U.S. patent application number 13/410789 was filed with the patent office on 2013-07-18 for method and device for swallowing impairment detection.
This patent application is currently assigned to MICRON TECHNOLOGY, INC.. The applicant listed for this patent is Thomas T.K. Chau, Joonwu Lee, Catriona M. Steele. Invention is credited to Thomas T.K. Chau, Joonwu Lee, Catriona M. Steele.
Application Number | 20130184538 13/410789 |
Document ID | / |
Family ID | 46515041 |
Filed Date | 2013-07-18 |
United States Patent
Application |
20130184538 |
Kind Code |
A1 |
Lee; Joonwu ; et
al. |
July 18, 2013 |
METHOD AND DEVICE FOR SWALLOWING IMPAIRMENT DETECTION
Abstract
Disclosed herein is a method and apparatus for swallowing
impairment detection, whereby cervical accelerometry and nasal
airflow data is acquired as a candidate executes one or more
swallowing events. Upon feature extraction and classification,
vibrational and airflow data acquired in respect of each swallowing
event is classified as indicative of one of normal or possibly
impaired swallowing. Computer-readable media comprising statements
and instructions for implementation by a processing device are also
described in facilitating swallowing impairment detection
respective to candidate swallowing events.
Inventors: |
Lee; Joonwu; (Somerville,
MA) ; Chau; Thomas T.K.; (Toronto, CA) ;
Steele; Catriona M.; (Toronto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Lee; Joonwu
Chau; Thomas T.K.
Steele; Catriona M. |
Somerville
Toronto
Toronto |
MA |
US
CA
CA |
|
|
Assignee: |
MICRON TECHNOLOGY, INC.
BOISE
ID
|
Family ID: |
46515041 |
Appl. No.: |
13/410789 |
Filed: |
March 2, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
PCT/CA2012/000036 |
Jan 18, 2012 |
|
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13410789 |
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Current U.S.
Class: |
600/301 |
Current CPC
Class: |
A61B 5/7282 20130101;
A61B 5/1123 20130101; A61B 5/1126 20130101; A61B 5/6843 20130101;
A61B 5/726 20130101; A61B 5/1121 20130101; A61B 5/4205 20130101;
A61B 5/7267 20130101 |
Class at
Publication: |
600/301 |
International
Class: |
A61B 5/11 20060101
A61B005/11; A61B 5/087 20060101 A61B005/087 |
Claims
1. A device for use in identifying a possible swallowing impairment
in a candidate during execution of a swallowing event, the device
comprising: an accelerometer to be positioned in a region of the
candidate's throat and configured to acquire vibrational data
representative of the swallowing event; a nasal airflow monitor to
be positioned in a region of the candidate's nostrils and
configured to acquire airflow data representative of the swallowing
event; a processing module operatively coupled to said
accelerometer and nasal airflow monitor for processing said
vibrational data and said airflow data to extract from each one
thereof one or more features representative of the swallowing
event, and classify said vibrational data and airflow data as
indicative of one of normal swallowing and possibly impaired
swallowing based on said extracted features.
2. The device of claim 1, the possible swallowing impairment
comprising at least one of a swallowing safety impairment and a
swallowing efficiency impairment.
3. The device of claim 1, said accelerometer comprising a dual-axis
accelerometer configured to acquire axis-specific data, said
processing module for processing said axis-specific data to extract
from each axis one or more features respective thereto
representative of the swallowing event.
4. The device of claim 3, wherein said features extracted from said
axis-specific data comprise distinct axis-specific features.
5. The device of claim 3, said dual-axis accelerometer configured
for alignment along an anterior-posterior axis (A-P) and a
superior-inferior axis (S-I) of the candidate's throat.
6. The device of claim 1, wherein said extracted features comprise
at least one vibrational data feature distinct from at least one
airflow data feature.
7. The device of claim 1, said processing module configured to
classify the swallowing event by comparing said extracted features
with preset classification criteria defined by features previously
extracted and classified from a known training data set.
8. The device of claim 8, wherein said extracted features are
classified as a function of a distance of said extracted features
from said classification criteria.
9. The device of claim 8, wherein said extracted features are
classified via discriminant analysis using one of Mahalanobis
distances and Euclidian distances.
10. The device of claim 1, said processing module comprising a
feature extraction module for extracting said one or more features
from each of said vibrational data and said airflow data, a feature
reduction module for identifying, based on preset feature reduction
parameters, predominant components of said extracted features, and
a classifier configured to classify said vibrational data and said
airflow data based on said extracted features and said predominant
components thereof.
11. The device of claim 1, for identifying the possible swallowing
impairment during execution of multiple successive swallowing
events, said processing module configured to classify vibrational
data and airflow data acquired in respect of each of said
successive swallowing events as indicative of one of normal
swallowing and possibly impaired swallowing.
12. The device of claim 11, further comprising an event
segmentation module for automatically segmenting vibrational data
and airflow data acquired in respect of each of said successive
events for independent processing and classification.
13. The device of claim 11, further comprising a user interface for
selectively segmenting vibrational data and airflow data acquired
in respect of each of said successive events for independent
processing and classification.
14. The device of claim 11, further comprising an output, said
processing module further configured to process said vibrational
and airflow data acquired in respect of each of said successive
events, and output results thereof in accordance with a preset
swallowing impairment assessment protocol.
15. The device of claim 1, wherein at least one of said extracted
features comprises a value derived from at least one signal wavelet
decomposition level.
16. The device of claim 15, wherein said value comprises at least
one of an energy and an energy ratio associated with said level, or
one or more segments thereof.
17. A computer readable-medium having statements and instructions
stored thereon for implementation by a processor to automatically
process input vibrational data and airflow data representative of a
candidate swallowing event in identifying a possible swallowing
impairment, by: extracting one or more preset features
representative of the swallowing event for each of the vibrational
data and airflow data; comparing said extracted features with
preset classification criteria defined as a function of said preset
features; and outputting, based on said comparing, classification
of said vibrational data and airflow data as indicative of one of
normal swallowing and possibly impaired swallowing.
18. The computer readable-medium of claim 17, said possibly
impaired swallowing comprising one or more of unsafe swallowing and
inefficient swallowing.
19. The computer readable-medium of claim 17, said possibly
impaired swallowing comprising identification of substantial
post-swallow residual material in one or more of valleculae and
pyriform sinuses.
20. The computer readable-medium of claim 17, the vibrational data
comprising dual-axis accelerometry data representative of the
swallowing event, said extracting comprising extracting one or more
features for each axis of said axis-specific data.
21. The computer readable-medium of claim 17, said comparing
comprising calculating a distance of said extracted features from
said classification criteria and selecting a most likely
classification as a function of said distance.
22. The computer readable-medium of claim 21, said calculating
comprising performing a linear discriminant analysis of said
extracted features using at least one of Euclidian and Mahalanobis
distances.
23. The computer readable-medium of claim 17, the vibrational data
and airflow data representative of successive swallowing events,
further comprising statements and instructions for automatically
segmenting the vibrational data and airflow data into
event-specific data, and repeating said extracting and classifying
for each said event-specific data.
24. A method for identifying a possible swallowing impairment in a
candidate via execution of one or more preset swallowing events,
comprising: recording vibrational data and airflow data
representative of the one or more swallowing events; extracting one
or more swallowing-event specific features for each of the
vibrational data and airflow data; and classifying said extracted
features as indicative of one of normal swallowing and possibly
impaired swallowing.
25. The method of claim 24, comprising, for execution of two or
more preset swallowing events, selectively recording said
vibrational data and airflow data for each event independently to
provide event-specific data, and implementing said extracting and
classifying on said event-specific data for each of said
events.
26. The method of claim 24, comprising, for execution of two or
more preset swallowing events, successively recording said
vibrational data and airflow data for each of said events, and
further comprising automatically segmenting said successively
recorded data to provide event-specific data, and implementing said
extracting and classifying on said event-specific data for each of
said events.
27. The method of claim 24, the swallowing impairment comprising at
least one of penetration, aspiration, unsafe swallowing,
inefficient swallowing, and post-swallow residue material in one or
more of valleculae and pyriform sinuses.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation-in-part of International
PCT Application No. PCT/CA2012/000036, filed on Jan. 18, 2012, the
entire disclosure of which is incorporated herein by reference.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates to swallowing impairments,
and in particular, to a method and device for swallowing impairment
detection.
