application 140 may be installed on client device 120,
and may operate without communication with the one or more servers
110. In embodiments discussed herein, a tremor feature may be
calculated using a server 110 in the cloud 130. This tremor feature
may be calculated in a computer 140 attached with a wire or
wirelessly to the digital device 155. This tremor feature may also
be calculated on the digital device 155 itself, in accordance with
techniques presented herein.
[0044] In some embodiments, patient and other data may be utilized
from data store 160, which may be connected to server 110 and/or
client device 120. The client device 120 is a computer or other
electronic device which may be used by one or more users 150 to
perform activities which may include browsing web pages on the
network 130, or using the one or more applications 140. The client
device 120, for example, may be a personal computer, personal
digital assistant (PDA), a mobile telephone, tablet, or another
type of electronic device. Only one server 110, and one client
device 120 will typically be discussed herein to simplify the
description. However, portions of techniques discussed herein may
be executed on different servers 110 and client devices 120.
[0045] The network 130 represents the communication pathways
between (e.g., communicative coupling of) the server 110 and client
device 120. In one embodiment, the network 130 is the Internet. The
network 130 may also include dedicated or private communications
links that are not necessarily a part of the Internet. In one
embodiment, the network 130 uses various communications
technologies and/or protocols. Thus, the network 130 may include
links using technologies such as Ethernet, 802.11, integrated
services digital network (ISDN), digital subscriber line (DSL),
asynchronous transfer mode (ATM), application programming interface
(API), etc. It may also include wireless links such as WiFi or
Bluetooth, or be a wired connection. Similarly, the networking
protocols used on the network 130 may include the transmission
control protocol/Internet protocol (TCP/IP), the hypertext
transport protocol (HTTP), the simple mail transfer protocol
(SMTP), the file transfer protocol (FTP), etc. The data exchanged
over the network 130 can be represented using technologies and/or
formats including the hypertext markup language (HTML), the
extensible markup language (XML), etc. In addition, all or some of
links may be encrypted using encryption technologies such as the
secure sockets layer (SSL), transport layer security (TLS), secure
HTTP (HTTPS), and/or virtual private networks (VPNs). In another
embodiment, the entities may use custom and/or dedicated data
communications technologies instead of, or in addition to, the ones
described above.
[0046] As shown in FIG. 1, client device 120 may execute an
application 140, such as a desktop application, web application, or
browser application, that allows a user to retrieve and view
content stored on other computers or servers on the network 130.
The application 140 may also allow the user to submit information
to other computers on the network 130, such as through user
interfaces, web pages, application program interfaces (APIs),
and/or other data portals. In one embodiment, the application 140
is a web browser, such as MICROSOFT INTERNET EXPLORER or MOZILLA
FIREFOX. The application 140 may support technologies including
JavaScript, ActionScript, and other scripting languages that allow
the client device 120 to perform actions in response to scripts and
other data sent to the application via the network 130. In some
embodiments, functions ascribed herein to the application 140 are
implemented via plug-ins such as ADOBE FLASH.
[0047] The server 110 may deliver data associated with a user
interface, such as a web page, to the application 140 over the
network 130. The application 140 may then load the user interface
and present it to the user.
[0048] Techniques presented herein may provide a tool for detecting
tremor in patients. These techniques may be presented on one or
more user interfaces. Certain techniques discussed herein may be
presented to patients 150, others to clinicians such as physicians,
etc. These features may have associated security requirements
before they may be used. For example, different
subjects/patients/clinicians/users 150 of the application 140 may
have different levels of privileges, allowing them to access
differing features of the application. In addition, many steps of
techniques discussed herein are disclosed in a particular order. In
general, steps discussed may be performed in any order, unless
expressly stated otherwise.
[0049] Techniques described herein may use a database 160 of many,
for example thousands, of subjects who have taken the digital clock
test, and who may have been diagnosed with one or more of a variety
of neurological problems. Some subjects in the database 160 may be
used as healthy controls. In each digital clock or other drawing
test, the subject 160 may draw the Command and Copy clocks using a
digital device 155 (may be referred to herein as a digital pen, but
may also comprise a stylus, device tracking finger movements on a
touchpad, computer mouse tracking device, gesture tracking device,
subject movement tracking software installed on a generic computing
device, etc.) or other device that allows the recording of pen
strokes. Such a digital pen 155 may record the position of the pen
point on the page, for example, dozens of times per second (such as
75 times per second) with accuracy of a small fraction of the
drawing size in the x- and y-directions (for example, 0.05 mm).
[0050] The system may record the drawing as a series of strokes
corresponding to samples of pen position in time while it was
pressed on the paper. The pen 155 may record the (x[n],y[n])
coordinates of the position of the n.sup.th sample together with,
for example, a timestamp t[n] and/or pressure p[n] for each time
sample. Some pens may be able to record angle and rotation as well.
Other coordinate frames, such as polar, may be used, and the
sampling rate need not be uniform. A stroke classification
algorithm may assign each stroke to one of several predefined
symbol types. In one embodiment, only the stroke identified as the
"clockface" outline might be used in the tremor test. This is the
circle that is supposed to mark the boundary of the clock, and be
big enough to hold all the digits and the hands. Characteristics of
this strategy include that the clockface outline may be drawn
free-hand, may be the longest stroke, and may typically take 2-4
seconds or more to draw, so there is time for tremor to manifest
itself. It may be helpful that the shape is a circle, which forces
the writer to move the pen 155 through all directions, forcing
multiple muscles to try to guide the pen 155 through a variety of
hand positions. At the same time, the shape has no sharp corners so
a healthy person (or unhealthy) should not need to make sudden
high-speed changes in velocity or acceleration.
