U.S. patent application number 13/590070 was filed with the patent office on 2012-12-13 for method and system of segmentation and time duration analysis of dual axis accelerometry signals.
Invention is credited to TOM CHAU, ERVIN SEJDIC.
Application Number | 20120316470 13/590070 |
Document ID | / |
Family ID | 42267148 |
Filed Date | 2012-12-13 |
United States Patent
Application |
20120316470 |
Kind Code |
A1 |
CHAU; TOM ; et al. |
December 13, 2012 |
METHOD AND SYSTEM OF SEGMENTATION AND TIME DURATION ANALYSIS OF
DUAL AXIS ACCELEROMETRY SIGNALS
Abstract
The proposed invention is a method and system for the
segmentation of dual-axis accelerometry signals for the purpose of
identifying problematic swallowing events. The method and system
employ a sensor, a data collection means including an algorithm for
analysis of the data. The proposed invention considers the
stochastic properties of swallowing signals in both directions, A-P
and S-I to extract events associated with swallowing. A
segmentation algorithm may be applied to the signals to establish
the time duration of swallows and swallows may be classified with
respect to gender, body mass index, age or types of swallow.
Inventors: |
CHAU; TOM; (TORONTO, CA)
; SEJDIC; ERVIN; (TORONTO, CA) |
Family ID: |
42267148 |
Appl. No.: |
13/590070 |
Filed: |
August 20, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12606797 |
Oct 27, 2009 |
8267875 |
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13590070 |
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Current U.S.
Class: |
600/593 |
Current CPC
Class: |
A61B 5/1107 20130101;
A61B 5/7282 20130101; A61B 5/4205 20130101; A61B 5/7264 20130101;
A61B 5/6822 20130101; A61B 2562/0219 20130101 |
Class at
Publication: |
600/593 |
International
Class: |
A61B 5/11 20060101
A61B005/11 |
Claims
1. A computer software program executable by a computer for
segmentation and analysis of dual-axis accelerometry signals for
the indication of problematic swallowing events comprising the
following steps: a. Receiving accelerometry data from an
accelerometry sensor, said data comprising a series of vibration
events having dual-axis acceleration in an A-P direction and a S-I
direction and representing swallowing and non-swallowing segments;
b. Executing a sequential segmentation algorithm; c. Forming a
first indicator function for a non-swallowing segment in said A-P
direction; d. Forming a second indicator function for a swallowing
segment in the A-P direction; e. Forming a first indicator function
for a non-swallowing segment in the S-I direction; f. Forming a
second indicator function for a swallowing segment in the S-I
direction; g. Applying fuzzy c-means optimization for determining
the time boundaries for each of said swallowing and non-swallowing
segments; h. Multiplying said first function in the A-P direction
by said first indicator function in the S-I direction for noise
reduction; and, i. Multiply said second indicator function in the
A-P direction by said second indicator function in the S-I
direction for noise reduction; j. Wherein steps h and I form a
binary dual-axis indicator function for swallowing and
non-swallowing segments.
2. The software program of claim 1 further comprising the step of
comparing the swallowing events.
3. The software program of claim 2 further incorporating data
representing a patient's anthropometric and demographic
characteristics and classifying the swallowing segments according
to said characteristics.
Description
CROSS REFERENCES TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 61/109,223 filed in the USPTO on Oct. 29,
2008. This application is a divisional of U.S. patent application
Ser. No. 12/606,797 filed Oct. 27, 2009.
FIELD OF INVENTION
[0002] This invention relates in general to the field of dual-axis
swallowing accelerometry signals analysis and more specifically to
a segmentation algorithm for performing such analysis.
BACKGROUND OF THE INVENTION
[0003] Dysphagia (swallowing difficulty) is a serious and
debilitation condition that often accompanies, stroke, acquired
brain injury and neurodegenerative illnesses. Individuals with
dysphagia are prone to aspiration, which directly increases the
risk of serious respiratory consequences, such as pneumonia.
Aspiration can be defined generally as the entry of foreign
material into the airway. Such foreign materials may be of many
types, for example, such as foods, liquids, vomit, saliva,
secretions from the mouth, or other materials.
[0004] The measurement of neck vibrations associated with
deglutition is known as swallowing accelerometry, a potentially
informative adjunct, to bedside screening for dysphagia.
Accelerometric measurements are minimally invasive, requiring only
the superficial attachment of a sensor anterior to the thyroid
notch.
[0005] Recent research has forced upon exploiting this vibration
signal for dysphagia screening. For example, combining
accelerometry and swallowing pressure, Suryanarayanan et al.
developed a hand-crafted fuzzy rule-base to classify sixteen
patients with dysphagia according to aspiration risk. Additionally,
from the physiological perspective, Reddy et al. attributed the
accelerometric signal to the extent of laryngeal elevation during
swallowing, thus arguing that accelerometry would be of diagnostic
value. Furthermore, based on this premise, Das et al. proposed a
hybrid fuzzy logic committee on neural networks trained to
accurately distinguish between swallows from twelve healthy
subjects and sixteen with dysphagia.
[0006] Moreover, studies in this area have provided further
information. In a paediatric study involving children with
dysphagia secondary to cerebral palsy, swallow accelerometry
signals were found to be largely nonstationary, while an off-line
radial basis classifier using two time-domain features
differentiated between manually segmented aspiration events and
safe swallows with 80% sensitivity and specificity.
[0007] Previous studies have only investigated a small number of
swallows and hence the data collected was conducive to manual
segmentation by a human analyst. Segmentation algorithms facilitate
segmentation of larger collection of data. This is necessary as
larger volumes of accelerometry data necessitate an automatic
method to mitigate human error due to fatigue or oversight and to
ensure consistent segmentation criteria. Such algorithms have been
developed in many fields, e.g. heart sounds analysis,
electroencephalogram signals analysis, knee joint
vibroarthrographic signals analysis and in the analysis of urine
magnetomyogram contractions during pregnancy, to name a few. In
particular, several successful methods rely on multiple channels of
information to enhance segmentation.