BACKGROUND
[0003] Dysphagia is a serious component of many neurological
diseases and injuries. The incidence of dysphagia following stroke
has been reported to be 37-78% across studies, with aspiration
incidence estimated at 43-54% in those with dysphagia. One
systematic review has concluded that stroke patients who aspirate
face 11.56 times the risk of developing pneumonia, compared to
those without dysphagia. Patients who are aspirate, have been shown
to be 10 times more likely (p<0.0001) to develop pneumonia in
the ensuing 6 months than those with normal swallowing. These
figures speak to the importance of identifying dysphagia and
managing aspiration risk as early as possible, both in potentially
avoiding numerous aspiration-related deaths, and in saving the
healthcare system considerable amounts of money by providing early
treatment. Over the past decade, numerous evidence-based best
practice guidelines have arrived at similar conclusions, strongly
endorsing the early implementation of screening protocols to
identify dysphagia and aspiration in high-risk populations, such as
those with stroke.
[0004] Aspiration, generally understood as the entry of foreign
contents into the upper airway, is a serious concern for
individuals with swallowing difficulty (dysphagia), and can lead to
pneumonia, for example. For instance, prandial aspiration, or the
entry of foreign material into the upper airway during swallowing,
is a serious component of dysphagia.
[0005] Using known and particularly invasive techniques, such as
videofluoroscopic swallowing examinations, aspiration severity may
be sub-classified based on the observed depth of airway invasion.
For example, transient entry of material into the laryngeal
vestibule, above the vocal cords, is termed high penetration (or a
score of 2 on the 8-point Penetration Aspiration Scale); scores of
3-5, termed penetration, apply when material enters the laryngeal
vestibule without subsequent clearance, and aspiration is the term
used when material crosses the vocal cords and enters the trachea
(scores of 6-8). A major dilemma for the detection of aspiration is
the fact that overt clinical signs (e.g., cough or throat clearing)
are reportedly absent up to 67% of the time; this is called "silent
aspiration". The risk of developing pneumonia has been found to be
4, 10, and 13 times greater, respectively, in patients with
penetration, aspiration, or silent aspiration on videofluoroscopy,
compared to individuals with normal swallowing. Evidence-based best
practice guidelines concur that screening protocols should be used
to facilitate the prompt identification and management of
aspiration risk in high-risk populations, such as stroke patients;
however, currently implemented protocols to this end often fail to
provide satisfactory results, or again, achieve reasonable results
at the expense of requiring the application of relatively invasive
procedures.
[0006] In addition to aspiration, swallowing inefficiency is a
major concern in individuals with dysphagia. Swallowing
inefficiency is defined as the inability to swallow the contents of
a single bolus (or mouthful) in a maximum of 2 swallows. This
frequently leads to the presence of residual material being left
behind in the throat (pharynx) after the swallow. The presence of
this leftover material is, in turn, a risk for aspiration.
[0007] The main goals of a swallow screening protocol are generally
two-fold: 1) to identify risk of impaired swallowing safety, i.e.
penetration (entry of material into the airway above the level of
the vocal cords) and/or aspiration (entry of material into the
airway below the level of the vocal cords); and 2) to identify risk
of impaired swallowing efficiency, characterized either by the
presence of residues in the pharynx after the swallow, and/or
prolonged transit times for moving a bolus in entirety from the
mouth into the esophagus. To date, the principal emphasis in health
policy calls for swallow screening has been on the first of these
goals, that is the identification of penetration and/or aspiration
risk (henceforth, "P-A risk"). When patients are identified to have
either dysphagia or P-A risk through screening, they are generally
referred for comprehensive swallowing assessment.
[0008] Unfortunately, the clinical identification of impaired
swallowing safety and efficiency related to dysphagia is not
particularly straightforward. Under usual circumstances, healthy
awake people will swallow reflexively when material penetrates the
airway above the vocal cords, and will cough when this material is
aspirated below the vocal cords. Current P-A risk screening tools
rely heavily on the recognition of overt clinical signs that imply
possible aspiration: coughing, throat clearing, changes in
respiratory rate, and changes in voice quality. In those with
neurologic injury, however, overt clinical signs are frequently
absent or volume-dependent. As noted above, silent aspiration is
reported to occur in 25%-67% of acute stroke patients, and in 28%
of patients overall, according to some studies. The variable
expression of overt clinical signs of impaired swallowing safety in
patients with neurogenic dysphagia contributes to limited success
in P-A risk detection through clinical screening, and means that
screeners must be trained to be alert for signs that are subtle.
Similarly, post-swallow residues, related to swallowing
inefficiency, are not reliably detectable at the bedside based on
the observation of clinical signs, or based on asking patients
whether they feel material sticking in their throats.
[0009] For example, current clinical approaches to non-invasive
screening for aspiration typically involve the swallowing of water.
The clinician notes signs of difficulty, including cough,
post-swallow throat clearing, or voice changes that might imply the
presence of liquid around the vocal cords. However, studies differ
in their conclusions regarding the validity of abnormal clinical
signs for revealing aspiration, compared to blinded ratings of
instrumental assessments, and screening protocols involving sips of
water tend to over-identify aspiration risk with false-positive
rates as high as 72%.
[0010] Furthermore, current approaches to screening frequently rely
on nurses to administer/conduct screening protocols. One
widely-promoted clinical screening protocol (the Tor-BSST) has an
accompanying training package, which involves initial training of 8
hours for a lead clinician/champion/trainer who then delivers
training of 4 hours for individuals who will administer the
screening protocol. However, institutional barriers have been
reported to prevent implementation of screening guidelines, even
after such extensive training. Given the turnover of nursing staff,
a strong institutional commitment to continuing skills training and
credentialing is required on a long-term basis.
[0011] Given the variable performance of swallow screenings for
detecting aspiration, and the burden that this approach involves
for training and competency-maintenance, a need exists for a valid
non-invasive instrumental method to reliably detect impaired
swallowing safety and efficiency, for example in a clinical setting
or at the bedside. While the appraisal of swallowing sounds or
vibrations has been proposed as a candidate method, available
studies have heretofore been unsuccessful at attaining valid
identification of aspiration. Accordingly, valid, reliable tools
for detecting aspiration and other related swallowing impairments
are needed that overcome the variable predictive utility of known
clinical screening protocols and/or reduce the substantial burden
on nursing staff imposed by the implementation of such
protocols.
[0012] Therefore, there remains a need for a method and device for
swallowing impairment detection that overcomes some of the
drawbacks of known techniques, or at least, provides a useful
alternative.
[0013] This background information is provided to reveal
information believed by the applicant to be of possible relevance
to the invention. No admission is necessarily intended, nor should
be construed, that any of the preceding information constitutes
prior art against the invention.
SUMMARY
[0014] An object of the invention is to provide a method and device
for swallowing impairment detection. In accordance with one
embodiment, there is provided a device for use in identifying a
possible swallowing impairment in a candidate during execution of a
swallowing event, the device comprising: an accelerometer to be
positioned in a region of the candidate's throat and configured to
acquire vibrational data representative of the swallowing event; a
nasal airflow monitor to be positioned in a region of the
candidate's nostrils and configured to acquire airflow data
representative of the swallowing event; a processing module
operatively coupled to said accelerometer and nasal airflow monitor
for processing said vibrational data and said airflow data to
extract from each one thereof one or more features representative
of the swallowing event, and classify said vibrational data and
airflow data as indicative of one of normal swallowing and possibly
impaired swallowing based on said extracted features.
[0015] In accordance with another embodiment, there is provided a
computer readable-medium having statements and instructions stored
thereon for implementation by a processor to automatically process
input vibrational data and airflow data representative of a
candidate swallowing event in identifying a possible swallowing
impairment, by: extracting one or more preset features
representative of the swallowing event for each of the vibrational
data and airflow data; comparing said extracted features with
preset classification criteria defined as a function of said preset
features; and outputting, based on said comparing, classification
of said vibrational data and airflow data as indicative of one of
normal swallowing and possibly impaired swallowing.
[0016] In accordance with another embodiment, there is provided a
method for identifying a possible swallowing impairment in a
candidate via execution of one or more preset swallowing events,
comprising: recording vibrational data and airflow data
representative of the one or more swallowing events; extracting one
or more swallowing-event specific features for each of the
vibrational data and airflow data; and classifying said extracted
features as indicative of one of normal swallowing and possibly
impaired swallowing.
[0017] Other aims, objects, advantages and features of the
invention will become more apparent upon reading of the following
non-restrictive description of specific embodiments thereof, given
by way of example only with reference to the accompanying
drawings.