[0051] FIG. 2 is an example of a complete clock drawing 200 from a
healthy subject, while FIGS. 3A-3D show analyses of the stroke
identified as the clockface outline from FIG. 2. The original
clockface of FIG. 2 may be somewhat circular or elliptical.
Sometimes the subject may use multiple strokes to finish the
outline. In some embodiments only the single longest stroke may be
used, while in others one or more of the strokes in the outline may
be used, and yet other embodiments may choose to analyze other
clock components such as the hour and minutes hands. In general,
tremor tends to oscillate in the range of 4-8 Hz, and so accurate
results may be achieved by using the longer strokes in the drawing
that take one or more seconds to draw.
[0052] A possible first step in processing digitized pen positions
is to derive a pen path in x,y coordinates as a function of time
with samples interpolated at a uniform rate. For example, some
digitizing devices may omit intermediate samples if the path is
straight and can be predicted from samples at the start and end of
a segment. Other devices may change the sampling rate depending on
pen speed. In any case, it is helpful (though not necessarily
required) to interpolate the pen position to an (x[n],y[n]) path
uniformly sampled in time n.
[0053] The next step may compute first and second order vector
differences between successive samples, which can be viewed as
initial estimates of pen velocity and acceleration in two
dimensions. FIG. 3A shows an example clockface circle drawn by a
healthy individual. FIGS. 3B and 3C show example unfiltered first
and second order vector differences between successive samples.
Each sample on the line in the graph corresponds to first or second
order (x,y) differences, at different moments in time. FIG. 3B with
first-order differences is a simple estimate of pen velocity, and
FIG. 3C with second-order differences is a simple estimate of pen
acceleration. Other signal processing techniques that we discuss
later may also be used to estimate velocity and acceleration. In
FIGS. 3B and 3C, distance from the origin corresponds to the
magnitude of pen velocity and acceleration, respectively, while the
angle relative to the origin indicates the direction of the pen
velocity and acceleration at a moment in time. FIG. 3D plots the
magnitude of the first order vector difference (pen velocity) as a
function of sample (time). If the patient had perfect drawing
ability, FIG. 3A would show a perfect circle, and velocity graph
FIG. 3D would show constant velocity over time, perhaps with start
and stop transients at the ends. FIG. 3B would be a perfect circle
assuming drawing velocity (distance from the origin) held constant
as the pen direction rotated through a full 360 degrees.
Acceleration in FIG. 3C would also be a perfect circle, with the
radius being the magnitude of acceleration and the angle
perpendicular to the pen velocity vector. Of course, even healthy
patients like this one cannot execute a complex stroke like a
circle with such perfection. Note that in FIGS. 3B and 3D, the
velocity ramps up quickly to a relatively constant magnitude as the
pen circles around the clockface once. The angle of pen velocity in
FIG. 3B also circles around once through 360 degrees as the pen
makes the full circuit around the clockface. The magnitude of
velocity is a bit uneven over time, and the stroke ends abruptly
without velocity returning to zero--this subject lifted the pen 155
off the paper before slowing down. Hand motion involves many subtle
movements, and so despite the smoothness of the clockface outline,
velocity is not as smooth as one might have expected. The second
order difference example (as an approximate measure of
acceleration; FIG. 3C) is even more ragged, though it does show
acceleration rotating through all 360 degrees in a somewhat
circular manner.
[0054] The pen speed in FIG. 3D shows the magnitude of the first
order difference in mm/sec as a function of sample number. The
sample rate of the pen is fixed (in this example it is 75 samples
per second), so drawing time may be estimated by dividing the
sample number by the sampling rate. Note that in this example it
appears that the user managed to keep the pen moving at a
reasonably constant speed through the entire circle.
[0055] Even with these rough measures of velocity and acceleration,
the pictures look quite different for a subject with Essential
Tremor, such as the clock drawing 400 example presented in FIG. 4.
FIG. 5A shows, as an example, just the clockface outline plus the
first and second order vector differences in FIGS. 5B and 5C,
respectively, and the magnitude of the first order difference as a
function of sample in FIG. 5D.
[0056] Note that the average pen speed in FIG. 3D for the healthy
patient is around 80 mm/sec, but the average pen speed in FIG. 5D
for the impaired patient is closer to 50 mm/sec. This user is
drawing slowly, probably in order to better control their hand
motion. The fluctuation in pen speed in the impaired FIG. 5D is
higher than the fluctuation in pen speed in the healthy FIG. 3D.
The directional velocity vector graph in FIG. 5B shows rapid
oscillation in velocity, instead of the circle we would have
expected, and the acceleration vector in FIG. 5C is 2-4 times
higher in this impaired drawing than in the healthy drawing FIG.
3C.
[0057] Insight into the signal processing required to enhance the
differences between healthy and tremor drawing may be gained by
examining the frequency content of the drawings. FIG. 6 and FIG. 7
show example graphs 600 and 700, respectively, of the Fourier
Transform magnitude squared of the X and Y coordinates of the
mean-removed clockface circles from the healthy subject and from
the subject with Essential Tremor. Each graph contains two plots
corresponding to the Fourier Transform of the X and the Y position
coordinates. There is a strong low frequency component
corresponding to the gross circular motion in all the spectra, but
the subject with tremor has extra energy visible in the 4-8 Hz
range. There is a small amount of energy boost in the tremor
subject from 9-12 Hz approximate range, but above about 13 Hz there
is little apparent structural difference between the spectra of the
healthy and the impaired drawings. This frequency structure is
consistent with academic research suggesting that the expected
frequency band of tremor should be around 4-6 Hz.