[0008] Wang and Willett have proposed a very simple algorithm that
determines the number of segments automatically. This algorithm is
useful but encounters a number of problems when utilized to analyze
swallowing accelerometry data. Specifically, the Wang and Willett
algorithm is prone to overestimating the number of segments of
nonstationary variance, which is an element of swallowing
accelerometry signals.
[0009] US Patent Application No. 2005/0283096 presents another
example of prior art in the area of study. The patent discloses an
apparatus and method for detecting swallowing activity. The method
and apparatus disclosed in this patent involve the generation of
electrical signals by an accelerometer positioned on the throat of
the patient and the receipt and analysis of those signals at a
computing device. Gamma distribution is applied to estimate the
spread and location parameters within the signals. Through the
method and apparatus the type of swallowing activity undertaken may
consequently be classified.
SUMMARY OF THE INVENTION
[0010] In one aspect, the present disclosure relates to a method of
segmentation of dual-axis accelerometry signals, comprising: (a)
generating dual-axis swallowing accelerometry signals using a
sensor; (b) transferring the dual-axis swallowing accelerometry
signals to a data collection means; and (c) analysing the dual-axis
swallowing accelerometry signals using a segmentation algorithm
applied by the data collection means; wherein the algorithm
facilitates and identification of class of swallowing.
[0011] In another aspect, the present disclosure relates to a
system of identifying dual-axis accelerometry signals, comprising:
(a) a dual-axis sensor attached to the subject to generate signals;
(b) a data collection means capable of receiving, storing and
analysing swallowing data; and (c) a segmentation algorithm capable
of analysing the swallowing data; wherein the signals generated by
the dual-axis sensor are transferred to the data collection means
as swallowing data and analysed by the segmentation algorithm; and
wherein the segmentation algorithm facilitates the identical of
classes of swallowing.
[0012] In this respect, before explaining at least one embodiment
of the invention in detail, it is to be understood that the
invention is not limited in its application to the details of
construction and to the arrangements of the components set forth in
the following description or illustrated in the drawings. The
invention is capable of other embodiments and of being practiced
and carried out in various ways. Also, it is to be understood that
the phraseology and terminology employed herein are for the purpose
of description and should not be regarded as limiting.
BRIEF DESCRIPTION OF DRAWINGS
[0013] The invention will be better understood and objects of the
invention will become apparent when consideration is given to the
following detailed description thereof. Such description makes
reference to the annexed drawings wherein:
[0014] FIG. 1 is a diagram showing the axes of acceleration in the
anterior-posterior and superior-inferior directions.
[0015] FIG. 2(a) is shows a sample swallowing accelerometry signal
(top) and a binary function (bottom) indicating the occurrence of
swallows as pinpointed by an SLP.
[0016] FIG. 2(b) shows a sample swallowing accelerometry signal
(top) and a binary function (bottom) indicating the occurrence of
correctly segmented swallows where the algorithm (pulse with dashed
line) slightly overestimates the swallow duration extracted by the
SLP (pulse with solid line).
[0017] FIG. 2(c) shows a sample swallowing accelerometry signal
(top) and a binary function (bottom) indicating the occurrence of
correctly segmented swallows where the algorithm (pulse with dash
line) slightly underestimates the swallowing duration extracted by
the SLP (pulse with solid line).
[0018] FIG. 2(d) shows a sample swallowing accelerometry signal
(top) and the binary function (bottom) indicating the occurrence of
correctly segmented swallows where the algorithm (pulse with dash
line) estimates the swallow duration extracted by the SLP (pulse
with solid line)
[0019] FIG. 2(e) sample shows swallowing accelerometry signal (top)
and binary function (bottom) indicating the occurrence of an
incorrectly segmented swallow.
[0020] FIG. 3(a) is a segmentation of test signals that show a
realization of the simulated signal.
[0021] FIG. 3(b) is a segmentation of test signals that show the
actual indicator sequence (dash line) and the indicator sequence
produced by the algorithm.
[0022] FIG. 4 shows a sample of wet chin tuck swallowing vibrations
in A-P and S-I directions along with the indicator sequence
obtained by the proposed algorithm.
[0023] FIG. 5 is a table that shows that vibrations caused by head
movement is some cases can overwhelm the vibrations of interest in
the A-P direction, encumbering detection by the proposed
algorithm.
[0024] FIG. 6 is a table that shows the duration of swallowing
segments grouped by gender wherein an asterisk denotes a
statistically significant gender difference.
[0025] FIG. 7 is a table that shows the duration of swallowing
signals grouped by BMI wherein an asterisk indicates significant
dependence of duration on BMI (p=0.05).
[0026] FIG. 8 is a table that shows the duration of swallowing
signals grouped by age wherein and asterisk indicates significant
dependence of duration on age (p=0.05)
[0027] FIG. 9 is a sample realization of synthetic signal.
[0028] In the drawings, embodiments of the invention are
illustrated by way of example. It is to be expressly understood
that the description and drawings are only for the purpose of
illustration and as an aid to understanding, and are not intended
as a definition of the limits of the invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0029] The proposed intention is a method and system of the
segmentation of dual-axis accelerometry signals for the indication
of problematic swallowing events. For example, such as dysphagia or
aspiration. Swallowing events can be defined for the purpose of
specific embodiments of the invention. The method and system employ
a sensor and a data collection means, as well as an algorithm for
analysis of the accelerometry data. The algorithm of the proposed
invention may considers the stochastic properties of swallowing
signals in two directions, namely anterior-posterior (A-P) and
superior-inferior (S-I). The inclusion of swallowing signals in
both directions may allow the algorithm to extract events
associated with swallowing from the accelerometry data.
Additionally, a segmentation algorithm may be applied to identify
dual-axis swallowing accelerometry signals to establish the time
duration of swallows.