BRIEF DESCRIPTION OF THE FIGURES
[0018] Several embodiments of the present disclosure will be
provided, by way of examples only, with reference to the appended
drawings, wherein:
[0019] FIG. 1 is a schematic diagram of a swallowing impairment
detection device in operation, in accordance with one embodiment of
the invention;
[0020] FIG. 2 is a schematic diagram of a swallowing impairment
detection device, and components thereof, in accordance with one
embodiment of the invention;
[0021] FIG. 3 is a high level multi-sensor data processing flow
diagram for implementation by a swallowing impairment detection
device, in accordance with one embodiment of the invention;
[0022] FIG. 4 is an illustrative multi-sensor data processing flow
diagram, showing optional steps in dashed-line boxes, for
implementation by a swallowing impairment detection device, in
accordance with one embodiment of the invention;
[0023] FIG. 5A is a detailed multi-sensor data processing flow
diagram for implementation by a swallowing impairment detection
device, in accordance with one embodiment of the invention;
[0024] FIG. 5B is a detailed multi-sensor data processing flow
diagram for implementation by a swallowing impairment detection
device in screening for both swallowing safety and efficiency, in
accordance with one embodiment of the invention;
[0025] FIG. 6 is a flow chart of a candidate screening and testing
protocol for implementation using a swallowing impairment screening
device, in accordance with one embodiment of the invention;
[0026] FIGS. 7A, B and C are tables of classification performance
results achieved for depth of airway invasion, bolus clearance from
the valleculae sinuses, and bolus clearance from the pyriform
sinuses, respectively, in accordance with various exemplary
embodiments of the invention;
[0027] FIG. 8 is a table of feature subsets selected in achieving
highest classification performances, as reported in the tables of
FIG. 7.
DETAILED DESCRIPTION
[0028] As introduced above, aspiration, generally understood as the
entry of foreign contents into the upper airway, is a serious
concern for individuals with swallowing difficulty, for example
suffering from dysphagia or other such conditions, and can lead to
pneumonia, for example. Unfortunately, aspiration is not always
readily identified, due to the variable expression of overt
clinical signs, such as cough. Other swallowing impairments, such
as the presence of residues post-swallowing, which is common in
patients suffering from dysphagia, are also of concern, and the
detection thereof is generally required in properly screening for
and/or diagnosing a patient's condition. The herein described
swallowing impairment detection methods and devices provide a
solution to the widespread need for an accurate, non-invasive
instrument to detect impaired swallowing safety and/or efficiency
during screening. As will be elaborated below, such methods and
devices now provide for the greater ability to incorporate
swallowing impairment screening into routine nursing assessments of
patient vital signs, in some embodiments, with minimal
training.
[0029] For instance, the methods and devices described herein
provide various improvements over the state of the art that assist
in the screening for, detection of, monitoring and/or diagnostic of
swallowing impairment(s). It will be appreciated that while
specific embodiments are described herein in the context of
specific applications and implementations, the various features of
the herein described methods, devices, and elements thereof, may be
considered in different contexts to improve different aspects
associated with swallowing impairment screening, detection,
monitoring and/or diagnostics, and that, with different levels of
user interaction, automation and complexity depending on the
application at hand. Namely, while certain implementation protocols
and operation guidelines may be considered herein with respect to
specific examples, alternative or complimentary approaches may also
be considered without departing from the general scope and nature
of the present disclosure. Accordingly, it will be appreciated that
the use herein of the term "impairment detection" is meant be
construed broadly to encompass different levels of impairment
identification, be it in the context of routine screenings or
diagnostics, and that, without departing from the general scope and
nature of the present disclosure.
[0030] As will be discussed in greater detail below, the methods
and devices described herein make use of cervical accelerometry and
airflow monitoring during swallowing for detecting swallowing
impairment(s), for example, in individuals with suspected
dysphagia. In one example, screening for potential aspiration
events in different subjects is implemented via analysis of
cervical vibrations and nasal airflow during thin liquid swallows,
or in executing other types of swallowing events as necessary or
appropriate given a specific screening or diagnostic protocol,
device programming or configuration, and other such considerations.
Features extracted from acquired accelerometry and airflow signals,
can be effectively and automatically classified to allow a user or
operator of the disclosed device/aspirometer to identify individual
swallowing events as one of a normal event or as one representative
of a possible aspiration event. Upon applying certain
screening/diagnostic protocols and/or guidelines, candidates at
risk of aspiration may thus be identified as such using the
disclosed methods and devices considered herein, wherein such
identification may lead to further assessment, diagnostics,
treatment and/or dietary restrictions, as deemed appropriate.
[0031] Similarly, screening for other swallowing impairments, such
as swallowing efficiency impairments generally manifested by the
presence of residues post-swallow, can also or alternatively be
detected by the herein described embodiments of the invention. As
will be appreciated by the skilled artisan, where the below
description refers more specifically to an aspiration detection
method or device (e.g. swallowing safety impairment
detection/screening), a similar implementation may be considered
for the detection or screening of other swallowing impairments,
such as swallowing efficiency impairments, and that, without
departing from the general scope and nature of the present
disclosure.
[0032] For example, in some embodiments, the swallowing impairment
detection device can be configured to provide indication of both
impaired swallowing safety and impaired swallowing efficiency
during swallow screening. Through filtering and processing of
vibrational signals collected with a cervical accelerometer placed
just below the thyroid cartilage, and airflow signals collected
with an airflow monitor (e.g. nasal cannula), one or more
classifiers, as disclosed herein, can be trained to discriminate
between signals associated with penetration-aspiration and those
displaying normal swallowing safety, and/or trained to discriminate
between signals associated with reduced swallowing efficiencies and
those displaying normal swallowing efficiencies.
[0033] As will be described in the below Example, in a study of 24
adult patients with dysphagia undergoing concurrent
videofluoroscopy, a swallowing impairment detection device,
configured in accordance with one embodiment of the invention, was
able to accurately identify impaired swallowing safety and
efficiency from the combined classification of concurrently
acquired cervical accelerometry and nasal airflow data. Namely, a
mean adjusted accuracy of 74.7% was achieved for depth of airway
invasion classifications (i.e. safe vs. unsafe swallows), whereas
mean adjusted accuracies of 83.7% and 84.2% were achieved for bolus
clearance from the valleculae and pyriform sinuses, respectively
(i.e. efficient vs. inefficient swallows). Based on these results,
and those of similar embodiments also reported in the below
Example, the combined classification of cervical accelerometry and
nasal airflow data, in accordance with the various embodiments of
the herein-described methods and devices, was proven effective in
detecting swallowing impairments, thus validating the use of this
multi-sensor fusion in accurately classifying abnormal
swallows.
[0034] With reference to FIG. 1, a system for use in swallowing
impairment detection, generally referred to using the numeral 100,
and in accordance with an illustrative embodiment of the invention,
will now be described. In this example, the system 100 generally
comprises a cervical accelerometer, such as dual-axis accelerometer
102, to be attached in a throat area of a candidate for acquiring
dual axis accelerometry data and/or signals during swallowing (e.g.
see illustrative superior-inferior axis (S-I) accelerometry signal
104 and anterior-posterior axis (A-P) accelerometry signal 106
shown in FIG. 1). A nasal airflow monitor, such as nasal cannula
108, is also provided for acquiring airflow data and/or signals
during swallowing (e.g. see illustrated nasal airflow signal 110 of
FIG. 1).
[0035] In one embodiment, the accelerometer is also fitted with a
pressure sensor or pressure sensitive film configured to
effectively measure a pressure between the accelerometer and the
candidate's throat when installed (e.g. measure a pressure applied
by the accelerometer on the candidate's throat as a function of a
tension in a strap or elasticized band used to secure the
accelerometer in position). Therefore, upon monitoring this
pressure, for example via an indicator on a user interface
associated with the device, a clinician may be better able to
position the accelerometer on each candidate with reproducible
accuracy, thus reducing the likelihood of improper
positioning/installation and thus, reducing data errors or
improving overall performance accuracy of the device. In other
embodiments, the accelerometer may rather be positioned via the
application of dual-sided adhesive tape, or the like.
[0036] In some embodiments, signal quality and/or accelerometer
positioning/placement may be otherwise tested and/or monitored via
a testing protocol, whereby an acquired test signal, for example,
may be checked for consistency with system calibration and/or
preset normal operating conditions. For example, a test signal may
be acquired during a non-swallowing task, such as the candidate
turning its head or saying `ah`, and such test signal compared with
one or more preset test signal ranges and/or characteristics. This
and other similar testing procedures can be applied before, during
and/or after different segments of the swallowing impairment
detection protocol, as will be appreciated by the skilled artisan,
without departing from the general scope and nature of the present
disclosure. Similar nasal airflow positioning and/or calibration
procedures may also be applied, in accordance with different
embodiments of the invention, as will be readily appreciated by the
skilled artisan.
