WO2013086615A1 - Dispositif et méthode pour dépister la dysphagie congénitale - Google Patents
Dispositif et méthode pour dépister la dysphagie congénitale Download PDFInfo
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- WO2013086615A1 WO2013086615A1 PCT/CA2012/001145 CA2012001145W WO2013086615A1 WO 2013086615 A1 WO2013086615 A1 WO 2013086615A1 CA 2012001145 W CA2012001145 W CA 2012001145W WO 2013086615 A1 WO2013086615 A1 WO 2013086615A1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
- A61B5/4205—Evaluating swallowing
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
Definitions
- the present disclosure relates to dysphagia, and in particular, to a device and method for detecting congenital dysphagia.
- Feeding disorders encompass a broad range of problems associated with eating solid and liquid foods. Difficulty with the process of swallowing is known as dysphagia, and can occur in both adult and pediatric populations. Epidemiologic data on the prevalence of dysphagia in children is not readily available. However, feeding disorders as a whole are estimated to be present in a significant and increasing portion of the pediatric population: in 25% to 45% of typically developing children and in 33% to 80% of children with developmental disorders. Dysphagia impacts the health and well- being of a child as the disorder may lead to malnutrition, dehydration, impairment of physical growth and developmental delay. Dysphagia can also induce feeding-related stress and challenges, affecting the psychosocial well-being of the child, family and other caregivers. A particularly dangerous condition, aspiration pneumonia, is frequently associated with dysphagia.
- VFSS videoflouroscopic swallow study
- VFSS is not without its shortcomings. Proper interpretation of the swallow usually requires an experienced practitioner or a team of assessors. The method itself also exposes children to ionizing radiation and therefore should be used minimally. As well, the process is expensive both in terms of equipment and human resources. Furthermore, VFSS can only provide a snapshot of a patient's swallowing function, despite the fact that this function can vary from day to day. Finally, many children find VFSS frightening and uncomfortable.
- the classification of swallowing accelerometry in the pediatric population presents different analytical challenges than those encountered in the adult case. While, the classification of uniaxial vibrations associated with swallowing activity in children was proposed using a radial basis classifier, the 80% adjusted accuracy achieved using this classifier only detected a very specific event, i.e., airway entry with inspiratory airflow, and was thus of limited utility.
- a device for use in identifying congenital dysphagia from execution of a swallowing event comprising: a dual axis accelerometer to be positioned in a region of the candidate's throat and configured to acquire axis-specific vibrational data representative of the swallowing event; and a processing module operatively coupled to said accelerometer and configured to process said axis-specific data to extract therefrom, for each axis, a set of designated features representative of the swallowing event, and classify said vibrational data, based on at least some of said extracted features, as indicative of one of normal swallowing and aspiration due to congenital dysphagia.
- the device is for use with pediatric candidates.
- the processing module comprises a classifier configured to receive as input said extracted features and output a classification specific to the swallowing event indicative of a normal event or an aspiration event.
- said classifier comprises a non-linear classifier.
- said classifier comprises a Support Vector Machines (SVM) classifier with a Radial Basis Function (RBF) kernel.
- SVM Support Vector Machines
- RBF Radial Basis Function
- said plurality of features comprises at least one feature representative of a main lobe of vibrational data.
- said at least one feature representative of said main lobe of vibrational data comprises a peak vibrational amplitude.
- said accelerometer is configured for alignment along an anterior-posterior axis (A-P) and a superior-inferior axis (S-I) of the candidate's throat.
- said plurality of features comprises at least one time-frequency feature.
- said at least one time-frequency feature comprises a feature related to a short-time Fourier transform of said vibrational data.
- said processing module having stored therein a designated input feature vector identified from a known vibrational data set to exploit correlations between said set of designated features, said processing module further configured to transform said extracted features as a function of said input feature vector and classify said vibrational data as a function of said transformed features.
- a dimension of said designated input feature vector is less than that of said designated set of features.
- said designated input feature vector is defined by a selected set of principle components identified from a principle component analysis of said known vibrational data set.