[0030] In one embodiment of the present invention, a sequential
segmentation algorithm may be applied to dual-axis swallowing
accelerometry signals. Such signals may be collected through
non-invasive means thus allowing for a non-invasive diagnosis of
swallowing difficulties. The algorithm may be based on a piecewise
fuzzy partitioning of the signal and is well-suited to long signals
with nonstationary variance. In embodiments of the present
invention dual-axis swallowing accelerometry signals from multiple
swallowing tasks may be compared with known attributes of healthy
swallows in known swallowing location. In this manner swallowing
signals may be classified.
[0031] The construction of dual-axis accelerometry applied in the
present invention involves collecting data from two-dimensional
movement of the hyoid and the larynx during swallowing. Dual-axis
accelerometry thus collects data from the hyolaryngeal complex that
occurs in both anterior-posterior and superior-inferior directions
during swallowing. The proposed segmentation algorithm of the
present invention may involve sequential fuzzy partitioning of the
signal and can be well-suited for long signals with nonstationary
variance.
[0032] The method of the present invention is designed to overcome
problems existing in the prior art. Previous accelerometric signal
segmentation algorithms experience several hurdles. For example,
known algorithms are unable to accurately analyze long signals.
Specifically, the present invention offers a segmentation algorithm
capable of application to long and noisy data sets. These
attributes cause the present invention to be appropriate for
assessments of dual-axis swallowing accelerometry, as the signals
that are produced by swallowing may be of variant durations and are
likely to be accompanied by noise. Furthermore, the method of
present invention overcomes the hurtle of segmenting signals of
nonstationary variance. Known methods and systems of the prior art
are prone to overestimate number of segments for nonstationary
variance signals and to experience problems with threshold tuning.
The present invention overcomes each of these prior art
problems.
[0033] Furthermore, generally a systematic analysis of swallowing
accelerometry signals necessities the demarcation of individual
swallows within an extended recorded of vibrations collected from
the neck. Larger volumes of accelerometry data necessitate an
automatic method, in order to mitigate human error due to fatigue
or oversight and to ensure consistent segmentation criteria. In
application to swallow accelerometry data the present invention may
exploit both A-P and S-I vibrations for this purpose.
[0034] Additionally, the present invention offers a method and
system whereby swallows may be classified in greater detail than is
possible in accordance with prior art. For example, swallows may be
assessed through the method and the proposed invention with respect
to gender, body mass index and age. A variety of types of swallows
may further be assessed through the method of the proposed
invention, for example, such as, saliva swallows, water swallows in
a neutral position and water swallows in a chin tuck position.
These types of swallows are typically considered during a manual
swallowing assessment.
[0035] Carry out the time duration analysis with respect to
factors, including gender, body mass index and age, can provide a
benefit as demographic and anthropometric variables may influence
the duration of segmented signals. For example, males may exhibit
longer swallows than females participants (p=0.05). Additionally,
older persons and persons with higher body mass indices may exhibit
swallows with significantly longer (p=0.05) duration than younger
persons and those with lower body mass indices, respectively. The
method of the present invention involves an algorithm capable of
including such factors in its analysis. The algorithm of the
present invention may extract individual swallows with a high
accuracy rate, for example, such as over 90% accuracy.
[0036] Thus, the present invention provides several benefits over
the prior art. Specifically, the algorithm of the present invention
is applicable to long data sets. It is furthermore, applicable to
data sets affected by noise and nonstationary variance signal data.
Additionally, the method of the present invention, that involves
the algorithm, permits analysis of swallowing accelerometry
data.
[0037] One embodiment of the present invention includes an
algorithm that functions so as to approximate locations of onsets
and offsets of vibration signals over time durations, such as the
vibrations of swallows. In this embodiment there may be no reason
to determine the exact values of onset and offsets of new segments.
Moreover, through analysis of the signals large changes in
variances may be detected. In the course of this understanding the
algorithms may form relationships between variables in a piecewise
and stochastic manner. This may be necessary because data sets are
long and the signals may be buried in noise. The algorithm may also
undertake approximations regarding dual-axis swallowing
accelerometry signals, for example, such as to determine maximum
likelihood estimates for the mixture separation. Approximations can
cut down on computational costs and allow the algorithm to function
more efficiently. Using fuzzy c-means optimization of the algorithm
may be able to determine time boundaries of segments, which can
allow for the identification of swallowing vibrations in A-P and
S-I directions. Once identified, indicator functions from both
directions may be multiplied to obtain an estimate of locations and
durations of signals. This step offers an advantage as noise along
one of the axes can lead to an incorrect estimate of swallowing
multiplicity in the corresponding signal.
[0038] Another embodiment of system of the present invention may
incorporate data regarding the height, weight, body fat percentage,
gender and mandibular jaw length, or other details from a person. A
dual-axis accelerometer sensor may be attached to the neck of a
person, anterior to the cricoid cartilage. Once the sensor is
attached the person may perform a variety of swallows (e.g. water
swallows with head in neutral position, or saliva swallows, etc.).
Swallowing accelerometry measurement data may be collected from the
sensor by a data collection means. A band-pass filtration of the
data may be undertaken and the output may be stored for immediate
or later analysis. The analysis may involve an application of the
algorithm of the present inventions to the signal data to produce
segmentation that indicates locations and durations of swallows.
The analysis may further include a review of the signal results for
average consistency with gender and other elements relevant to
person.