[0058] In the next two sections of this disclosure (entitled "Data
Acquisition and Pre-Processing" and "Tremor Quantification
Features"), algorithms are presented that quantitatively correlate
with the amount of tremor without being misled by the typical
unevenness of voluntary movements by healthy individuals. One
technique is to measure the deviation of the drawing from the
intended shape, velocity and acceleration, focusing on the tremor
frequency band of 4-8 Hz while suppressing lower frequencies, which
may be the result of voluntary motion, and/or suppressing
irrelevant data in the upper frequencies from the pen 155.
[0059] Data Acquisition and Pre-Processing
[0060] FIG. 8 shows an example block diagram 800 of the signal
processing that may be performed in accordance with techniques
presented herein. First, the stream of points and strokes from the
digital pen 155 may be decompressed at step 805. This data may
include position data (X, Y), time data (T), pressure data (P),
Tilt and Rotation, etc. For I/O data efficiency, the sampling rate
may be non-uniform. For example, the pen 155 may skip over points
that are in a straight line at constant speed at step 810, so these
missing points may first be reconstructed through interpolation. An
important improvement to estimating pen position and velocity is to
use filtering in order to reduce the noise in the pen position
estimates. An initial estimate may be determined of the intended
direction and speed by low-pass filtering the position information,
which may then be processed through a smoothed derivative filter in
order to estimate velocity. For example, suppose the pen position
was sampled at 75 Hz. The low-pass, shown at step 815, may be a
symmetric, linear phase finite impulse response (FIR) filter of
order 18, with 2 Hz cutoff (see FIG. 9). The derivative filter,
shown at step 820, may be designed to behave like a first order
difference up to approximately 10 Hz, but then drop into a stop
band as it approaches the Nyquist frequency. For example, the
derivative filter may be an odd-length order 8 anti-symmetric
generalized linear phase finite impulse response (FIR) filter (see
FIG. 10). The output of the first filter is a smoothed estimate of
pen position as a function of time, and the second filter gives a
smoothed estimate of velocity that includes the key frequency band
of 4-8 Hz. Note that the input to each filter is a two-dimensional
(x,y) vector function of time, and so each filter shown is really a
pair of filters, one for the X component and one for the Y.
[0061] On a separate branch, at step 825, the (x,y) pen data may be
processed through a 4-8 Hz bandpass filter in order to keep only
the frequencies of interest for detecting tremor. For example, this
could be a bandpass FIR filter of order 18 (see FIG. 11). This may
then be passed through a cascade of two smoothed derivative
filters, at steps 830 and 835, to form a bandpass filtered velocity
vector signal and a bandpass filtered acceleration signal. The same
derivative filter design may be used as in step 820, or a different
filter could be used.
[0062] The specific filters used as examples here are appropriate
for a pen sample rate of 75 Hz, but may need to be modified
appropriately for different pen sample rates. A variety of
different low pass, bandpass and derivative filter structures could
be implemented (Parks-McClellan, least squares, windowed-sinc
function, infinite impulse response (IIR), etc.). In addition, the
lower and upper thresholds of the frequency band of interest, 4 and
8 Hz respectively in the aforementioned embodiments, could be
adjusted up or down. If the digitizing pen has relatively low noise
levels, then the bandpass filter could also be replaced by a high
pass filter, keeping frequencies above 4 Hz or so.
[0063] One consideration in the filter design is that the clockface
outline stroke may typically be only about 70-400 samples long at
75 Hz sampling. If an FIR filter of length L (L taps) is applied to
N points of data, and extrapolating the data outside the interval
[0,N-1] is undesirable, then only N-L+1 samples of the output may
be able to be computed. When FIR filters are cascaded, as in steps
830 and 835, the valid sample count may drop after each filtering
stage. To preserve as many data samples as possible for analysis,
it may be helpful to keep the filters short. On the other hand,
longer filters may achieve better stopband suppression. The example
filter lengths are a compromise. The bandpass acceleration signal,
in our example, would be 34 samples shorter than the clockface,
which means that about 20% of a clockface that took 2 seconds to
draw may be lost (the sample rate is 75 Hz).
[0064] One issue is that the start and stop of each pen stroke
involves different muscle movements than the middle portion, and
often there are "hooklet" shapes at the start and end due to
transient start/stop hand behavior. One benefit of shortening the
data with each filter pass is that data corrupted by the hooklet or
other start/stop motion is discarded. It also may be preferable to
remove the end segments altogether before filtering to avoid
contaminating the rest of the data, although this further reduces
the amount of data available for analysis.
[0065] A variable length filter may instead be used that is
appropriately shorter at the beginning and end of the data segment.
As mentioned above, however, the ends may need to be truncated to
remove the hooklets, so this alternative filtering strategy might
not provide much improvement.
[0066] FIGS. 12A-12D show the (X,Y) components of the outputs of
the filters in FIG. 8 as they evolve over time for the clockface
stroke drawn by a healthy individual. FIG. 12A shows the bandpass
filtered pen position signal B[n], which traces a near-circle. FIG.
12B shows the bandpass velocity signal v.sub.B[n], and FIG. 12C
shows the bandpass acceleration signal .sub.B[n]. Both of these
would be circles if the subject was drawing perfectly.
Nevertheless, both of these signals are comparatively small, and
are distributed somewhat uniformly in direction. FIG. 12D shows the
lowpass filtered velocity v.sub.L [n], which we would expect to be
a circle if perfectly drawn, and which is recognizably close to
that.
[0067] FIGS. 13A-13D show the same filtered signals for a subject
with Essential Tremor. The differences between these two sets of
graphs are quite clear. Low-pass velocity for the healthy subject,
in FIG. 12D, is somewhat smooth, but the tremor subject in FIG.
13D, has somewhat more ragged low-pass velocity. On average, this
tremor subject's low-pass velocity is about half that of the
healthy subject's velocity. Bandpass velocity for the tremor
subject, in FIG. 13B, is double that of the healthy subject, from
FIG. 12B, and bandpass acceleration for the tremor subject (FIG.