- a method for identifying congenital dysphagia in a candidate via a designated candidate swallowing event comprising: recording dual-axis vibrational data representative of the swallowing event; extracting, for each axis, a set of designated features from said dual axis vibrational data; and classifying said extracted features as indicative of one of normal swallowing and aspiration due to congenital dysphagia.
- the candidate consists of a pediatric candidate.
- said classifying step comprises comparing said extracted features with a pre-classified set of such features, said pre-classified set extracted from a known vibrational data set and classified as representative of candidates having known congenital dysphagia conditions.
- said comparing step comprises transforming said extracted features in accordance with a designated feature vector identified from said known vibrational data set to exploit correlations between said set of designated features; and classifying said transformed features as a function of one or more designated classification criteria previously identified from said pre-classified set to distinguish between such feature vectors representative of congenital dysphagia and those representative of normal swallowing.
- a method for classifying cervical dual-axis acceierometry data acquired in respect of a candidate swallowing event to detect congenital dysphagia comprising: receiving as input axis-specific vibrational data representative of the swallowing event; extracting a plurality of designated features representative of the swallowing event for each axis of said axis-specific vibrational data; comparing said extracted features with one or more designated classification criteria previously identified from such features when extracted from a known vibration data set to distinguish between normal and impaired swallowing events; and outputting, based on said comparing, classification of said vibrational data as indicative of one of normal swallowing and aspiration due to congenital dysphagia.
- said comparing comprises transforming said extracted features in accordance with a designated feature vector identified from said known vibrational data set to exploit correlations between said set of designated features, said one or more designated classification criteria previously identified to distinguish between such feature vectors representative of congenital dysphagia and those representative of normal swallowing.
- said designated input feature vector is defined by a selected set of principle components identified from a principle component analysis of said known vibrational data set.
- a computer readable-medium having statements and instructions stored thereon for implementation by a processor to automatically implement the above methods.
- a device for use in identifying congenital dysphagia from execution of a swallowing event comprising: an input operable to receive axis-specific vibrational data representative of the swallowing event, said vibrational data acquired from a dual-axis accelerometer positioned in a region of the candidate's throat; the above computer readable medium; a processor operable to implement the statements and instructions stored on said computer readable medium; and an output operable to render classification of said vibrational data as indicative of one of normal swallowing and aspiration due to congenital dysphagia.
- the device further comprises said dual-axis accelerometer.
- Figure 1 is a schematic diagram of an aspiration detection device in operation, in accordance with an embodiment of the invention.
- Figure 2 is a schematic diagram of an aspirometer, and components thereof, in accordance with an embodiment of the invention.
- Figure 3 is a high level dual axis accelerometry data processing flow diagram for implementation by an aspiration detection device, in accordance with an embodiment of the invention;
- Figure 4 is an illustrative dual axis accelerometry data processing flow diagram, for implementation by an aspiration detection device, in accordance with an embodiment of the invention
- Figure 5 are illustrative plots of dual-axis swallowing accelerometry signals, wherein (a) and (b) show signals for the A-P and S-I axes, respectively, having been cleansed of vocalizations and head movements, and subsequently segmented, wherein the same signals are shown after deno i s i ng and trimming in the A-P (c) and S-I (d) axes; and
- Figure 6 is an exemplary detailed dual axis accelerometry data processing flow diagram, in accordance with one embodiment of the invention.
- dysphagia or swallowing disorder can negatively impact a child's health and development, as can it adversely affect the health of older subjects when not properly diagnosed, treated and/or managed.
- the gold standard of dysphagia detection is videofluoroscopy which exposes the child or older subject to ionizing radiation, and requires specialized clinical expertise and expensive institutionally-based equipment, precluding day-to-day and repeated assessment of fluctuating swallowing function.
- the methods, systems and devices described herein provide for the identification of unsafe swallows via swallowing acceierometry, which is the non- invasive measurement of cervical vibrations during swallowing, and which can provide a portable and cost-effective bedside alternative to the current standards.