[0039] The method of the present invention builds upon prior art
segmentation algorithms. For example, prior art segmentation
methods often consider segments of different stochastic behaviour
such that a realization of a process given by N points
{x.sub.i|1.ltoreq.i.ltoreq.N} can be composed of K segments with
K-1 transition times .tau.={t.sub.1, t.sub.2, . . . , t.sub.K-1}
where t.sub.k.epsilon..sup.+. Furthermore, the data within the
k.sup.th segment can be assumed to follow an independent and
identically distributed Gaussian distribution with variance
.sigma..sub.k.sup.2. Hence, the probability density function (PDF)
for data within the k.sup.th segment would be given by:
ln p ( x t k - 1 , , x t k - 1 .sigma. k 2 ) = - t k - t k - 1 2 ln
( 2 .pi..sigma. k 2 ) - i = t k - 1 t k - 1 x i 2 .sigma. k 2 ( 1 )
##EQU00001##
[0040] By writing .theta.={.sigma..sub.1.sup.2,
.sigma..sub.2.sup.2, . . . , .sigma..sub.k.sup.2}, which indicates
the vector of variance for all K segments, and assuming that these
segments are statistically independent, the PDF of the data set {x}
can be written as
p(x|.tau., .theta., K)=.PI..sub.k=1.sup.Kp(x.sub.t.sub.k-1, . . . ,
x.sub.t.sub.k.sub.-1|.sigma..sub.k.sup.2) (2)
where by definition t.sub.0=0 and t.sub.k-1=N. Then, the
segmentation problem demands a joint estimation of .tau.,
.theta.and K. The determined values would be represent the best fit
of the data x to (2). Different solutions to this segmentation
problem have been proposed in the literature over the years.
However, computational costs associated with the proposed solutions
are very high.
[0041] A second prior art segmentation algorithm is that of Wang
and Willet, which is very simple algorithm, that determines the
number of segments automatically and avoids threshold tuning. Wang
and Willet's algorithm begins with an initial assumption that the
length of any segment is bounded below by L.sub.min and above by
L.sub.max.
L.sub.min.ltoreq.t.sub.k-t.sub.k-1.ltoreq.L.sub.max (3)
[0042] This assumption mandates that there is at most one change
during any interval of length L.sub.min. In other words, for
{x.sub.i|t.sub.k-1.ltoreq.i.ltoreq.t.sub.k+L.sub.min-1} only two
situations are possible and they are given by:
x.sub.i.about.N(0, .sigma..sub.k.sup.2) for
t.sub.k-1.ltoreq.i.ltoreq.t.sub.k+L.sub.min-1 Hypothesis 1
.E-backward.l.sub.o.epsilon.[L.sub.min,L.sub.max] such that
x.sub.i.about.N(0,.sigma..sub.k.sup.2) for
t.sub.k-1.ltoreq.i.ltoreq.l.sub.o and
x.sub.i.about.N(0,.sigma..sub.k+1.sup.2) for
l.sub.o+1.ltoreq.i.ltoreq.t.sub.k+L.sub.min-1 Hypothesis 2
where .sigma..sub.k.sup.2 and .sigma..sub.k+1.sup.2 are
distinguishable. In other words, either segment is homo- or
heteroscedastic. The value of l.sub.o, is estimated through the
following relation:
l ^ o = arg max l o .di-elect cons. [ L min , L max ] { - t o - t k
- 1 2 ln ( 2 .pi. .sigma. ^ k 2 ) - i = t k - 1 l o x i 2 2 .sigma.
^ k 2 - t k + L min - 1 - ( l o + 1 ) 2 ln ( 2 .pi. .sigma. ^ k + 1
2 ) - i = l o + 1 t k + L min - 1 x i 2 2 .sigma. k + 1 2 } ( 4 )
where .sigma. ^ k 2 = 1 l o - t k - 1 - 1 i = t k - 1 l o x i 2 ( 5
) .sigma. ^ k + 1 2 = 1 L min i = l o + 1 l o + L min x i 2 ( 6 )
##EQU00002##
[0043] The data contained in the segment given
.chi.={x.sub.i|t.sub.k-1.ltoreq.i.ltoreq.{circumflex over
(l)}.sub.o+L.sub.min-1} satisfies either case 1 or case 2. In order
to determine which hypothesis best describes the segment .chi., the
minimum description length (MDL) principle is employed as
follows:
c ^ = arg max c .di-elect cons. [ 1.2 ] MDL ( c ; .chi. ) = arg max
c .di-elect cons. [ 1.2 ] I c ( N x ) - 2 c - 1 2 ln ( N .chi. ) (
7 ) ##EQU00003##
where N.sub..chi. is the length of .chi. and
I c ( N .chi. ) = max { T 1 , T 2 , , T l - 1 } .di-elect cons. [ L
min , L max ] T 0 = 1 , T l = N .chi. i = 1 c - T i - T i - 1 2 (
ln ( 2 .pi. .sigma. ^ i 2 ) + 1 ) ( 8 ) ##EQU00004##
{circumflex over (.sigma.)}.sub.i.sup.2 being the maximum
likelihood (ML) estimated of variance for time interval
T.epsilon.[T.sub.i-1, T.sub.i].
[0044] Based on the value produced by (7), a decision is reached
for the given segment. This procedure continues until the entire
signal is analyzed. Wang and Willet also proposed a post-hoc
refinement stage to improve the accuracy of segmentation.
[0045] For simulated Gaussian time series with zero means and
piecewise constant variance and lengths up to six thousand points,
the algorithm reportedly performs accurately, with computation
times comparable to, if not shorter than those of competing
algorithms. However, for the swallowing records, which are of
considerable length (>>10.sup.4 points), the approximately
linear complexity of the algorithm results in a marked increase in
the computational time. This computational cost is further
heightened when considering the two realizations of the same
process (i.e., dual-axis swallowing accelerometry).
[0046] Swallowing accelerometry signals often possess nonstationary
variance and hence the present algorithm may "overestimate" the
number of segments. For example, a slight change in variance within
the boundaries of a given segment would cause the algorithm to
detect a transition point, when in fact the segment may be a
single, cohesive swallowing event from the physiological point of
view. For example, in the case of a synthetic signal as shown in
FIG. 9, Wang and Willet's algorithm will correctly detect all the
transition points between the baseline and the activity regions. It
will also detect changes change in variances which might occur
during an activity region. If the third activity region (14) is
selected, Wang and Willett's algorithm also detects the changes
denoted by the arrows (16). While mathematically this may be
correct, from a physiological point of view it is not. The present
invention, therefore, may detect distinct physiological events
(i.e., swallows vs. non-swallowing activity), rather than small
fluctuations in variance. Embodiments of the present invention
address the aforementioned challenges of lengthy dual-axis
swallowing accelerometry signals.