13C) may be up to five times larger or more than that of the
healthy subject (FIG. 12C). The relative sizes of these signals may
be different for different patients.
[0068] The low-pass filtered velocity may be viewed as reflecting
the intended pen direction. It therefore may be useful to compute
the projections of the bandpass velocity both parallel and
perpendicular to the low-pass velocity, as shown in FIGS. 14A-14C.
One purpose of separating out the velocity and acceleration into
directions parallel to and perpendicular to the smoothed velocity
curve is that tremor that is visible in the drawing to a human
observer corresponds to velocity and acceleration fluctuations
perpendicular to the smoothed velocity direction. Such
perpendicular fluctuations result in a wavy drawn line oscillating
from side to side along the stroke direction. Fluctuations in
velocity and acceleration parallel to smoothed velocity are
different in that they may cause the pen 155 to speed up and slow
down in the general direction of travel, but they might not leave
as visible a mark on the written page.
[0069] To formalize the different signals considered, it may be
helpful to start by defining some notation. Let x[n]=(x[n], y[n])'
be the column vector of interpolated pen positions for n=0, . . . ,
N-1. Because the filters may be odd-length and either symmetric
(low-pass, bandpass) or anti-symmetric (derivative), it may be
convenient to center all the filters at the origin. Let h.sub.L[n]
be the impulse response of the low-pass filter with non-zero
taps[-N.sub.L,N.sub.L], let h.sub.B[n] be the impulse response of
the band-pass filter with taps[-N.sub.B,N.sub.B], and let
h.sub.D[n] be the impulse response of the derivative filter with
taps [-N.sub.D,N.sub.D]. It may be convenient, though not
necessary, to have the low-pass and bandpass filter lengths be the
same, N.sub.L=N.sub.B. Then the smoothed, low-pass filtered
position may be:
x _ L [ n ] = k = - N L N L h L [ k ] x _ [ n - k ] for n = N L , ,
N - 1 - N L ##EQU00001##
[0070] The smoothed velocity estimates may be:
v _ L [ n ] = k = - N D N D h D [ k ] x _ L [ n - k ] for n = N D +
N L , , N - 1 - N D - N L ##EQU00002##
[0071] Similarly, the bandpass filtered velocities
v.sub.B[n]=(v.sub.Bx[n],v.sub.By[n])' and accelerations
a.sub.B[n]=(a.sub.Bx[n],a.sub.By[n])' may be derived as
follows:
x _ B [ n ] = k = - N B N B h B [ k ] x _ [ n - k ] for n = N B , ,
N - 1 - N B ##EQU00003## v _ B [ n ] = k = - N D N D h D [ k ] x _
B [ n - k ] ##EQU00003.2## for n = N D + N B , , N - 1 - N D - N B
##EQU00003.3## a _ B [ n ] = k = - N D N D h D [ k ] v _ B [ n - k
] ##EQU00003.4## for n = 2 N D + N B , , N - 1 - 2 N D - N B
##EQU00003.5##
[0072] The velocity in the direction of pen motion may be
calculated by taking the inner product between the bandpass
velocity with the direction of the low-pass filtered velocity. This
can also be viewed as a projection operation, and it results in the
signal v.sub.P[n]. The velocity perpendicular to pen motion may be
calculated by taking the cross-product between the bandpass
velocity with the direction of the low-pass filtered velocity. This
gives signal v.sub..perp.[n].
v P [ n ] = 1 v _ L [ n ] ( v Lx [ n ] v Bx [ n ] + v Ly [ n ] v By
[ n ] ) ##EQU00004## for n = N D + N B , , N - 1 - N D - N B
##EQU00004.2## v .perp. [ n ] = 1 v _ L [ n ] ( v Lx [ n ] v By [ n
] - v Ly [ n ] v Bx [ n ] ) ##EQU00004.3##
[0073] Similarly, to compute the acceleration in the direction of
pen motion, take the inner product of the bandpass acceleration
with the direction of the low-pass filtered velocity, giving
a.sub.P[n]. To compute the acceleration perpendicular to pen
motion, take the cross-product between the bandpass acceleration
with the direction of the low-pass filtered velocity, giving
a.sub..perp.[n].
a P [ n ] = 1 v _ L [ n ] ( v Lx [ n ] a Bx [ n ] + v Ly [ n ] + a
By [ n ] ) ##EQU00005## for n = 2 N D + N B , , N - 1 - 2 N D - N B
##EQU00005.2## a .perp. [ n ] = 1 v _ L [ n ] ( v Lx [ n ] a By [ n
] - v Ly [ n ] a Bx [ n ] ) ##EQU00005.3##
[0074] If the user were drawing a perfect circle at constant
velocity, and we ignore the filtering for the moment, then we would
expect v.sub.P[n] to be the pen velocity, and v.sub..perp.[n] would
be zero. We would expect acceleration to be perpendicular to the
velocity and aligned in the radial direction when drawing a perfect
circle so that a.sub.P[n] should be zero and
a.sub..perp.[n]=v.sub.P.sup.2[n]/Radius. Of course, even healthy
subjects do not draw perfect circles.
[0075] FIGS. 14A-14C show these signals for the example healthy
individual. FIG. 14A displays an example graph 1400 of a low-pass
velocity, showing the X and Y components of the smoothed velocity
vector. If the drawing were a perfect circle, these curves would
look like cosines and sines, and the curves do in fact resemble
those shapes. FIG. 14B displays an example graph 1430 showing
bandpass velocity projected parallel and perpendicular to the
smoothed low-pass velocity vector (v.sub.P[n] and v.sub..perp.[n]).