- dual-axis accelerometry is used to accurately identify unsafe swallowing events in pediatrics, or again within the context of older candidates manifesting symptoms of, or suspected of suffering from congenital dysphagia, for example, via implementation of a classifier, classification system and method, as described in greater detail below.
- dual-axis accelerometric signals were collected simultaneous to videofluoroscopic records from 29 pediatric participants (age 6.8 ⁇ 4.8 years; 20 males) previously diagnosed with neurogenic dysphagia. Participants swallowed 3-5 sips of barium-coated boluses of different consistencies (normally, from thick puree to thin liquid) by spoon or bottle. Videofluoroscopic records were reviewed retrospectively by a clinical expert to extract swallow timings and ratings. The dual-axis acceleration signals corresponding to each identified swallow were pre-processed, segmented and trimmed prior to feature extraction from time, frequency, time-frequency and information theoretic domains. Feature space dimensionality was reduced via principal component analysis.
- the system 100 generally comprises a dual axis accelerometer 102 to be attached in a throat area of a candidate (a child or older subject suspected of suffering from congenital dysphagia, in this example) for acquiring dual axis acceierometry data and/or signals during swallowing (e.g. see illustrative S-I Acceleration signal 104 shown in Figure 1).
- acceierometry data may include, but is not limited to, throat vibration signals acquired along the anterior-posterior axis (A-P) and superior-inferior axis (S-I).
- A-P anterior-posterior axis
- S-I superior-inferior axis
- the accelerometer is operatively coupled to an aspirometry data processing module or aspirometer 106 configured to process the acquired data in accordance with the classification system and method discussed below, in distinguishing healthy from unhealthy swallows.
- the processing module 106 is depicted herein as a distinctly implemented device, or aspirometer, operatively coupled to accelerometer 102 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, for example, depending on the intended practicality of the aspirometer, and/or context within which it is to be implemented.
- 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 diagnostics tools.
- 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 diagnostics tools.
- the processing module depicted herein generically as a self-contained device or aspirometer 200, generally comprises a power supply 202, such as a batteiy or other known 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 (as shown in Figure 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 associated with the acquisition and processing (i.e. classification) of accelerometry data for each swallowing event.
- 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 and/or processed data with respect to aspiration detection, screening, etc. (e.g. graphical user interface such as CRT, LCD, LED screen or the like, visual and/or audible signals/alerts/warnings/cues, numerical displays, etc.)
- data and/or commands e.g. keyboard, mouse, scroll pad, touch screen, push-buttons, switches, etc.
- graphical user interface such as CRT, LCD, LED screen or the like, visual and/or audible signals/alerts/warnings/cues, numerical displays, etc.
- aspirometer 200 may equally be implemented as a distinct and dedicated device, such as a dedicated home, clinical or bedside aspiration 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, or the like.
- the processing of acquired or collected dual axis accelerometry data 302 representative of at least one swallowing event may be composed of two broad steps, namely a dual axis feature extraction step 304 applied for data representative of each swallowing event along each axis, and a swallowing event classification step 306 based on the extracted features of step 304.
- a dual axis feature extraction step 304 applied for data representative of each swallowing event along each axis
- a swallowing event classification step 306 based on the extracted features of step 304.
- such swallowing events may be effectively classified as normal swallowing events or aspiration events.
- accelerometry data 402 is acquired or provided in respect of multiple swallowing events. This data is then preprocessed at step 404 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.
- the preprocessed data is then manually or automatically segmented into distinct swallowing events (step 406).
- an automated swallowing event segmentation process such as described in co-pending United States Patent Application Publication No. 2010/0160833, the entire contents of which are incorporated herein by reference, may be applied to the data to segment this data by swallowing event.
- manual segmentation may be applied 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).
- data may be recorded distinctly for each swallowing event, thus naturally providing event-specific data without need for further segmentation.
- swallowing event-specific data may be preprocessed individually, thus effectively applying the manual signal segmentation step 406 of Figure 4 during acquisition of accelerometry data 402 and prior to preprocessing step 404.
- these and other such variations may be considered herein without departing from the general scope and nature of the present disclosure.