[0037] The accelerometer 102 and airflow monitor 108 are distinctly
or commonly operatively coupled to a swallowing data processing
module or aspirometer 112 configured to process the acquired data
for swallowing impairment detection. Note that the term
"aspirometer" is used generically herein to refer not only to a
device for aspiration detection, but also to similar devices also
or alternatively configured for the detection of other swallowing
impairments, such as swallowing inefficiencies. The processing
module 112 is depicted herein as a distinctly implemented device,
or aspirometer, operatively coupled to accelerometer 102 and
airflow monitor 108 for communication of data thereto, for example,
via one or more data communication media such as wires, cables,
optical fibres, and the like, and/or one or more wireless data
transfer protocols, as would be readily appreciated by one of
ordinary skill in the art. The processing module may, however, in
accordance with another embodiment, be implemented integrally with
the accelerometer/airflow monitor, for example, depending on the
intended practicality of the aspirometer, and/or context within
which it is to be implemented. As will be appreciated by the
skilled artisan, the processing module may further be coupled to,
or operated in conjunction with, an external processing and/or
interfacing device, such as a local or remote computing device or
platform provided for the further processing and/or display of raw
and/or processed data, or again for the interactive display of
system implementation data, protocols and/or screening/diagnostic
tools.
[0038] With reference to FIG. 2, the processing module, depicted
herein generically as a self-contained device or aspirometer 200,
generally comprises a power supply 202, such as a battery or other
know power source, and various input/output port(s) 204 for the
transfer of data, commands, instructions and the like with
interactive and/or peripheral devices and/or components (not
shown), such as for example, a distinctly operated accelerometer
and/or airflow monitor (as shown in FIG. 1), external data
processing module, display or the like. The device 200 further
comprises one or more computer-readable media 208 having stored
thereon statements and instructions, for implementation by one or
more processors 206, in automatically implementing various
computational tasks with respect to, for example, accelerometer and
airflow data acquisition and processing, operation of the device in
accordance with a given or selected impairment detection protocol
(e.g. one or more clinically accepted operation protocols, testing
and/or validation sequences, etc.), or again in the implementation
of one or more impairment detection, monitoring, screening and/or
diagnostic tools implemented on or in conjunction with the device
200. The device 200 may further comprise a user interface 210,
either integral thereto, or distinctly and/or remotely operated
therefrom for the input of data and/or commands (e.g. keyboard,
mouse, scroll pad, touch screen, push-buttons, switches, etc.) by
an operator thereof, and/or for the presentation of raw, processed
and/or screening/diagnostic data with respect to swallowing
impairment detection, monitoring, screening and/or diagnostic (e.g.
graphical user interface such as CRT, LCD, LED screen, touchscreen,
or the like, visual and/or audible signals/alerts/warnings/cues,
numerical displays, etc.)
[0039] As will be appreciated by those of ordinary skill in the
art, additional and/or alternative components operable in
conjunction and/or in parallel with the above-described
illustrative embodiment of device 200 may be considered herein
without departing from the general scope and nature of the present
disclosure. It will further be appreciated that device 200 may
equally be implemented as a distinct and dedicated device, such as
a dedicated home, clinical or bedside impairment detection device,
or again implemented by a multi-purpose device, such as a
multi-purpose clinical or bedside device, or again as an
application operating on a conventional computing device, such as a
laptop or PC, or other personal computing devices such as a PDA,
smartphone, tablet or the like.
[0040] With reference to FIG. 3, an example of a data processing
stream, in accordance with one embodiment of the invention, will
now be described. In general terms, the processing of acquired or
collected cervical accelerometry and nasal airflow data 302
representative of at least one swallowing event may be composed of
two broad steps, namely a feature extraction step 304 applied for
both accelerometry data and airflow data representative of each
swallowing event, and a swallowing event classification step 306
based on the extracted feature(s) of step 304. In applying this
approach to the combined cervical accelerometry data and nasal
airflow data representative of respective swallowing events, such
swallowing events may be effectively classified as one of normal
swallowing events and potentially impaired swallowing events (e.g.
unsafe and/or inefficient), which classification output 308 may
then be utilized in screening/diagnosing the tested candidate in
question and allocating thereto appropriate treatment, further
testing, and/or proposing various dietary or other related
restrictions thereto until further assessment and/or treatment may
be applied.
[0041] As will be appreciated by the person of ordinary skill in
the art upon reference to the following description of specific
examples, wherein greater detail is provided in qualifying
different possibilities for the implementation of these steps in
accordance with different embodiments of the invention, the nature
of these steps substantially remains the same in achieving
swallowing event classification. As will be further appreciated by
the skilled artisan, while the above and following refer to data
processing steps, it will be appreciated that such processes may be
implemented, in accordance with different embodiments of the
invention, by various processing techniques and approaches, which
may for example, be subdivided into distinct, cooperative and/or
interactive processing subroutines, modules or the like, and that,
without departing from the general scope and nature of the present
disclosure. For clarity, the processing steps have and will be
described below as distinct processing steps or modules, however,
it will be appreciated that a swallowing impairment detection
device or computer-readable medium embodied therein comprising
statements and instructions for implementation by a processor
thereof in accomplishing a swallowing impairment detection method,
in accordance with different embodiments of the invention, may be
characterized by cooperative, parallel, successive and/or distinct
processing modules that, in combination, achieve the results
considered herein, without departing from the general scope and
nature of the present disclosure.
[0042] With reference to FIG. 4, and in accordance with an
embodiment of the invention, a further illustrative processing flow
for combined cervical accelerometry and nasal airflow data will be
described, wherein optional steps in this embodiment are shown in
dashed-line boxes. In this particular embodiment, data 402 is
acquired or provided in respect of multiple swallowing events. This
data is then processed via an optional preprocessing module 404
configured to condition the raw data and thus facilitate further
processing thereof. For example, the raw data may be filtered,
denoised and/or processed for signal artifact removal, and that via
common and/or distinct pre-filtering approaches for each of
accelerometry and airflow data, as necessary and/or
appropriate.
[0043] The preprocessed data is then automatically or manually
segmented into distinct swallowing events (step 406). For example,
an automated swallowing event segmentation process may be applied
to the data to segment this data by swallowing event, such as
described in co-pending U.S. Patent Application Publication No.
2010/0160833, and/or such as described in the publication by Lee et
al. titled Swallow segmentation with artificial neural networks and
multi-sensor fusion and published in Medical Engineering &
Physics 31 (2009) 1049-1055, the entire contents of each of which
are incorporated herein by reference. Alternatively, manual
segmentation may be applied, for example, upon visual inspection of
the data (e.g. identification of the start of each swallowing
event, which may be readily and systematically recognized by an
operator of the device). Alternatively, the device and method, in
accordance with one embodiment, may involve segmented data
recordal, as will be described further with reference to the
exemplary protocol depicted in FIG. 6, whereby data is explicitly
recorded for each swallowing event individually. In such
embodiments, it will be appreciated that swallowing event-specific
data may be preprocessed individually, thus effectively applying
the manual signal segmentation step 406 of FIG. 4 during
acquisition of accelerometry and airflow data 402 and prior to
preprocessing step 404. As will be appreciated by the skilled
artisan, these and other such variations may be considered herein
without departing from the general scope and nature of the present
disclosure.
[0044] The event-specific data is then processed by a feature
extraction module 408, and optionally, a feature reduction module
410, allowing for each swallowing event to be classified at step
412 based on these extracted features. As discussed above
generally, such classification thus allows for the determination
and output 414 of which swallowing event represented a normal
swallowing event as compared to a potentially unsafe and/or
inefficient swallowing event.
[0045] With reference to FIG. 5A, a detailed processing stream, in
this example contemplating dual axis accelerometry in combination
with nasal airflow monitoring, will be described, showing therein
specific examples of processing techniques applicable with respect
to some of the general processing modules described above at a
higher level. It will be appreciated that while the following
provides specific examples, such examples are not intended to limit
the general scope of the present disclosure, but are rather
presented solely for the purpose of exemplifying certain techniques
implemented for the purpose of testing and validating the various
embodiments of the invention described herein.
[0046] The process of FIG. 5A is applied to dual axis accelerometry
and nasal airflow data 502 representative of multiple swallowing
events. For example, the data may be acquired, as labeled, along
the anterior-posterior and superior-inferior axes, respectively,
for accelerometry, and via nasal airflow monitoring during
swallowing. Clearly, previously segmented and/or individually
recorded data sets may also be utilized in this context, bypassing
segmentation step 506 described below.