The perpendicular velocity component is close to zero, though there
is some fluctuation. FIG. 14C shows an example graph 1460 of
bandpass acceleration projected parallel and perpendicular to the
low-pass velocity vector. Both components are comparatively
small.
[0076] FIGS. 15A-15C depict the same signals, but for a patient
exhibiting tremor. FIG. 15A displays an example graph 1500 of the
low-pass filtered pen velocity of a patient with tremor. Note that
it is more distorted than FIG. 14A. FIG. 15B displays an example
graph 1530 of the bandpass filtered pen velocity of a patient with
tremor, and FIG. 15C displays an example graph 1560 of the bandpass
filtered pen acceleration of a patient with tremor. Note that,
unlike the ideal case, the parallel and perpendicular components of
both velocity and acceleration are of comparable magnitude, and the
size of the oscillations in the acceleration in particular are much
larger than the acceleration components of the healthy patient.
Although there is oscillatory behavior in these signals due to the
tremor, the frequency may not be fixed and the amplitude may depend
on the precise position of the hand and the relative velocity
direction.
[0077] Note that these tremor signals are large and measurable even
though the person writing has their hand resting on the paper and
may be trying hard to keep the pen steady.
[0078] Tremor Quantification Features
[0079] There are a number of ways that the strength of the bandpass
velocity and acceleration signals could be used to quantify the
tremor. The system could measure the total energy in one or more of
the bandpass filtered and/or parallel/perpendicular signals,
different energy norms could be used (sum of squares, L1 norm, and
so forth), higher order derivatives could be measured, windows
could apply different weights to the center vs. the ends of the
stroke in a fixed or adaptive manner, and so forth. As an example,
some useful features that correlate well with tremor are:
PnAccLogEnergy = log ( 1 K A M A n a _ B [ n ] 2 ) ##EQU00006##
PnCurvLogEnergy = log ( 1 K A M A n a .perp. 2 [ n ] )
##EQU00006.2## PnAccParLogEnergy = log ( 1 K A M A n a P 2 [ n ] )
##EQU00006.3## PnSpdLogEnergy = log ( 1 K V M V n v _ B [ n ] 2 )
##EQU00006.4## PnSpdPerpLogEnergy = log ( 1 K V M V n v .perp. 2 [
n ] ) ##EQU00006.5## PnSpdParLogEnergy = log ( 1 K V M V n v P 2 [
n ] ) ##EQU00006.6##
Here PnAccLogEnergy is the energy (sum of squares) of the bandpass
filtered acceleration magnitude, normalized by dividing by the
number of terms in the sum, M.sub.A=N-4N.sub.D-2N.sub.B, and
further normalized by a scaling factor, K.sub.A, that may
compensate for drawing size, drawing time, overall speed, and so
forth. The log( ) function may be used to make the distribution of
feature values look more Gaussian. PnCurvLogEnergy is a similar
feature that only sums the energy in the acceleration component
perpendicular to pen motion. This can also be thought of as a
measurement of the energy of the curvature of the stroke.
PnAccParLogEnergy measures the energy in the acceleration component
parallel to the pen motion. PnSpdLogEnergy is the energy (sum of
squares) of the bandpass filtered velocity magnitude, normalized by
dividing by the number of terms in the sum,
M.sub.V=N-2N.sub.D-2N.sub.B, and further normalized by a scaling
factor, K.sub.V, that may compensate for drawing size, drawing
time, overall speed, and so forth. The log( ) function may be used
to make the distribution of feature values look more Gaussian.
PnSpdPerpLogEnergy is similar, but it sums the energy in the
perpendicular bandpass velocity signal. PnSpdParLogEnergy is
similar, but it sums the energy in the parallel bandpass velocity
signal.
[0080] The normalizing factors may be important to achieving good
results. For example, suppose the subject doubled the height and
width of the drawing but still drew the clock in the same amount of
time. All velocities and accelerations would double. On the other
hand, if the diagram were drawn in the same size but in twice the
time, the velocities would halve and the accelerations would drop
by a quarter. In both of these examples, however, it may be
desirable for the tremor feature value to remain unaltered.
Compensating for these factors may be important because individuals
affected by movement disorders may draw relatively small diagrams,
moving the pen carefully and slowly.
[0081] One example technique is to normalize the bandpass velocity
by dividing by the average velocity one would expect given the
length of ink and the time required to draw the clockface.
v _ ~ B [ n ] = v _ B [ n ] / v avg ##EQU00007## where v avg =
Length of Ink Drawing Time ##EQU00007.2##
[0082] Doubling the size or doubling the time would then leave
features depending on bandpass velocity unchanged. Similarly,
bandpass acceleration may be normalized by dividing by the average
acceleration:
a _ ~ B [ n ] = a _ B [ n ] / a avg ##EQU00008## where a avg =
Length of Ink Drawing Time 2 ##EQU00008.2##
[0083] The preceding embodiments may underestimate the complexity
of the normalization problem since speeding up and slowing down or
drawing large or small may also change the frequency content of the
tremor, which in turn may change how much energy passes through the
bandpass filter and thereby change the feature. It may not be clear
how the amplitude and frequency of the tremor changes when the
subject tries to write faster or larger. In some embodiments,
velocity and acceleration may be normalized by the same or similar
factor, the square of the ratio of ink length to drawing time. For
the features above, this may yield normalizing constants:
K A = K V = ( Length of Ink Drawing Time ) 2 ##EQU00009##
[0084] Similar strategies may use the size of the drawing, size of
the bounding box, average radius, and so forth, instead of length
of ink.
[0085] Each of the features described above may increase with
tremor, and so may be viewed as tremor scores. Other variations of
these features such as those discussed earlier can be devised that
may also behave as tremor scores. In addition, multiple features
may be combined in formulas or algorithms in order to build yet
other tremor scores.