[0047] The computational bottleneck of Wang and Willett's algorithm
lies in the optimization procedures to estimate the transition
point l.sub.o and the segment class, c. The present invention
simplifies the optimization procedures and thereby reduces the
computational load. First, exact locations of the onsets and the
offsets of swallow are unknown and can only be approximately
determined. Hence, forcing the algorithm to determine the optimal
value for l.sub.o (i.e., the exact locations of onsets and offsets)
is unnecessary in the swallowing application and is not undertaken
in the present invention. Secondly, the goal of the present
invention regarding segmentation is not to detect small changes in
variances, but rather large changes. In light of the above, the
present invention involves two modified hypotheses for sequential
segmentation, namely,
.phi..sub.i.about.g.sub.1(.phi.|.theta..sub.1) for
t.sub.k-1.ltoreq.i.ltoreq.t.sub.k-1 Hypothesis 1
.phi..sub.i.about.g.sub.2(.phi.|.theta..sub.2) for
t.sub.k-1.ltoreq.i.ltoreq.t.sub.k-1 Hypothesis 2
where .phi. is a variable related to x;
g.sub.1(.phi.|.theta..sub.1) and g.sub.2(.phi.|.theta..sub.2) are
conditional probability density functions and .theta..sub.1 and
.theta..sub.2 are parameter vectors. The PDF
g.sub.1(.phi.|.theta..sub.1) represents the absence of swallowing
activities, while g.sub.2(.phi.|.theta..sub.2) models the presence
of swallowing activity.
[0048] Due to the fact that data sets are very long and the
swallowing signals are buried in the noise, the relationship
between .phi. and x is to be formed in a piecewise and stochastic
manner. To follow Wang and Willett's algorithm, it is assumed that
.phi. is a piecewise constant estimate of the variance of x. That
is, choose L.epsilon..sup.+ satisfying relation (3) and M>L,
M.epsilon..sup.+. Furthermore, it is assumed that L<M in order
to have M-L overlap in sequential procedure. To relate the entire
signal x to the variable .phi., the following steps are proposed:
[0049] 1. Initialize v.sub.k=1 and set
y={x.sub.i|v.sub.k.ltoreq.i.ltoreq.v.sub.k+M-1} [0050] 2. Estimate
the sample means of y:
[0050] .mu. ^ y = E { y } .apprxeq. 1 M i = 1 M y i ( 9 )
##EQU00005## [0051] 3. Estimate the sample standard deviation of y
in a maximum likelihood (ML) sense:
[0051] .sigma. ^ y = 1 M i = 1 M ( y i - .mu. ^ y ) 2 ( 10 )
##EQU00006## [0052] 4. Set the variable .phi. as follows:
[0052] .phi..sub.i={circumflex over (.sigma.)}.sub.y for
v.sub.k.ltoreq.i.ltoreq.v.sub.k+M-1 (11) [0053] 5. Set
v.sub.k-1=v.sub.k=L and proceed to step 2 until the entire signal
is analyzed.
[0054] It can be assumed that the vector .phi. of standard
deviations is sampled from g.sub.1(.phi.|.theta..sub.1) and
g.sub.2(.phi.|.theta..sub.2) with a priori probabilities P.sub.1
and P.sub.2, respectively, where 0<P.sub.1, P.sub.2<1 and
P.sub.1+P.sub.2=0.
[0055] However, since these a priori probabilities are not known,
it has to be assumed that .phi. is sampled from p(.phi.|.xi.) which
is PDF representing a mixture of g.sub.1(.phi.|.theta..sub.1) and
g.sub.2(.phi.|.theta..sub.2) with mixing parameters being P.sub.1
and P.sub.2. In other words, p(.phi.|.xi.) is given by
p(.phi.|.xi.)=P.sub.1g.sub.1(.phi.|.theta..sub.1)+P.sub.2g.sub.2(.phi.|.-
theta..sub.2) (12)
where .xi.={P.sub.1, P.sub.2, .theta..sub.1, .theta..sub.2}.
Therefore, the mixture separation problem boils down to the
estimation of the members of .xi.. Let the set .PHI. drawn from
p(.phi.|.xi.) represent all possible outcomes of independent
trials, then the ML estimate of .xi. would be given by
.xi.*=argmax.sub..xi..SIGMA..sub..phi..epsilon..PHI.ln
p(.phi.|.xi.). (13)
[0056] Nevertheless, finding the maximum likelihood estimate of
.xi. for dual-axis swallowing accelerometry signals can be
computationally costly. Hence, some approximation is needed.