[0047] A data preprocessing step 504 is once again applied to the
accelerometry and airflow data, consisting in this example, of an
inverse filter, which may include various low-pass, band-pass
and/or high-pass filters, followed by signal amplification (e.g. in
the example below, accelerometry data was band-pass filtered 0.1 Hz
to 3 kHz with 10X and 10,000X amplification for accelerometry and
nasal airflow channels, respectively).
[0048] In one embodiment, a denoising subroutine is then applied to
the inverse filtered data, which may consist, in one example, of
processing signal wavelets and iterating to find a minimum mean
square error, for example as described in co-pending application
Ser. No. 12/819,216, the entire contents of which are incorporated
herein by reference. It will be appreciated that various
optimization schemes may be implemented to find such minimum value,
as can alternative denoising subroutines to achieve similar results
in accordance with different embodiments of the invention.
[0049] In one example, the preprocessing module may further
comprise a subroutine for the removal of candidate movement
artifacts from the data, for example, in relation to a candidate's
head movement. In one such example, a splines-based subroutine may
be implemented to achieve satisfactory artifact removal; however,
other techniques may also be applied to achieve similar results.
Other signal artifacts, such as vocalization, blood flow, and the
like, may also be removed from acquired signals as necessary or
applicable, in accordance with different embodiments of the
invention.
[0050] Upon completion of the data preprocessing step 504, which
may involve different levels of complexity depending on the quality
and reliability of acquired data, and other such parameters, the
data is then manually or automatically segmented (step 506) for
event-specific processing, as described above. Again, it will be
appreciated that data segmentation may be implemented prior to
preprocessing, or again, avoided entirely where event-specific data
sets are acquired independently. In the Example described below,
preprocessing steps were implemented both prior to, and after
segmentation. Namely, each segmented signal was denoised by a
5-level discrete wavelet decomposition with the Daubechies-5
wavelet, and to remove low-frequency motion artifacts, each signal
was then passed through a 4th-order highpass Butterworth filter
with a cutoff frequency of 1 Hz. It will be appreciated that
different approaches may applied to the pre-processing of acquired
data in readying such data for further processing to increase an
ultimate performance accuracy of the trained classifier(s),
described below.
[0051] In this embodiment, the event-specific data is processed
through a feature extraction module 508, which consists of
calculating one or more, time, frequency and/or time-frequency
domain features for each data set. In this particular example, the
feature extraction module 508 implements, amongst other possible
routines, a 20-level discrete wavelet decomposition of the
respective signals with the Daubechies-5 wavelet, and calculates
wavelet energies and/or energy ratios thereof to be used as
selected features in subsequent steps. Other features, such as
selected from the time, frequency and/or time-frequency domains
(e.g. mean, variance, center frequency, dispersion ratio, etc.),
may also be considered jointly or distinctly to achieve similar
results. It will be appreciated that different combinations of
extracted features may be considered herein without departing from
the general scope and nature of the present disclosure. For
example, as described below, various feature combinations were
tested to optimize the herein described method and device, each one
providing, to varying degrees, effective differentiation between
healthy and potentially impaired swallowing events. Further, while
some embodiments, may contemplate different extracted features for
each data set (i.e. A-P axis, S-I axis and airflow), it will be
appreciated that the same features may be extracted in each case,
or again, that multiple features may be extracted from each set,
and that, in different combinations.
[0052] Upon feature extraction, an optional feature reduction
module 510 is then implemented to further process the data for
effective classification. For example, the feature reduction module
may be configured to select a subset of the extracted features for
classification, for instance based on the previous analysis of
similar extracted feature sets derived during classifier training
and/or calibration. For example, in one embodiment, the most
prominent features or feature components/levels extracted from the
classifier training data set are retained as most likely to provide
classifiable results when applied to new test data, and are thus
selected to define a reduced feature set for training the
classifier and ultimately enabling classification. For instance, in
the context of wavelet decompositions, or other such signal
decompositions, techniques such as linear discriminant analysis,
principle component analysis or other such optimization techniques
effectively implemented to qualify a quantity and/or quality of
information available from a given decomposition level, may be used
on the training data set to preselect feature components or levels
most likely to provide the highest level of usable information in
classifying newly acquired signals. Such preselected feature
components/levels can then be used to train the classifier for
subsequent classifications. Ultimately, these preselected features
can be used in characterizing the classification criteria for
subsequent classifications.
[0053] Accordingly, where the device has been configured to operate
from a reduced feature set, such as described above, this reduced
feature set will be characterized by a predefined feature subset or
feature reduction criteria that resulted from the previous
implementation of a feature reduction technique on the classifier
training data set. Newly acquired data will thus proceed through
the various pre-processing and segmentation steps described above
(steps 504, 506), the various swallowing events so identified then
processed for feature extraction at step 508 (e.g. full feature
set), and those features corresponding with the preselected subset
retained at step 510 for classification at step 512.
[0054] The features of Table 6 (FIG. 8), described below in the
context of an illustrative embodiment of the invention, provides
different reduced feature subsets shown to provide particularly
effective means for differentiating between healthy and potentially
impaired swallowing events, be it to identify possible airway
invasion (i.e. unsafe swallow and/or aspiration), or post-swallow
residue (e.g. inefficient swallow) for example in the valleculae
and/or pyriform sinuses. Again, while different feature subsets (or
reduced features) are provided in this example for each data set
(i.e. axis- and sensor-specific feature subset) it will be
appreciated that a same subset may be used for each axis and/or for
both vibrational and airflow data sets.
[0055] As validated by the results presented in the below Example,
this approach to feature extraction and reduction was effectively
used to distinguish safe from potentially unsafe swallows, and
efficient from potentially inefficient swallows. Namely, as
evidenced by the below results and validation of the
above-described technique, the extraction of these selected
features from new test data can now be compared to preset
classification criteria established as a function of these same
selected features as previously extracted and reduced from an
adequate training data set, to classify the new test data as
representative of a normal vs. impaired swallow (e.g. safe vs.
unsafe swallows and/or efficient vs. inefficient swallows)
[0056] While the above and below described examples contemplate a
discrete selection of the most prominent features, or components
thereof, other techniques may also readily apply. For example, in
some embodiments, the results of the feature reduction process may
rather be manifested in a weighted series or vector for association
with the extracted feature set in assigning a particular weight or
level of significance to each extracted feature component or level
during the classification process. For example, rather than to use
respective feature subsets such as listed in Table 6, a weighted
sum or the like of extracted features may rather be applied to
reflect the predominance of certain levels or components, while
still accounting for each level, for example.
[0057] As will be appreciated by the skilled artisan, other feature
sets, such as frequency, time and/or time-frequency domain
features, may also be considered to provide similar results.
Similarly, while the above provides one example of selected subsets
of features identified via an applied feature reduction process,
other feature selections based on similar feature reduction
techniques (e.g. genetic algorithms, principal component analysis,
etc.) and/or identified from a different training data set, may
also be considered to provide similar results.
[0058] Upon feature reduction, feature classification is
implemented by classification module 512, which in this embodiment,
implements a discriminant analysis using Mahalanobis and/or
Euclidian distances to compare the extracted features (or
reduced/weighted subset thereof) of acquired swallow-specific data
with pre-set classification criteria so to effectively classify
each data set as representative of a normal swallowing event or a
potentially impaired swallowing event. As will be appreciated by
the skilled artisan, different classification techniques may be
implemented in classifying swallowing event data, which may include
for example, genetic algorithms, principal component analysis,
neural networks, etc.
[0059] In the Example provided below, best results were achieved
via the above-described technique, wherein extracted features were
ultimately evaluated as a function of their effective distance from
a previously classified training data set representative of healthy
and unhealthy swallows.
[0060] In one embodiment, a clinical impairment detection protocol,
for example as described below with reference to FIG. 6, is
implemented on a swallow-by-swallow basis, whereby a
screening/diagnosis with respect to potential aspiration, and/or
other such impairments, is executed for each swallowing event
independently. In such embodiments, swallowing event data
classification is implemented independently for each acquired data
set (signal segmentation is also effectively avoided), whereby
features extracted from this event-specific data set is classified
upon comparison with preset classification criteria established,
for example, on the basis of repeated clinical trials and/or device
calibration implemented via similar data processing techniques.
[0061] For example, in one embodiment, depicted by the dashed-line
boxes of FIG. 5A as an optional training/validation subroutine 516,
a data set representative of multiple swallows is processed as
described above such that each swallow-specific data set ultimately
experiences the preprocessing, feature extraction and feature
reduction modules described above. The training/validation
subroutine 516 is then vigorously tested over a known training data
set so to establish reliable classification criteria against which
subsequent test data sets can be compared for classification. An
exemplary training sequence is described in the Example below,
however, one of ordinary skill in the art will appreciate that
different approaches to classifier training and validation can be
implemented to provide similar results, without departing from the
general scope and nature of the present disclosure.