- the event-specific data is then processed by a dual axis feature extraction module 408 configured to extract for each axis, a plurality of preset features previously identified as amenable to distinguishing signals respective to each output classification of interest (e.g. as identified via classifier training, discussed below), allowing for each swallowing event to be classified at step 412 based on these extracted features.
- a dual axis feature extraction module 408 configured to extract for each axis, a plurality of preset features previously identified as amenable to distinguishing signals respective to each output classification of interest (e.g. as identified via classifier training, discussed below), allowing for each swallowing event to be classified at step 412 based on these extracted features.
- classification thus allows for the determination and output 414 of which swallowing event represented a normal swallowing event as compared to a likely aspiration event.
- the device 200 of Figure 2 may be configured to implement the above classification as a function of a designated feature set previously identified to distinguish, when compared with designated classification criteria, between vibrational data representative of a normal swallowing event and that representative of congenital dysphagia.
- the device 208 may be configured to receive as input axis- specific vibrational data representative of the swallowing event (e.g. from an integrated, associated and/or remote or distinct dual-axis accelerometer) and extract therefrom (or from a pre-processed representation thereof) a plurality of designated features representative of the swallowing event for each axis of the axis-specific vibrational data.
- the features may be compared, either directly or indirectly, with one or more designated classification criteria, these criteria previously identified from such features when extracted from a known vibrational data set to distinguish between normal and impaired swallowing events.
- an output is provided classifying the input data as indicative of one of normal swallowing and aspiration due to congenital dysphagia.
- comparison of input data with the one or more classification criteria is performed via a designated transformation of the input data, for example, in accordance with a designated feature vector identified from the known vibrational data set to exploit correlations between the set of designated features.
- classification criteria may be characterized as resulting from the application of a same transformation to the known data set, thus allowing for comparison of the feature vector associated with the acquired data, with those similarly associated with the blown data set and thus known to represent normal or impaired swallowing events.
- the designated input feature vector is defined by a selected set of principle components identified from a principle component analysis of the known vibrational data set, and in particular, in which a dimensionality of the input vector is thus effectively reduced from an overall dimensionality of the extracted feature set.
- Designated classification criteria in the below example output from the training of a selected data classifier operating on the known feature vectors, result when applied to a new input feature vector, in the classification of this new vector as indicative of a normal or impaired swallowing event due to congenital dysphagia.
- the accelerometer Analog Devices, ADXL322.
- S-I superior-inferior
- A-P posterior-anterior
- Each child was fed barium-coated boluses of varying consistencies, ranging from thick puree to thin liquid. Children were generally fed by spoon and infants were fed by bottle. The starting consistency varied by child and was determined based on the recent case history.
- the swallowing vibration signals from the accelerometer were filtered and amplified (Astro-Med Inc., Grass, P55 A.C. Preamplifier; filtered lHz to 3 kHz; amplification lOx) and sampled at 10 kHz via a data-acquisition card (National Instruments, USB NI-6210) prior to storage on a research computer.
- Bolus activity in the cervical region was captured by lateral fluoroscopic video recording onto a research computer via a PCI card (National Instruments, PCI 1405).
- the trigger to initiate and terminate the recording of these inputs was controlled through a custom LabVIEW application (National Instruments), which served to synchronize the VFSS video recording with the accelerometric data, enabling swallow time-stamp identification retrospectively.
- Table 1 Swallow rating based on path of bolus; Each swallow was assigned a number using this system based on visual observation of the VFSS video playback [0061] Swallows rated as 1 were discarded from the study to avoid ambiguity between safe and unsafe swallowing.
- the resultant data set contained 94 swallows rated 2 (i.e. Unsafe swallows) and 544 swallows rated 0 (i.e. safe swallows).
- the dual axis accelerometer generated time-stamped S-I and A-P vibration signals. Each signal was processed individually. Initially, the signal was down-sampled to 1 kHz. Next, segments deemed to contain vocalizations by the robust algorithm for pitch tracking (RAPT) were identified. The RAPT parameters were adjusted to minimize the number of discarded segments which contained swallows as follows: the maximum possible fundamental frequency was set to 1000 Hz, the duration of frame size was set to 0.01s, the duration of the correlation window was set to 0.01s and the maximum number of hypotheses at each frame was set to 50. Segments identified by the RAPT algorithm with the adjusted parameters were discarded. Any swallows within these removed segments were therefore eliminated from further consideration.