[0086] The digital clock test may have two separate clock drawings,
the "command" where the individual draws the clock from memory, and
the "copy" where the individual copies a pre-drawn clock. Tremor
may be estimated using the two outline strokes separately, and
scores may be combined from the two clockfaces in the two clocks.
Including more pen stroke data (such as data obtained when drawing
additional details of the clock, e.g. numbers and hands) may
improve the ability to distinguish between presence and absence of
tremor. In addition, multiple features may be combined, for example
a feature using velocity may be combined with another using
acceleration, in an attempt to use whatever additional information
might be available. These ideas are now discussed in the context of
data collected from a trial of this system.
[0087] Comparing Essential Tremor and Healthy Subjects
[0088] Subjects were selected who were diagnosed to have Essential
Tremor (60 total, 36 males, age 65.6.+-.11.8 years) or who were
healthy controls (59 total, 14 males, age 55.1.+-.12.5 years). They
were administered Digital Clock Drawing tests, and the features on
each of the command and copy clockface outlines were computed for
these subjects. When the Digital Clock Test is used to screen for
cognitive impairments, the Command and Copy clocks may have quite
different characteristics, but they may be equally susceptible to
tremor and may be treated equally. As shown in FIGS. 16A-16C,
histograms were computed of each feature for the healthy and
Essential Tremor populations, and ROC curves were computed by
considering all possible thresholds. Effective measures for this
data set included PnAccLogEnergy, the energy in the bandpass
acceleration, as shown in graph 1600 in FIG. 16A,
PnSpdPerpLogEnergy, the energy in the bandpass velocity
perpendicular to the low pass filtered velocity, as shown in graph
1630 in FIG. 16B, and PnCurvLogEnergy, the energy in the component
of bandpass acceleration perpendicular to the smoothed velocity
(curvature), as shown in FIG. 16C.
[0089] In this test, these histograms were normalized by dividing
by the number of clockfaces so that the height of each bar
represented a frequency of occurrence. In general, for all three
scoring metrics, subjects with tremor have scores that were usually
larger than scores of healthy subjects. In all three cases, there
is some overlap in scores between the tails of the tremor and
healthy populations (denoted as "crossover" in FIGS. 16A-16C.) In
general, though, these three metrics had comparable performance at
separating the Essential Tremor and healthy populations.
[0090] FIG. 17 discloses an example graph 1700 of the Receiver
Operating Characteristic (ROC) curves for these metrics. To
generate a ROC, a feature like one discussed above is chosen and
compared with a threshold. If the score is above that threshold the
subject is assigned the "tremor" label, and if below then the
subject is assigned the "healthy" label. The probability of false
alarm (PF) is the fraction of subjects who have been diagnosed by
medical professionals as healthy, but whose feature scores are
above threshold. The probability of detect (PD) is the fraction of
subjects who have been diagnosed by medical professionals as having
tremor, and for whom the feature score is above threshold. The ROC
curve may be built by calculating PF and PD for all possible
thresholds and plotting these two statistics in a 2D graph. At one
extreme with the threshold set to -.infin. no patients are
diagnosed as having tremor so PF=0 and PD=0. At the other extreme
with the threshold set to +.infin. all patients are diagnosed as
having tremor, so PF=1 and PD=1. A perfect test would have a
threshold such that PF=0 and PD=1. Note that the ROC curve in FIG.
17 shows that performance is not perfect, but it is possible to set
the threshold so that PF.ltoreq.10% while PD.gtoreq.90%.
[0091] A metric called Area Under the Curve (AUC) measures the area
underneath the ROC curve. A perfect test would have AUC=1.0, while
a test that had no discrimination ability at all would have
AUC=0.5. Area Under the Curve (AUC) values from the data set
presented earlier are in Table 1. The first column gives the AUC if
each clockface circle is considered independently. The second
column shows the AUC if the scores for the two clockfaces from each
subject are averaged. These AUC scores are all close to 1.0.
TABLE-US-00001 TABLE 1 AUC for different metrics, treating the two
drawing separately or combined Command and Command + Metric Copy
separately Copy combined PnAccLogEnergy 0.970 0.977 PnCurvLogEnergy
0.977 0.987 PnSpdPerpLogEnergy 0.980 0.987
[0092] To describe what the feature values imply about visible
tremor in the drawings, the healthy and Essential Tremor subjects
whose graphs were shown in FIGS. 3 and 5 had the following feature
values:
TABLE-US-00002 TABLE 2 Values for Healthy and Essential Tremor
Subjects in the Earlier Examples PnAcc- PnCurv- PnSpdPerp- Subject
LogEnergy LogEnergy LogEnergy Healthy Subject (FIG. 9.4 8.4 1.09
3A-3D) Essential Tremor 13.9 13.0 5.21 Subject (FIG. 5A-5D)
[0093] Other features from both drawings may be used to further
improve the score. For example, on the data set disclosed above, if
(PnCurvLogEnergy+PnSpedPerpLogEnergy)/2 is averaged over the two
drawings and used as the score, it raises the AUC for this
particular dataset to 0.99. However, in general, the gains from
combining these features may be limited because they may be highly
correlated. FIG. 18 discloses an example graph 1800 that plots
PnCurvLogEnergy against PnSpdPerpLogEnergy for each of the drawings
for the Essential Tremor and Healthy populations. Note that the
values cluster tightly along a line, suggesting strong correlation
between these metrics, which in turn implies that little additional
information is gained by using both.