[0062] Once all events in the training data set have been
classified and validated, output criteria may be generated for
future classification without necessarily applying further
validation to the classification criteria. Alternatively, routine
validation may be implemented to either refine the statistical
significance of classification criteria, or again as a measure to
accommodate specific equipment and/or protocol changes (e.g.
recalibration of specific equipment, for example, upon replacing
accelerometer with same or different accelerometer type/model,
changing operating conditions, new processing modules such as
further preprocessing subroutines, artifact removal, additional
feature extraction/reduction, etc.).
[0063] With reference to FIG. 5B, a similar process as described
above with reference to FIG. 5A is presented. In this process,
steps 502 to 510 are kept substantially unchanged; however, the
process proceeds to distinct classification steps 512A and 512B in
the classification of the acquired event-specific vibrational data
and airflow data as indicative of safe vs. unsafe swallows, and as
indicative of efficient vs. inefficient swallows, respectively. For
example the same feature extraction and reduction techniques may be
commonly applied (for the same or distinct preselected feature
subsets) prior to the application of the distinctive
classifications, which classifications may rely on distinct
classification criteria previously defined as a function of
respective classifier training. For instance, the same
classification technique may be employed, but trained in accordance
with distinct parameters, namely based on a known training data set
(which may be the same for establishing both sets of classifying
criteria) segregated into safe vs. unsafe swallowing events, and
efficient vs. inefficient swallowing events, respectively. In such
embodiments, the device may thus be configured to perform two
distinct classifications in parallel or in sequence, to achieve
greater screening accuracy and complexity. It will be appreciated
that while the above example considers the implementation of a same
feature extraction, reduction and classification technique for both
intended classifications, a distinct technique may otherwise be
applied to each classification problem, without departing from the
general scope and nature of the present disclosure.
[0064] With reference to FIG. 6, an exemplary clinical or bedside
protocol is provided in implementing a testing or screening
sequence for impairment detection via dual-axis accelerometry, with
candidates satisfying the following eligibility criteria: [0065] a)
patient must be alert and awake; [0066] b) patient must be able to
breathe freely on room air (those with tracheostomies or on
supplemental oxygen should proceed directly to a full assessment);
[0067] c) patient should be able to sit upright with minimal
support, and able to hold head upright; [0068] d) patient should be
able to follow simple instructions; and [0069] e) patient's mouth
should be clean and free of debris before proceeding; dentures may,
but do not need to be worn for this test.
[0070] As depicted in FIG. 6, upon identifying a patient as an
eligible candidate (602), the testing or screening sequence
proceeds as follows. At step 604, a dual-axis accelerometer is
positioned on the candidates neck (e.g. by way of a strap,
elasticized band and/or double-sided adhesive tape), for example in
midline, anterior to the cricoid cartilage; a nasal airflow monitor
is also positioned. At step 606, the device is activated (e.g.
device turned on, application running on a portable screening
device activated, and/or application set to initiate new screening
session initiated). At step 608, and generally once the candidate
has been provided with a given quantity of a substance to be
swallowed as a first swallowing event (e.g. a 5-cc cup of water),
recording is started, thus allowing recordal of respective
accelerometric and airflow signals corresponding to the candidate's
first swallowing event (e.g. via a device push button or a virtual
button rendered on a graphical user interface of the device). Upon
completion of the swallowing event, or where the candidate begins
coughing, recording is stopped at step 610 (e.g. upon pushing the
same or a distinct physical/virtual button), and the recorded
signals automatically processed (step 612) for classification as
indicative of a safe vs. unsafe swallow (and/or efficient vs.
inefficient swallow). In one embodiment, the graphical user
interface of the device is configured to output a result at step
614 for the completed swallowing event, which may be noted
manually, or tracked sequentially by the device for each subsequent
event. Exemplary outputs may include, but are not limited to, a
message such as "No aspiration/residue detected" or "Possible
aspiration/residue detected", a colour coded light or indicia to
identify a safe swallowing event (e.g. green), possibly unsafe
swallowing event (e.g. red), or possibly inefficient swallowing
event (e.g. orange), and/or other such display mechanisms. Note
that such results may alternatively be recorded automatically by
the device to render a consolidated/overall report at the end of
the protocol/session, thus further reducing reliability on user
intervention.
[0071] In this exemplary embodiment, the above steps are repeated
for 3 swallowing events, unless coughing is identified during the
first two events, at which point, the session is ended after two
events at step 616 and the candidate automatically referred to
further assessment (e.g. via VFSS) at step 618. Otherwise, overall
results may be output at step 620 (e.g. upon pressing an "end
session button" or again automatically output upon the device
acknowledging at step 622 the completion of the session's
prescribed three swallowing events) and, where results are
indicative that the candidate may be exhibiting a swallowing
impairment (e.g. detection of at least one possibly unsafe or
inefficient swallowing event), the candidate is again referred for
further assessment at step 618.
[0072] It will be appreciated that different embodiments may be
configured to provide different levels of information, consistent
with the classification techniques employed and level of training
implemented in configuring the device. For example, in one
embodiment, the device is configured to output an indication as to
potential swallowing safety impairment (e.g. healthy swallow vs.
possible penetration/aspiration). In another embodiment, the device
may be further configured to also output an indication as to
potential swallowing efficiency impairment (e.g. absence vs.
presence of residue post-swallow). In such embodiments, the device
would effectively process the recorded signal based on a dual
classification process, namely one trained to identify aspiration
risk, and the other to identify swallowing inefficiencies, the
combined results thus providing for a more complete dysphagia
screening and characterization process, for example.
[0073] From the above, it is appreciated that limited training and
intervention is required for implementation of the above protocol
in assessing aspiration and/or swallowing efficiency risks. Namely,
the device considered herein in accordance with different
embodiments of the invention allows for the ready assessment, or
pre-assessment (e.g. screening) of potential aspiration/dysphagia
candidates, without significant operator intervention, contrary to
traditional swallowing impairment detection techniques.
Furthermore, and as validated by the results of the specific
example described below, the reliability of the results output
using this approach, as compared to other approaches, makes for a
greater candidate assessment tool resulting in fewer misdiagnoses
and/or fewer referral of otherwise healthy patients to further and
generally more invasive treatment/testing procedures.
[0074] The following provides an example of a swallowing impairment
detection system, method and device, in accordance with an
embodiment of the invention, validated by the parallel
implementation of videofluoroscopic examinations. It will be
appreciated by the person of ordinary skill in the art that the
following describes an exemplary embodiment of the invention, and
is not intended as a limiting disclosure, but rather merely
illustrative of one of different possible embodiments of the
inventive impairment detection method, system and devices
considered within the context of the present disclosure.
EXAMPLE
[0075] In this example, dual-axis accelerometry and nasal airflow
signals were acquired from 24 (22 males) adult patients with
dysphagia during routine VFSS sessions. All patients had suffered
either stroke or acquired brain injury and underwent VFSS to
investigate their swallowing function. The average age of the
patients was 64.8.+-.18.6 years. Corresponding X-ray videos were
recorded as well. The presiding speech-language pathologists
determined the number of swallows and stimulus types for each
patient, although a standardized approach, beginning with a thin
liquid 40% weight per volume barium suspension and progressing
through nectar and spoon-thick liquids to solid stimuli, was used.
The mean number of swallows per patient was 17.8.+-.8.8.
[0076] Dual-axis accelerometry signals were acquired via a
dual-axis accelerometer (ADXL322, Analog Devices) placed on the
neck just below the thyroid cartilage, with the axes oriented in
the A-P and S-I directions. The sensor was attached to the neck
with a double sided electrode collar (650455, VIASYS Healthcare).
Nasal airflow signals were recorded with a nasal cannula (Pro-Flow
Cannulas Model 1259, Grass Technologies) placed at the nares,
connected to a pressure transducer (PTAFLITE, Grass Technologies),
e.g. as shown in FIG. 1. Each signal channel was sampled at 10 kHz
by a custom LabVIEW application. A pre-amplifier with a bandpass
filter (Model P55, Grass Technologies) was utilized for each
channel, with the cutoff frequencies set at 0.1 Hz and 3 kHz.
Amplification was set at 10 and 10,000 for the two accelerometry
channels and nasal airflow, respectively.