- RAPT pitch tracking
- Each segment was filtered using a denoising algorithm developed for swallowing accelerometry signals, such as that described in co-pending US Patent Application no. 12/819,216, the entire contents of which are incorporated herein by reference.
- This method was based on a ten-level discrete wavelet transform using the Meyer wavelet with soft thresholding.
- Figure 5 provides an example of a swallowing accelerometry signal that has been cleansed of vocalizations and head movements and subsequently segmented in the A-P (a) and S-I (b). The same signals are shown afte r de no i s i ng and trimming in the A-P (c) and S-I (d).
- duration and visual characteristics depicted herein are meant to provide an example only, as such characteristics may vary between swallows.
- features were extracted from the time, frequency, time-frequency and information-theoretic domains to capture differences between safe and unsafe swallows.
- the following features were calculated for each of the A-P and S-I signal segments: mean, variance, skewness, kurtosis, entropy rate, memory, Lempel-Ziv complexity, measures of the relative energy and entropy in each wavelet decomposition level as extracted from a 10-level discrete wavelet decomposition of the signal using the discrete Meyer wavelet and as extracted from a 20-level Daubechies 5 wavelet decomposition, normality, stationarity, number of zero-crossings, dispersion ratio, interquartile range, peak Fast Fourier Transform (FFT) magnitude, frequency at spectral peak, and maximum hyolaryngeal excursion (estimated via double integration of accelerometry).
- FFT Fast Fourier Transform
- frequency corresponding to maximum spectral density over all time of the short-time Fourier transform spectrogram (max frequency); difference between frequency values corresponding to 75% and 25% of maximum spectral density at time corresponding to max frequency; and 20 features computed by the summation of power spectral density values within a 10Hz range, from 0 to 200Hz (calculated in unit of power per radian per sample using Welch's averaged modified periodogram method of spectral estimation).
- Classification was performed using 8 fold cross-validations. In each fold, training and test sets contained a balanced distribution of both classes. The dimensionality of the training subset was reduced using principal component analysis (PCA) with a variance threshold of 80%. The principle components served as the inputs to four different classifier models: linear discriminant analysis (LDA) using Euclidean and Mahalanobis distance measure variants, and support vector machines (SVM) trained with a linear kernel and with a Radial Basis Function (RBF) kernel. Adjusted accuracy was averaged over all folds and runs.
- PCA principal component analysis
- LDA linear discriminant analysis
- SVM support vector machines
- RBF Radial Basis Function
- the classifier was further optimized through a method of removing redundant variables for a data set composed of more than 10 features.
- method B2 was deployed, as introduced by Joliffe in "Discarding variables in a principal component analysis. I: Artificial data. Journal of the Royal Statistical Society, Series C (Applied Statistics) 1972, 21(2): 160-173," the entire contents of which are hereby incorporated herein by reference. This method was selected given its ability to parsimoniously represent the data while achieving accuracies comparable to those attained with the full feature set.
- the feature set was reduced by removing variables associated with the largest coefficient in each of the last 70 principal components when ordered by their contribution to variance, from largest to smallest. Principal component analysis was then performed on the new feature subset and dimensions were reduced to 16-17. The reduced subset of principal components was then input to the SVM classifier with the RBF kernel. All steps outlined in the preceding sections are outlined in Figure 6.
- Table 2 displays averaged results of multiple runs of 8-fold cross- validation for each of the four different classification models using principal components comprising the full 144 feature set. The reduction in dimensionality as a result of PCA is also reported for each of the models.
- PCA diminished the dimensionality from 144 to 16 or 17 dimensions, depending on the particular iteration and fold.
- the average input dimensionality to the classifier was 17 ⁇ 1.