[0057] Before proceeding further on, let's consider the available
information. The location and duration (i.e. onset and offsets) of
swallows are only approximately known. In addition, it is known
that .phi. contains data which are sampled either from
g.sub.1(.phi.|.theta..sub.1) and g.sub.2(.phi.|.theta..sub.2)
depending on whether or not a swallow occurred. Therefore, rather
than solving (13), only indicator functions defined as:
u g 1 ( .phi. i ) = { .kappa. .phi. i ~ g 1 ( .phi. .theta. 1 ) 0
otherwise ( 14 ) u g 2 ( .phi. i ) = { 1 - .kappa. .phi. i ~ g 2 (
.phi. .theta. 2 ) 0 otherwise ( 15 ) ##EQU00007##
can be formed, where 0.ltoreq.K.ltoreq.1. It is clear that
u.sub.g.sub.1+u.sub.g.sub.2=1. The functions introduced by (14) and
(15) indicate the presence of different segments (i.e. no-swallow
or swallow), but the functions do not reveal any information about
the time boundaries of these segments. Therefore, the next step is
to determine these boundaries. Let us write the two indicator
functions as a matrix U=[u.sub.g.sub.1,u.sub.g.sub.2]. Furthermore,
the segment space is the set
S.sub..phi.={U.epsilon.V.sub.U|u.sub.g.sub.1,u.sub.g.sub.2.epsilon.[0,1]-
; u.sub.g.sub.1+u.sub.g.sub.2=1;
0<.SIGMA..sub.i=1.sup.Nu.sub.g.sub.ji<N, for j=1,2} (16)
where u.sub.g.sub.ji=u.sub.g.sub.j(.phi..sub.i) and V.sub.U is the
vector space of U. To find these segments, i.e., the regions
representing when .phi. was sampled from either distribution, an
objective function J.sub.m(U,v): S.sub.100.times..sup.+ should be
minimized:
J.sub.m(U,v)=.SIGMA..sub.i=1.sup.N.SIGMA..sub.j=1.sup.2(u.sub.g.sub.ji).-
sup.m(d.sub.ji).sup.2 (17)
where
(d.sub.ji).sup.2=.parallel..phi..sub.i-v.sub.j.parallel..sup.2
(18)
is the inner product induced norm; v.sub.j is the prototype of
u.sub.g.sub.j, j=1,2; and m is the weighting exponent given by
m.epsilon.[1, .infin.]. For the purposes of the present invention,
m=2. However, it should be noted that J.sub.m(U,v) can only be
minimized if d.sub.ji>0 for {j,i|1.ltoreq.j.ltoreq.2,
1.ltoreq.i.ltoreq.N}, m>1 and u.sub.g.sub.j,v.sub.j are obtained
through the following iterative steps:
u g jk = [ o = 1 2 ( jk ok ) 2 / ( m - 1 ) ] - 1 ( 19 ) v j = k = 1
N ( u g jk ) m .phi. k k = 1 N ( u g jk ) m for j = 1 , 2. ( 20 )
##EQU00008##
[0058] The above formulation is a 2-class fuzzy c-means
optimization problem. Furthermore, this minimization can be simply
realized through Picard iteration of (19) and (20): [0059] 1.
Randomly initialized U.sub.(0).epsilon.S.sub..phi. and then at
steps h=1, 2 . . . [0060] 2. Calculate {v.sub.j.sup.(h)} with (20)
and U.sup.(h-1) [0061] 3. Computer U.sup.(h) Using
{v.sub.j.sup.(h)} and (19) [0062] 4. If
.parallel.U.sup.(h)-U.sup.(h-1).parallel..ltoreq..epsilon. stop,
otherwise, increment h and return to step 2.
[0063] The aforementioned steps yield two indicator functions:
u.sub.g.sub.1 and u.sub.g.sub.2 which denote the absence or
presence of swallowing on one axis. For the dual-axis recordings,
there are four indicator functions: u.sub.g.sub.1.sub.AP,
u.sub.g.sub.2.sub.AP, u.sub.g.sub.1.sub.SI and u.sub.g.sub.2.sub.SI
with u.sub.g.sub.2.sub.AP and u.sub.g.sub.2.sub.SI representing
independently the absence of presence of swallowing vibrations in
the A-P and S-I directions, respectively. However, excessive noise
along one of the axes may lead to an incorrect estimate of swallow
multiplicity in the corresponding signal. Therefore, to obtain a
more accurate estimate of the locations and durations of swallows,
the indicator functions from both axes should be multiplied.
Therefore, the dual-axis indicator function, u.sub.DA is given
by:
u DA = u g 2 AP .times. u g 2 AP = { .rho. .phi. ~ g 2 ( .phi.
.theta. ) 0 otherwise ( 21 ) ##EQU00009##
where 0<<.rho..ltoreq.1. If desired, u.sub.DA can be turned
into binary indicator sequence as:
u DA = { 1 .rho. .gtoreq. .gamma. 0 otherwise ( 22 )
##EQU00010##
where .gamma. is predetermined threshold value. The algorithm of
the present invention is intended to be applicable to very long and
noisy data sets.
[0064] The system of the present invention may include an
accelerometer sensor capable of producing signals indicating
swallowing activities. A person skilled in the art will recognize
that a variety of accelerometer sensors may be utilized. In one
embodiment of the present invention a dual-axis accelerometer, for
example, such as an ADXL322 or other analog device, may be applied.
The sensor may be attached to a person's neck. The sensor should be
positioned anterior to the cricoid cartilage of the person's neck.
A variety of means may be applied to position the sensor and to
hold the sensor in such position, for example, such as double-sided
tape. The positioning of the sensor should be such that the axes of
acceleration are aligned to the anterior-posterior and
super-inferior directions 10, as shown in FIG. 1.
[0065] In another embodiment of the present invention, details may
be collected from the person, such as height, weight, body fat
percentage, gender and mandibular jaw length, or other details. The
person may be required to perform a variety of types and swallows
(e.g. water swallow with head in neutral position, or saliva
swallows, etc.). For each swallow, swallowing accelerometry
measurement data may be generated by the sensor.
[0066] Signals generated by the sensor may be passed as data to a
band-pass filter hardware. A person skilled in the art will
recognize that a variety of filters may be applied, such as a
filter with a pass band of 0.1-3000 Hz. Once filtered the signal
data may be sampled, for example, such as at 10 kHz using LabVIEW
program running on a laptop computer. A person skilled in the art
will recognize that other sampling techniques may be applied,
including other software programs and computer hardware.
Additionally, data storage means may be included, either on-site or
remotely. Signal data may be stored in such data storage means for
subsequent off-line analysis. Such as analysis will be performed by
way of the algorithm of the present invention which must be stored
on the computer hardware and compatible with the software of the
system of the present invention.
EXAMPLE 1
[0067] The accuracy of the segmentation algorithm of the present
invention has been evaluated in the course of a study which
undertook two evaluations. First, by way of a set of simulated test
signals with known change points, i.e., swallow locations; and
secondly, by way of a subset of signals with 295 real swallows
manually extracted by speech language pathologist (SLP).