[0077] The signal acquisition hardware was comprised of two
separate components: one for the acquisition of the accelerometry
and nasal airflow signals and the other for videofluoroscopy
recording. The two components were synchronized by resetting the
time-code generator (Time Code Master Model 5010, Evertz
Microsystems), which inserted time stamps on the X-ray videos, when
signal acquisition started immediately prior to the beginning of
the VFSS session. Signal acquisition and video recording were
stopped immediately after the end of the session.
[0078] The X-ray video of each swallow was analyzed and rated by a
speech-language pathologist. First, individual swallows were
located in the videos. Swallow onset was defined as the moment when
the leading edge of the bolus crossed the inferior margin of the
shadow of the ramus of the mandible. The corresponding offset was
defined as the moment when the hyoid bone returned to a resting
position following the passage of material from the pharynx into
the esophagus.
[0079] Each swallow was then rated in terms of the depth of airway
invasion and bolus clearance from the valleculae and pyriform
sinuses. A 4-point scale was utilized for each rating. Tables 1 and
2, below, tabulate the scale descriptions for the depth of airway
invasion and bolus clearance, respectively. Bolus clearance from
the valleculae and pyriform sinuses followed the same scale. In
Table 1, note that levels 1 and 2 correspond to penetration,
whereas level 3 refers to aspiration.
TABLE-US-00001 TABLE 1 4-point depth of airway invasion scale based
on VFSS Level Clinical Description 0 Material does not enter airway
1 Material enters supraglottic space, but does not reach true vocal
folds 2 Material enters airway, and contacts true vocal folds but
does not pass below 3 Material enters airway, and passes below true
vocal folds
TABLE-US-00002 TABLE 2 4-point bolus clearance scale based on VFSS
Level Clinical Description 0 Material does not enter airway 1
Material enters supraglottic space, but does not reach true vocal
folds 2 Material enters airway, and contacts true vocal folds but
does not pass below 3 Material enters airway, and passes below true
vocal folds
[0080] In this example, a binary system was implemented for each
scenario whereby classification of acquired signals was implemented
to distinguish those associated with a level 0 from those
associated with a level 3, for each rating. Clinically, the
automatic discrimination between a level 0 and level 3 rating may
serve as a useful decision support or screening tool. Levels 0 and
3 represented healthy and abnormal swallows, respectively, and a
separate binary classification was considered for each rating
scheme. In the end, there were 265 level-0 and 39 level-3 swallows
with a depth of airway invasion rating, 61 level-0 and 64 level-3
swallows with a bolus clearance from the valleculae rating, and 129
level-0 and 25 level-3 swallows with a bolus clearance from the
pyriform sinuses rating. The total number of swallows was not
constant across the three rating schemes because visual obstruction
(e.g. the shoulder) or image quality issues prevented several
swallows from receiving all three ratings. Hence, the clinical
ratings provided up to three labels for each swallow, a depth of
airway invasion label and two bolus clearance labels (clearance
from the valleculae and pyriform sinuses). These labels served as
the true classification of the swallow.
[0081] All signals were first down-sampled to 1 kHz from a 10 kHz
sampling frequency, which was initially selected to account for the
potential for high frequency content in the signals. Since the
majority of the signal power in cervical accelerometry is expected
to lie below 100 Hz, and since respiration completes only 10 to 12
cycles per minute at rest in adults, this down-sampling was
expected to result in minimal signal information removal, while
greatly reducing computational burden for subsequent analyses.
[0082] Individual swallows were then segmented according to the
swallow onsets and offsets determined from the X-ray videos. The
time stamps on the videos identified the corresponding onsets and
offsets in the signals, and the same onsets and offsets were
applied to all signal channels. Each segmented signal was then
denoised by a 5-level discrete wavelet decomposition with the
Daubechies-5 wavelet. To remove low-frequency motion artifacts,
each signal was then passed through a 4th-order highpass
Butterworth filter with a cutoff frequency of 1 Hz. FIG. 1 shows
sample signals from one particular swallow after all the
pre-processing steps applied in this example. It will be
appreciated that these sample signals portray just one swallow, and
that substantial variability among the signals in terms of length
and visual characteristics is to be expected between swallowing
events.
[0083] For each segmented swallow, a variety of features were
extracted from the pre-processed accelerometry and nasal airflow
signals in the time, frequency, and time-frequency domains. A large
number of features, 444 in total, were first extracted from each
swallow to maximize the possibility of finding discriminatory
features in the subsequent feature selection step.
[0084] Specifically, the mean, variance, skewness, kurtosis,
interquartile range, number of zero-crossings, maximum hyolaryngeal
excursion (estimated via double integration of accelerometry), air
volume (estimated via integration of airflow), normality,
stationarity (via the reverse arrangement test), dispersion ratio,
peak Fast Fourier Transform (FFT) magnitude, frequency at spectral
peak, and 10th order linear prediction coefficients (LPC) were
computed for each signal.
[0085] In addition, wavelet energies extracted from the beginning,
middle, and end of the swallow were also considered using a
20-level discrete wavelet decomposition with the Daubechies-5
wavelet. Each of the approximation and detail signals were divided
into three equal segments, and the average of the squared
coefficients in each segment became a feature. Moreover, all
.sub.3C.sub.2=3 possible ratios between the energies in two
segments for each approximation or detail signal were used as
features. For instance, for the 1st-level detail signal whose ith
data point is denoted as d1.sub.i, i=1, 2, . . . , [n/2], the
energy in the jth segment, E.sub.d1,,j, where j=1, 2, 3, was
computed as follows:
E.sub.d1,j=[n/6].sup.-1.SIGMA..sub.i=[n/6](j-1)+1.sup.[n/6]jd1.sub.i.sup-
.2 (1)
[0086] Again for the 1st-level detail signal, the energy ratio of
the ith to jth segment, ER.sub.d1,i,j was computed as follows:
ER d 1 , i , j = E d 1 , i E d 1 , j , i > j ( 2 )
##EQU00001##
Each feature was also linearly normalized to the range [-1, 1].
[0087] From the large feature pool, the most discriminatory feature
combinations were selected by a genetic algorithm, which can
efficiently identify discriminatory feature subsets from massive
feature spaces without the need for a priori knowledge about the
discriminability of individual features. In this particular
example, the target dimensionality of the reduced feature space,
D.sub.T, was varied such that 3.ltoreq.D.sub.T.ltoreq.12, D.sub.T
.di-elect cons.Z.sup.+. It will be appreciated that different
target feature space dimensionalities may be selected depending on
the quality and efficiency of results desired or required, which
can also vary depending on the types of features selected, signal
quality, and other such parameters as will be readily appreciated
by the skilled artisan. In this particular case, a lower limit of 3
was selected in seeking to achieve a certain minimum degree of
classification efficiency, whereas a higher limit of 12 was
selected to, amongst other reasons, manage computational load. With
these limits and given that 444 features were originally sampled,
the number of possible combinations ranged from
.sub.444C.sub.3.apprxeq.1.45.times.10.sup.7 for 3 features to
.sub.444C.sub.12.apprxeq.1.05.times.10.sup.23 for 12 features. As
it would have been computationally prohibitive to try all possible
combinations, a genetic algorithm was used and deemed particularly
helpful. Namely, as stochastic optimization routines, genetic
algorithms are capable of finding locally optimal solutions in
high-dimensional feature spaces by considering only a finite
tractable subset of solutions at any given time.
[0088] In this example, chromosomes were defined as feature
combinations represented as vectors of length equal to D.sub.T.
Each chromosome was comprised of a sequence of D.sub.T genes, each
ranging in value from 1 to 444 to indicate the selection of one of
the 444 features. In each generation, the two elite chromosomes
that resulted in the minimum error rates were guaranteed to survive
to the next generation. Half of the population in the next
generation, not including the elite children, were created via a
scattered crossover function. In this crossover scheme, each gene
of a newly generated chromosome was randomly selected between the
parental genes at the same genetic locus with equal probabilities.
In addition, uniform mutation was employed, in which each gene of a
chromosome had a probability of 0.01 of being replaced by a random
gene uniformly selected from all possible genes.
[0089] To avoid local minima, random initialization was utilized by
running the genetic algorithm 10 times for each target
dimensionality, while limiting the population size to 2000. The
fitness function of the genetic algorithm employed a linear
discriminant classifier and 10-fold cross-validation for the
estimation of error rate. All swallows from the 24 patients were
conglomerated and randomly partitioned into training and test sets;
this partitioning scheme was repeated throughout this example. The
linear discriminant classifier fitted a separate multivariate
normal density for each class and utilized the Euclidean distance
measure. Due to the prominent imbalance between the two classes in
the data set, the output of the fitness function was set to be the
average of false positive and negative rates, so that the
classifier would not be trained to favor the majority class.