[0094] Comparing Essential Tremor Subjects with Others
[0095] Tremor may be compared with diagnoses of other neurological
conditions, including Parkinson's Disease, Alzheimer's Disease,
vascular dementia, and others. To do this, the patient database may
be expanded to include patients with a wide variety of neurological
conditions in addition to Essential Tremor. The key difficulty is
that many neurological disorders, most notably Parkinson's, may
have tremor as a side effect. Patients whose primary diagnosis is
not Essential Tremor may therefore score high on the tremor scales
discussed above, not because of a flaw in the algorithms, but
because tremor is a secondary effect associated with their primary
disease.
[0096] The ability of these tremor quantification metrics to pick
up tremor and in distinguishing medical conditions from each other
may be further improved and extended. Some examples are:
[0097] Using these tremor quantification metrics and other metrics
extracted from the drawing process to distinguish the tremor
associated with Essential Tremor from the tremor associated with
Parkinson's disease.
[0098] Monitoring the tremor quantification metrics over time in
order to track changes in Essential Tremor.
[0099] Tracking changes in tremor in subjects that undergo Deep
Brain Stimulation surgery.
[0100] Mild Tremor
[0101] The clock drawing of FIG. 19 illustrates a set of issues
when judging presence of tremor in cases where the tremor is
relatively mild. As shown in drawing 2100 of FIG. 19, by itself the
clock drawing may not look particularly tremulous. However, as
shown in FIGS. 20A-20D, plotting the low pass filtered clockface,
and the bandpass velocity and acceleration reveals a tremor that is
barely visible in FIG. 19. The low-pass filtering of the clockface
enhances the tremor in FIG. 20A. The bandpass velocity curve in
FIG. 20B shows oscillation in the speed of the pen (the distance to
(0,0) keeps changing) that dwarfs the expected circular shape. The
bandpass acceleration in FIG. 20C suggests erratic control of the
pen. FIG. 21 at graph 2330 shows the bandpass velocity parallel and
perpendicular to the direction of pen travel. Note the clear
oscillatory tremor at around 7-8 Hz (there are about 7-8
oscillations in each 75 samples, which is a second of data.) This
example clockface outline yielded a PnCurvLogEnergy score of 11.1,
on the border of where one might put the threshold between normal
and tremor.
[0102] FIG. 22 discloses an example block diagram 2400 of a
technique presented herein. Step 2405 comprises receiving data from
a digital device, the data comprising a plurality of digital device
positions and a plurality of timestamps, each timestamp in the
plurality of timestamps being associated with a digital device
position in the plurality of digital device positions. At step
2410, a plurality of frequencies of hand movements of the subject
may be determined based on the plurality of digital device
positions and plurality of timestamps. At step 2415, a low tremor
threshold may be determined, the low tremor threshold corresponding
to a predetermined hand tremor frequency. At step 2420, a
subportion of the data may be determined corresponding to
frequencies of hand movements above the low tremor threshold. At
step 2425, a magnitude of tremors of the subject's hand may be
determined based, at least in part, on the subportion of the
data.
[0103] Additional Techniques
[0104] Features discussed herein may be used to create tremor
scores whose rating accuracy matches or exceeds that of medical
professionals working from drawings on paper. One reason is that
the telltale wavy lines drawn by a person with tremor make visible
only the perpendicular component of velocity. With a digitizing
pen, the system can achieve better performance because it can also
measure oscillations in the direction of pen travel. This suggests
that tremor rating by medical professionals might be improved by
playing back a video of the patient drawing the picture. Erratic
speed up and slow down of pen motion in the direction of travel may
be nearly as visible as erratic pen motion perpendicular to travel.
On the other hand, if the goal were to match the judgments of the
medical professionals looking at static drawings, then it may be
best to restrict the system to analyzing perpendicular components
of velocity and acceleration.
[0105] The example features discussed earlier sum energy in the
various filtered signals with equal weight to all samples summed.
Alternative approaches are possible, such as assigning less weight
to samples near the endpoints of the pen stroke, or using L1 norms
or other measures.
[0106] Further, the algorithm might look at more than just the
clockface outline stroke, processing any secondary strokes used to
finish the clockface outline, plus perhaps strokes for the hour and
minute hands. It may be desirable, however, to use only longer
strokes in the analysis and algorithms.
[0107] It is also noted that, if accurate pressure measurements
were available from the digitizing device, that normalization of
the features might be adjusted according to pressure. This is
because it is possible that more pressure might correspond to more
highly stressed muscles which might increase tremor.
[0108] Techniques discussed herein may execute independently or
coupled with the system that judges the cognitive impairment of the
subject. Performance might be improved by using information about
cognitive state to adjust thresholds or modify processing. For
example, if the individual is believed to have dementia, then
sudden changes in pen direction might be more likely caused by
cognitive problems rather than tremor.
[0109] Further, the digitizing pen may compress lines that are
mostly straight by dropping intermediate points. The interpolation
scheme simply fills in points at uniform spacing along an exact
line through the gap. The first derivative of the line in these
interpolated sections may be constant and acceleration may be zero.
This may distort the tremor quantification features. Fortunately,
people with tremor tend not to move the pen in straight lines, so
not many points will be interpolated. This anomaly may cause
healthy subjects to have lower tremor scores than expected.
[0110] Also, a large set of healthy subjects may be used to get a
better estimate of false alarm rate for the features. Expanding the
range of data available should be possible as use of the digital
clock test expands to more researchers.
[0111] In addition, techniques presented herein may further require
drawing with the non-dominant and non-writing hand. Yet another
possibility is that a screening or diagnostic system could be
designed so that a high tremor score on the drawing test may
trigger requests for additional drawings or other tests in order to
refine the scores and improve accuracy.
Features of Embodiments
[0112] The digital version of the Clock Drawing Test may be
augmented to measure tremor from the clockface and other stroke(s).