- Multiple runs of 8- fold cross-validation using this reduced feature set obtained an average adjusted accuracy of 89.6% ⁇ 0.9, a sensitivity of 92.2% ⁇ 1.6 and a specificity of 86.9% ⁇ 0.9.
- the peak accelerometry amplitude has been correlated to the movement of the hyoid bone and laiynx during a swallow.
- Studies evaluating dysphagic swallowing suggest penetration or aspiration may occur with delay or absence of hyolaryngeal movement or improper closure of the larynx. Therefore, differences in hyolaryngeal movement may result in a change in the duration and area of the main lobe, providing features which discriminate between healthy and unhealthy swallows.
- the selection of features relating to the short-time Fourier transform is also consistent with past studies that demonstrated the discriminative value of time-frequency features of swallowing accelerometry signals.
- the improvement in accuracy attained using the nonlinear RBF kernel indicates that the two classes were not linearly separable, and that the selection of a high dimensional input vector, i.e., 16 or 17 dimensions, may prove beneficial in achieving high accuracy.
- visual inspection of the accelerometer data did not reveal a regular signal pattern for either class, suggesting that no obvious low dimensional representation of the data would yield class separability.
- the data were necessarily collected under a variety of conditions; feeding modality (spoon or cup), bolus viscosity (thin liquid to thick puree), participant age and etiology of dysphagia varied among participants. These variables may have introduced differential effects on the accelerometer signals, even within one class.
- a multi-dimensional, non-linear classifier may be selected, in accordance with one embodiment, to sort through such complex inputs, and thus provide a robust solution for widespread aspiration detection.
- dual-axis accelerometry for the discrimination of swallowing signals in the dysphagic pediatric population has been validated.
- a nonlinear classifier was used to achieve an adjusted accuracy of 90% using an input vector of up to 17 dimensions. This study demonstrates that, regardless of the potential differences in input signal quality that may exist between adult and pediatric swallowing, dual-axis accelerometry holds significant promise for the screening for unsafe swallows in the pediatric domain.
- the above provides one example of a classification system for screening unsafe swallows in pediatrics, in accordance with one embodiment of the invention. Other embodiments may also be considered to provide similar effects. Namely, Table 4, below, outlines various alternative approaches to the above example.
- Fisher's Ratio can be used instead of PCA to reduce dimensions prior to input to an LDA classifier.
- the dimensions can be reduced according to different levels of percent contribution to variation (e.g. 75%, 80% or 85%), where in the above example an optimal percent of 80% was selected to achieve the above-reported accuracies.
- a range of feature combinations can be selected for optimization. It was observed that the use of PCA to reduce dimensions (without use of B2 to remove features) generated generally higher adjusted accuracies than the use of the Fisher's Ratio to select features, but not significantly so.
- the number of folds utilized during cross-validation can also be adjusted, with general expectation that the accuracy will increase incrementally with increased folds.
- filters may be applied to the trimmed data, such as for example, a lowpass Butterworth filter with a passband of 0 to 190Hz with 3dB of ripple and a stopband of 220 to 500Hz with 60dB of attenuation, or a Daubechies 10 filter, to achieve similar results.
- a lowpass Butterworth filter with a passband of 0 to 190Hz with 3dB of ripple and a stopband of 220 to 500Hz with 60dB of attenuation, or a Daubechies 10 filter, to achieve similar results.
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Abstract
La présente invention a pour objet des méthodes et des dispositifs destinés à identifier la dysphagie congénitale chez des candidats pédiatriques. D'une manière générale, les méthodes et les dispositifs envisagés dans le cadre de l'invention font appel à un accéléromètre bi-axial cervical pour distinguer les événements de déglutition dangereux des événements de déglutition non dangereux.