[0090] Each of the 10 random initializations of the genetic
algorithm yielded 10 feature combinations, one for each of the 10
D.sub.T values. For each D.sub.T value, the best among the 10
feature combinations from the random initializations was further
selected after a more rigorous evaluation of classification
performance with resampling. The resampling step served two
objectives: (1) to eliminate the imbalance between the two classes
by generating the same number of samples for each class, and (2) to
resolve the curse of dimensionality by generating a large number of
training samples.
[0091] For each feature and for each class, a univariate density
estimate was constructed with a Gaussian kernel based on the
original training data points. The following is a mathematical
representation of the probability density estimation:
f ^ h ( y ) = 1 Nh i = 1 N G ( y - y i h ) ( 3 ) ##EQU00002##
where N is the number of original training data points in the given
class, h is the bandwidth, and y, is the feature value of the ith
training data point. G is a Gaussian kernel with zero mean and unit
variance.
[0092] The density estimates were then used to resample 5000
examples for the training data of each class. This technique is
equivalent to the smoothed bootstrap, in which resampled examples
are not exact replicas of the original data, unlike the typical
bootstrap methodology. Each feature was resampled independently
from its univariate density estimate; correlations among features
were ignored.
[0093] The validity of resampling from such univariate density
estimates was evaluated by reporting classification performance on
test data that had been excluded from density estimation. Ten-fold
cross-validation and linear discriminant classifiers with the
Euclidean distance were employed. In order to compensate for the
imbalance between the two classes in test data, which had not been
subjected to the resampling, adjusted accuracy was computed as
follows to serve as the criterion to select the best feature
combination for each D.sub.T:
Adjusted accuracy = sensitivity + specificity 2 ##EQU00003##
[0094] At this point, 10 feature combinations, one for each of the
10 DT values, remained for each of the 3 rating schemes. The
efficacy of different classifier models on the 10 final feature
combinations was explored by training several different classifiers
with a larger resampled data set. The same resampling methodology
as described above was repeated to generate 10,000 examples per
class. Also, the same 10-fold cross-validation scheme was utilized
so that density estimation and resampling were solely based on
training data. Four classifier models were deployed: linear
discriminant analysis (LDA), neural network (NN), probabilistic
neural network (PNN), and K-nearest-neighbor (KNN). These choices
covered linear (LDA), non-linear (NN), and nonparametric (PNN and
KNN) classification paradigms.
[0095] As in the previous feature selection steps, LDA classifiers
were trained by fitting separate multivariate Gaussian densities
for the two classes. Two variants were trained: one with the
Euclidean and the other with the Mahalanobis distance measure.
[0096] Feed-forward NN classifiers with one hidden layer were
trained with three different numbers of hidden units (HUs): 10, 20,
and 30. Hyperbolic tangent sigmoids were the activation functions
in both the hidden and output units. Each training data set was
randomly split 80-20 for training and validation; early stopping
based on validation data ensured regularization. The networks with
30 HUs and 12 features (inputs) were the largest networks and
possessed 421 free parameters (360 input weights+30 hidden
biases+30 hidden weights+1 output bias). To avoid overfitting, the
number of free parameters should generally be limited to roughly
10% of the number of training samples. In this example, the number
of free parameters was less than or equal to 421 while the number
of training examples was 20,000 (10,000 from each class).
Therefore, accompanied by the early stopping during training,
overfitting was mitigated.
[0097] PNN classifiers were trained as two-layer networks. The
first layer had radial basis neurons with a spread value of 0.1,
which was 5% of the normalized feature value range [-1,1]. The
neurons in the second layer employed the competitive transfer
function, which yielded the output vector, Y=[y.sub.1, y.sub.2, . .
. ], given an input vector, X=[x.sub.1, x.sub.2, . . . ], according
to the following:
y i = { 0 , if i .noteq. arg max j x j 1 , if i = arg max j x j
##EQU00004##
[0098] KNN classifiers were evaluated with three K values: 11, 21,
and 31.
[0099] Classification performance on test data was reported in
terms of sensitivity, specificity, and adjusted accuracy. For each
classifier, the best result across different D.sub.T was selected
with respect to mean adjusted accuracy.
[0100] Tables 3 to 5 (FIGS. 7A to C) tabulate the best
classification results of each classifier model for the depth of
airway invasion, bolus clearance from the valleculae, and bolus
clearance from the pyriform sinuses, respectively. The
corresponding feature space dimensionality is also reported. All
LDA and NN classifiers for bolus clearance ratings (Tables 4 and 5)
show mean adjusted accuracies greater than 80%, whereas mean
adjusted accuracies for depth of airway invasion ratings (Table 3)
were found as high as approximately 75%. The Euclidean LDA and
Mahalanobis LDA classifiers are associated with the best mean
adjusted accuracy for depth of airway invasion and both bolus
clearance ratings, respectively. In particular, the mean adjusted
accuracy of 84.2% by the Mahalanobis LDA for bolus clearance from
the pyriform sinuses is the highest of all rating schemes.
[0101] LDA and NN classifiers were found by Wilcoxon rank sum tests
to significantly outperform PNN and KNN classifiers for bolus
clearance from the valleculae (p=0.0159) and for bolus clearance
from the pyriform sinuses (p=0.0159), at a significance level
of=0.05. Also, NN classifiers did not result in significant
improvement with respect to mean adjusted accuracy over LDA
classifiers for the depth of airway invasion (p=1) and bolus
clearance from the valleculae (p=0.4) and pyriform sinuses (p=0.4)
by Wilcoxon rank sum tests, with=0.05.
[0102] Table 6 (FIG. 8) lists the feature combinations that
resulted in the best mean adjusted accuracies for each of the 3
rating schemes. The first, second, and third columns correspond to
the classification performances shown in the first row of Table 3,
the second row of Table 4, and the second row of Table 5,
respectively. The majority of the selected features are from
wavelet analysis.
[0103] For all three rating schemes, the outcome of the feature
selection steps included features from all three signal channels,
demonstrating the improvements brought forth by the herein proposed
approach to swallowing impairment detection via multi-sensor
classification, whereby information made available via cervical
accelerometry can be enhanced by information hereby shown available
in nasal airflow data.
[0104] In this particular example, most of the features in Table 6
stemmed from wavelet analysis. In particular, two levels of wavelet
decomposition were repeatedly selected across all three ratings:
3rd level A-P accelerometry and 7th level S-I accelerometry,
corresponding to approximate frequency bands of 62.5-125 Hz and
3.9-7.8 Hz, respectively. It will be appreciated that while these
particular wavelet features were deemed most useful in the present
example, other features and feature subsets may be readily applied
and relied upon to achieve similar results, and that, without
departing from the general scope and nature of the present
disclosure.
[0105] While certain results have been demonstrated above to
provide useful results in classifying data related to swallowing
activity, it will be appreciated that other techniques or feature
selection may yield similar results. Namely, since genetic
algorithms are susceptible to yield results at local minima,
repeated random initialization, as contemplated above, can allow
for greater optimization, and thus, further attempts at feature
selection and classifier training may provide alternative
solutions, which, as will be appreciated by the skilled artisan,
should be construed to fall within the scope of the present
disclosure.
[0106] The current data demonstrates that the combination of
cervical accelerometry and nasal airflow can be useful in
discriminating swallows with poor hyolaryngeal excursion and
associated pharyngeal residues from healthy swallows in individuals
with dysphagia. A device and method as described herein thus
provides a valuable contribution to the clinical swallowing
assessment toolkit, because the presence of residues is something
that cannot be determined with confidence without instrumental
confirmation. Furthermore, because reductions in hyolaryngeal
excursion are known to be common in those with dysphagia, they
commonly constitute a major focus in rehabilitative intervention.
The current analysis thus also supports the use of a combined
accelerometry and airflow data classifier in monitoring progress
and improvement in hyolaryngeal excursion across treatment.
[0107] The above also demonstrates the usability of a combined
classifier for cervical accelerometry and nasal airflow data in
detecting the occurrence of aspiration in swallowing.
[0108] This example shows that cervical accelerometry, in
combination with nasal airflow, as discussed and considered in
accordance with different embodiments of the invention, provides a
reasonable alternative to known techniques in providing a
substantially noninvasive technique for accurately detecting
swallowing impairments, as evidenced by the results of the
above-described Example.
[0109] While the present disclosure describes various exemplary
embodiments, the disclosure is not so limited. To the contrary, the
disclosure is intended to cover various modifications and
equivalent arrangements included within the spirit and scope of the
appended claims. The scope of the following claims is to be
accorded the broadest interpretation so as to encompass all such
modifications and equivalent structures and functions.
* * * * *