Data Collection
[0068] Four hundred and eight (408) participants (aged 18-65) were
recruited over a three month period from a public science centre.
Participants had not documented swallowing disorders and passed an
oral mechanism exam prior to participation.
[0069] Participants sat behind a screen for privacy. They answered
a set of questions relating to medical and swallowing history. A
speech language pathologist measured the height, weight, body fat
percentage (BIA Meter, BC-500, Tanita) and mandibular jaw length of
each participant. A dual-axis accelerometer (ADXL322, Analog
Device) was attached to the participant's neck (anterior to the
cricoid cartilage) using double sided tape. The axes of
acceleration were aligned to the anterior-posterior and
superior-inferior directions, as shown in FIG. 1. Data were
band-pass filtered in hardware with a pass band of 0.1-3000 Hz and
sampled at 10 kHz using a custom LabVIEW program running on a
laptop computer. Data were saved for subsequent off-line
analysis.
[0070] With the accelerometer attached, each participant was cued
to perform five saliva swallows. After each swallow, there was a
brief rest to allow for saliva production. Subsequently, the
participant completed five water swallows by cup with their chin in
neutral position (i.e. perpendicular to the floor) and five water
swallows in the chin-tucked position. Water was served chilled, in
ten individual cups so that pre and post swallowing cup weight
could be measured on a digital scale. The measurements facilitated
the estimation of bolus volume. Previous research suggested that
natural sip size during this kind of task is between 5 and 8 ML per
sip. The entire data collection session lasted fifteen minutes per
participant.
[0071] Examination of the collected data revealed that some
acquired signals were inadequate for further analysis due to the
presence of strong disturbances, such as vocalization, coughing,
and excessive head movements. Nevertheless, 9800 swallows were
retained for subsequent analysis.
Validation with Synthetic Test Signals
[0072] In the collected data, exact locations of swallow onsets and
offsets were unknown as corresponding videofluoroscopic sequences
were not acquired. The synthetic signals with known change points
thus provided a gold standard against which the segmentation
algorithm could be benchmarked.
[0073] To ensure that the test signals mimicked the dual-axis
swallowing accelerometry signals acquired in this experiment, the
following data generation rules applied. [0074] For every
realization, two signals should be generated: one simulating
acceleration in the A-P direction, and the other simulating
acceleration in S-I direction. [0075] There should be five distinct
intervals where the variance of signals increases above the
baseline variance. [0076] Each of the five intervals should have
random duration and random frequency components to mimic
intersubject variations.
[0077] The following definitions of a signal s.sub.j(n) adheres to
the above rules.
s j ( n ) = { l = 1 15 b jl sin ( 2 .pi. f jl nT + .theta. l ) + w
= 1 4 0.2 sin ( 2 .pi. f jw nT ) n 1 .ltoreq. n .ltoreq. n 2 l = 1
15 b jl sin ( 2 .pi. f jl nT + .theta. l ) + w = 1 4 0.2 sin ( 2
.pi. f jw nT ) n 3 .ltoreq. n .ltoreq. n 4 l = 1 15 b jl sin ( 2
.pi. f jl nT + .theta. l ) + w = 1 4 0.2 sin ( 2 .pi. f jw nT ) n 5
.ltoreq. n .ltoreq. n 6 l = 1 15 b jl sin ( 2 .pi. f jl nT +
.theta. l ) + w = 1 4 0.2 sin ( 2 .pi. f jw nT ) n 7 .ltoreq. n
.ltoreq. n 8 l = 1 15 b jl sin ( 2 .pi. f jl nT + .theta. l ) + w =
1 4 0.2 sin ( 2 .pi. f jw nT ) n 9 .ltoreq. n .ltoreq. n 10 l = 1
15 b jl sin ( 2 .pi. f jl nT + .theta. l ) + w = 1 4 0.2 sin ( 2
.pi. f jw nT ) otherwise ( 23 ) ##EQU00011##
where j=1,2 indexes the two directions; T=0.0001 seconds;
1.ltoreq.n.ltoreq.N and N.about.(600000, (50000).sup.2) with a
constraint that N>150000; N>n.sub.10>n.sub.9> . . .
>n.sub.1; |n.sub.2q-n.sub.2q-1|.about.(250000,(5000).sup.2) for
1.ltoreq.q.ltoreq.5 with a constraint that
|n.sub.2q-n.sub.q|>5000; n.sub.2.kappa.+1-n.sub.2.kappa.-1=[N/5]
where 1.ltoreq..kappa..ltoreq.4; b.sub.jl is uniformly drawn from
[0,0.05]; f.sub.jl is uniformly drawn from [1,5000]; .theta..sub.l
is uniformly drawn from [0,.pi.] and f.sub.jw.about.(90,(15).sup.2)
with a constraint f.sub.jw>1. Using the above definition, one
thousand (1000) pairs of dual-axes test signals were simulated. The
top graph of FIG. 3 depicts a typical simulated test signal.
[0078] Accuracy was defined as a number of correctly identified
high-variances segments divided by the number of all high-variance
segments. To be considered correct, an extracted segment had to
overlap with the corresponding known segment by at least 90%.
Validation Against Manually Segmented Swallows
[0079] As a second evaluation step, a speech pathologist (SLP)
manually segmented nineteen recordings representing saliva swallows
(dry swallows), twenty recordings representing water swallows (wet
swallows), and nineteen recordings representing water swallows in
the chin-tuck position (wet chin tuck). Manual segmentation
involved the location of onset and offsets by visual inspection and
auditory verification. Each recording contained five or six
swallows, yielding a total of 295 swallows. It should be noted that
the selected recordings were chosen to fairly represent different
age and gender groups of the population under study.