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Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015154960A1 (fr) * | 2014-04-09 | 2015-10-15 | Nestec S.A. | Technique de détermination d'une déficience de la déglutition |
WO2017137844A1 (fr) * | 2016-02-09 | 2017-08-17 | Holland Bloorview Kids Rehabilitation Hospital | Ajustement de signal et réduction des faux positifs de données d'accélérométrie de déglutition post-segmentation |
CN112135564A (zh) * | 2018-05-23 | 2020-12-25 | 松下知识产权经营株式会社 | 摄食吞咽功能评价方法、程序、评价装置以及评价系统 |
EP3658024A4 (fr) * | 2017-07-27 | 2021-04-28 | Holland Bloorview Kids Rehabilitation Hospital | Détection automatique d'aspiration-pénétration au moyen de signaux d'accélérométrie de déglutition |
US11490853B2 (en) * | 2017-02-28 | 2022-11-08 | Societe Des Produits Nestle S.A. | Methods and devices using meta-features extracted from accelerometry signals for swallowing impairment detection |
US11839482B2 (en) | 2018-06-26 | 2023-12-12 | Koninklijke Philips N.V. | Monitoring swallowing in a subject |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1773184A1 (fr) * | 2004-06-17 | 2007-04-18 | Bloorview Macmillan Children's Centre | Système et méthode pour détecter l'activité de déglutition |
US20100160833A1 (en) * | 2008-10-29 | 2010-06-24 | Tom Chau | Method and system of segmentation and time duration analysis of dual-axis swallowing accelerometry signals |
-
2012
- 2012-12-14 WO PCT/CA2012/001145 patent/WO2013086615A1/fr active Application Filing
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1773184A1 (fr) * | 2004-06-17 | 2007-04-18 | Bloorview Macmillan Children's Centre | Système et méthode pour détecter l'activité de déglutition |
US20100160833A1 (en) * | 2008-10-29 | 2010-06-24 | Tom Chau | Method and system of segmentation and time duration analysis of dual-axis swallowing accelerometry signals |
Non-Patent Citations (6)
Title |
---|
LEE, J. ET AL.: "Effects of liquid stimuli on dual-axis swallowing accelerometry signals in a healthy population", BIOMEDICAL ENGINEERING ONLINE, vol. 9, no. 7, 4 February 2010 (2010-02-04), pages 1 - 14, XP055073083, Retrieved from the Internet <URL:http://ww.biomedcentral.com/content/pdf/1475-925X-9-7.pdf> * |
MEREY, C. ET AL.: "Quantitative classification of pediatric swallowing through accelerometry, art 34", JOURNAL OF NEUROENGINEERING AND REHABILITATION, vol. 9, 9 June 2012 (2012-06-09), pages 1 - 8, XP021116692, Retrieved from the Internet <URL:http://wwv.jneuroengrehab.com/content/9/1/34> * |
MEREY, C.: "Quantitative classification of pediatric swallowing through accelerometry (Thesis)", GRADUATE DEPARTMENT OF INSTITUTE OF BIOMATERIALS AND BIOMEDICAL ENGINEERING, 4 December 2012 (2012-12-04), pages 1 - 28, XP003031247, Retrieved from the Internet <URL:https://tspace.library.utoronto.ca/bitstream/1807/33763/1/Merey_Celeste_201111_MHSc_thesis.pdf> * |
SEJDIC, E. ET AL.: "A procedure for denoising dual-axis swallowing accelerometry signals", PHYSIOLOGICAL MEASUREMENTS, 31 January 2010 (2010-01-31), pages N1 - N9, XP055073084, Retrieved from the Internet <URL:http://www.imedlab.org/publicationsdenoising%20swallowing%20signals.pdf> * |
SEJDIC, E. ET AL.: "Segmentation of dual-axis swallowing accelerometry signals in healthy subjects with analysis of anthropometric effects on duration of swallowing activities", IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 56, no. 4, April 2009 (2009-04-01), pages 1090 - 1097, XP011338945, Retrieved from the Internet <URL:http://www.imedlab.org/publications/swallowing%20segmentation.pdf> * |
STEELE. C.M. ET AL.: "Noninvasive detection of thin-liquid aspiration using dual-axis swallowing accelerometry", DYSPHAGIA, 28 July 2012 (2012-07-28), pages 105 - 112, XP055072999, Retrieved from the Internet <URL:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576558/pdf/455_2012_Article_9418.pdf> * |
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