Unlike existing techniques, the task may be very natural to the
individual with their hand resting on the paper and drawing
freehand on a sheet of paper. While the Clock Drawing Test is
discussed herein, other drawing tests may be used. For example,
pictures of other objects or faces, copied or traced or drawn from
memory, spiral or pattern shapes, and others may all be used to
measure tremor, for example from long strokes.
[0113] As discussed herein, if the goal is to match the ratings
assigned by medical professionals observing the drawings, then
measurements of motion perpendicular to the smoothed velocity
vector may be used because these motions correspond closely to the
features that are more easily observed by human vision. In
addition, the deviation of the drawing from the intended shape,
velocity, and acceleration may be measured. In particular, the
tremor frequency band of 4-8 Hz may be given particular attention,
while lower frequencies may be suppressed, as lower frequencies may
be the result of voluntary motion. Frequencies higher than the 4-8
Hz band may similarly be suppressed.
[0114] Other techniques may be used in combination herein. For
example, missing time-stamped pen coordinates or a non-uniform
sampling may be resampled to a uniform sample rate through
interpolation. Upsampling or downsampling might be used to adjust
the digitizing device sampling rate. As discussed above, a variety
of filter design algorithms, such as Parks-McClellan, least squares
and linear programming, may be used. A variety of filter lengths
may be used, especially if the sample rate is different than 75 Hz.
The passband may also vary. If the digitizing pen has relatively
low noise levels, then the bandpass filter could also be replaced
by a high pass filter, keeping frequencies above 4 Hz or so.
[0115] Energy in bandpassed velocity and/or acceleration may be
used to estimate tremor. A component of bandpassed velocity and/or
acceleration, either at a fixed angle or perpendicular or parallel
to pen motion, may be used to estimate tremor. If at a fixed angle,
that angle may be selected in part by the subject's dominant hand,
or by the positioning of the subject's fingers. The fixed angle may
be estimated from the digital recording by, for example, finding
the direction angle with greatest oscillatory energy.
[0116] Further, the tremor feature may be used to create a metric
for subject screening for tremor. A tremor feature may also be
created that mimics the tremor rating from human experts.
[0117] In addition, various transformations may be used such as a
log-transform to ensure that the metric for subject screening for
tremor has a specific distribution, for example, so that it matches
ratings from human raters.
[0118] Tremor quantification metrics may be combined with other
drawing process metrics, demographic information, and/or other
cognitive testing results to improve the quantification of tremor
and the screening and diagnosis of cognitive conditions.
[0119] Tremor quantification features may be used in a medical
context for any of, for example, differential diagnosis of tremor
predominant disorders (e.g. Essential Tremor) vs. secondary tremor
(e.g. Parkinson's disease with tremor, dystonia with tremor,
drug-induced tremor). Tremor features may further be used for
monitoring and tracking tremor treatment for any change, or
quantifying potential effects related to medical treatment such as
medication or Deep Brain Stimulation. Tremor features may further
be used to calibrate Deep Brain Stimulation to maximize the
reduction in tremor or tremor-related symptoms, or for creating a
score for the potential risk of falling, an event common amongst
movement disorder individuals.
[0120] Further, tremor quantification features determined using
techniques discussed herein may be used in an educational,
vocational, and employment context for any of, for example,
measuring the ability and risk of handling machinery, from
equipment deemed dangerous (e.g. electric saw) to equipment
requiring extreme motion precision by the user for the correct
usage (e.g. circuit board soldering). Surgeons may also be tested
and/or trained to ensure required level of precision. More
generally, these techniques may be used for evaluating fine motor
skills for prospective and current employment decisions.
[0121] Techniques disclosed herein thus are able to detect very
subtle and mild tremor, and are able to do it much earlier in
disease progression than conventional examination via the paper
Clock Drawing Test. The magnitude of tremors may be determined, for
example, by determining directional velocity and acceleration
components of the tracked movements of a device held by the
subject. Techniques disclosed herein may further be used to
determine cognitive impairment. For at least these reasons,
techniques disclosed herein improve the technical field.
[0122] Some embodiments may be described using the expression
"coupled" and "connected" along with their derivatives. It should
be understood that these terms are not intended as synonyms for
each other. For example, some embodiments may be described using
the term "connected" to indicate that two or more elements are in
direct physical or electrical contact with each other. In another
example, some embodiments may be described using the term "coupled"
to indicate that two or more elements are in direct physical or
electrical contact. The term "coupled," however, may also mean that
two or more elements are not in direct contact with each other, but
yet still co-operate or interact with each other. The embodiments
are not limited in this context.
[0123] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having" or any other variation
thereof, are intended to cover a non-exclusive inclusion. For
example, a process, method, article, or apparatus that comprises a
list of elements is not necessarily limited to only those elements
but may include other elements not expressly listed or inherent to
such process, method, article, or apparatus. Further, unless
expressly stated to the contrary, "or" refers to an inclusive or
and not to an exclusive or. For example, a condition A or B is
satisfied by any one of the following: A is true (or present) and B
is false (or not present), A is false (or not present) and B is
true (or present), and both A and B are true (or present).
[0124] In addition, use of the "a" or "an" are employed to describe
elements and components of the embodiments herein. This is done
merely for convenience and to give a general sense of the
invention. This description should be read to include one or at
least one and the singular also includes the plural unless it is
obvious that it is meant otherwise.
[0125] Upon reading this disclosure, those of skill in the art will
appreciate still additional alternative structural and functional
designs for systems and methods for tremor detection and
quantification through the disclosed principles herein. Thus, while
particular embodiments and applications have been illustrated and
described, it is to be understood that the disclosed embodiments
are not limited to the precise construction and components
disclosed herein. Various modifications, changes and variations,
which will be apparent to those skilled in the art, may be made in
the arrangement, operation and details of the method and apparatus
disclosed herein without departing from the spirit and scope
defined in the appended claims.
* * * * *