[0080] In the validation against the human expert, a correctly
segmented swallow was defined for the purpose of the study as one
in which there was a minimum 90% overlap with the SLP extracted
swallow. A sample swallowing accelerometry signal is depicted in
FIG. 2(a) along with a binary indicator function, where
"high"denoted the presence of a swallow as indicated by the SLP.
The second swallow is arbitrarily selected in FIG. 2(b)-(e) to
illustrate different segmentation possibilities. In each graph, the
dashed lines represent possible indicator functions obtained by the
algorithm 12. Evidently, to be considered a correctly segmented
swallow as in graphs (b)-(d), most of the swallow duration
(>90%) as indicated by the SLP must be captured. Otherwise, the
algorithm is deemed to have incorrectly identified the swallow; as
exemplified in FIG. 2(e).
[0081] After signals from all 408 participants had been segmented,
non-parametric inferential statistical methods and linear
regression analysis were used to test for potential effects of
gender, BMI and age on swallowing duration.
Results
[0082] With the 1000 pairs of simulated test signals, the
extraction accuracy of the proposed algorithm was 97.7.+-.1.3%.
Also, the average duration of the extracted segment was
(2.59.+-.0.50).times.10.sup.4 points which is statistically similar
(p=0.18) to the average duration of the original segments
(2.5.times.10.sup.4 points). This close agreement between original
and extracted segment onsets, offsets and durations is illustrated
in the bottom graph of FIG. 3. Results with the test signals
demonstrated that the proposed algorithm is indeed capable of
accurately extracting segments with elevated variance and varying
length from long time series.
[0083] The results of the validation against manual segmentation by
the SLP are summarized in FIG. 5. Each row in the table represents
the performance of the segmentation algorithm on one type of
swallow.
[0084] Evidently, the proposed algorithm achieves very good overall
accuracy considering that the segmentation in performed on raw data
(i.e. there was no pre-processing of data). The lowest accuracy is
achieved for set shin tuck swallows. However, this is expected
since the wet chin tuck swallows are manifested through very
complex signals, especially in the S-I direction, as shown in FIG.
5.
[0085] The temporal accuracy of the algorithm can be examined
through a comparison of the durations of manually and automatically
segmented swallows. The average durations are shown in the last two
columns of FIG. 5. Several observations are in order. While, the
durations for dry the wet swallows appear to agree closely with the
durations obtained by the SLP, a Wilcoxon rank-sum test revealed
that the durations are statistically similar only for the dry
swallows (p=1.10). The durations of the wet chin tuck swallows were
overestimated by the algorithm, on average, by one second. The
overestimation was due to the overwhelming motion artefact depicted
in FIG. 4.
[0086] With additional measurements, e.g., a head motion sensor;
these swallow durations could be further refined. Additionally, the
segmented vibration signals likely included events associated with
both the oral and pharyngeal phase of swallowing, each of which
persists for approximately one second. This would explain the
algorithm's overall average duration of 2.4.+-.1.1 s in FIG. 5,
which incidentally, is consistent with the temporal
characterizations of the oral-pharyngeal phase of swallowing
reported by Sonies et al.
Analysis of Swallowing Signals' Duration
[0087] The study further attempted to uncover any associations
between the duration of the segmented signals and the
anthropometric/demographic variables, namely, gender, BMI or age.
The results of such an analysis are summarized in Tables 2-4. The
table entries are average durations of the segmented signals in
seconds for different levels of the selected variables (gender, BMI
or age). While both neck circumference and BMI were measured, a
simple linear regression analysis showed these variables were
highly correlated. Therefore, neck circumference was discarded and
BMI was chosen for further analysis. The latter variable is
appealing since participants can be grouped according to
standardized BMI intervals.
[0088] In FIG. 6, events associated with wet swallows are shown as
consistently manifested as the shortest signals (Wilcoxon rank-sum
test, p<<10.sup.-5), while the events associated with wet
chin tuck swallows tend to embody the longest signals (Wilcoxon
rank-sum test, p<<10.sup.-5). The extended length of the wet
chin tuck swallows has already been attributed to the algorithm's
overestimation in the presence of excessive motion artefact.
Regarding the other types of swallows, Sonies et al., also found
that wet swallows were shorter than dry ones. Finally, the
swallowing signals obtained from male participants were longer than
those extracted from female participants, for dry and wet swallow
types (Wilcoxon rank-sum test, p<<10.sup.-5). This difference
in duration can be attributed to gender-based anatomical
differences in the oropharyngeal mechanism. The gender difference
did not appear in the wet chin-tuck swallows due to inflated
variability in durations for this task, likely due to motion
artefact.
[0089] Data reflected in FIG. 7 suggests that as a person's BMI
increases, the duration of the swallowing events increases as well.
According to a regression test, this dependence on BMI is
statistically significant for the events associated with wet chin
tuck swallows (p<10.sup.-5). A possible expectation is that an
increase in adipose tissue results in an attenuation of the signal
amplitude and velocity. The latter effect may allow the vibration
signal to decay more slowly, thereby extending the duration of the
measured activity.
[0090] Moreover, the results suggest that as the age of the
participant increases, the duration events associated with a
swallow tends to increase as well (FIG. 8). Based on the results of
a regression test, this dependence on age is statistically
significant for the events associated with all types of swallows
(p<<10.sup.-5). This can trend may be attributed to the
age-related decoupling of oral and pharyngeal stages of swallowing,
leading to longer overall swallowing times.
[0091] It will be appreciated by those skilled in the art that
other variations of the embodiments described herein may also be
practiced without departing from the scope of the invention. Other
modifications are therefore possible. For example, the system may
be applied to the collection of other signal data that involves
long data sets that include a clear activation period that has a
significantly different variance from the baseline, such as upper
limb movement in patients after strokes, automatic step detection
and extraction of movements while a person is washing his or her
face. A skilled reader will understand that other data may also be
utilized in embodiments of the present invention. Moreover, the
present invention may be applied to three-dimensional data
reflecting a phenomenon that manifests itself in the same manner in
all three dimensions.
* * * * *