METHOD AND DEVICE FOR SWALLOWING IMPAIRMENT DETECTION

• US 20140228714A1
• Filed: 01/18/2012
• Published: 08/14/2014
• Est. Priority Date: 01/18/2011
• Status: Active Grant

• Current Assignee: Holland Bloorview Kids Rehabilitation Hospital, University Health Network
• Original Assignee: Holland Bloorview Kids Rehabilitation Hospital, University Health Network
• Inventors: Chau, Thomas T.K., Maruzzo, Bruno C., Steele, Catriona, Sejdic, Ervin
• Granted Patent: US 9,687,191 B2
• Time in Patent Office: Days
• US Class Current: 600/593
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/1126 using a particular sensing technique
• CPC Class Codes: A61B 5/4205 Evaluating swallowing
• CPC Class Codes: A61B 5/6843 Monitoring or controlling sensor contact pressure
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Disclosed herein is a method and apparatus for swallowing impairment detection, whereby a candidate executes one or more swallowing events and dual axis accelerometry data is acquired representative thereof. Upon feature extraction and classification, vibrational 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.

Claims

1. A device for use in identifying a possible swallowing impairment in a candidate during execution of a swallowing event, the device 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 the accelerometer for processing the axis-specific data to extract therefrom, for each axis, one or more features representative of the swallowing event, and classify the vibrational data as indicative of one of normal swallowing and possibly impaired swallowing based on the extracted features.

2. The device of claim 1, wherein the possible swallowing impairment comprises at least one of a swallowing safety impairment and a swallowing efficiency impairment.

3. The device of claim 1, wherein the possible swallowing impairment comprises penetration or aspiration, the processing module configured to classify the swallowing event as indicative of one of a safe event and an unsafe event.

4. The device of claim 1, wherein the accelerometer is configured for aligned along an anterior-posterior axis (A-P) and a superior-inferior axis (S-I) of the candidate'"'"'s throat.

5. The device of claim 1, wherein the extracted features comprise distinct axis-specific features.

6. The device of claim 1, the features selected from an entropy of the axis-specific data and a log-energy of the axis-specific data.

7. The device of claim 4, the extracted features comprising a log energy of vibrational data acquired along the A-P axis and an entropy of vibrational data acquired along the S-I axis.

8. The device of claim 7, wherein the extracted features comprises a log energy of each level of an 18 level sym8 wavelet decomposition of the vibrational data acquired along the A-P axis, and an entropy of each level of a 12 level sym8 wavelet decomposition of said vibrational data acquired along the S-I axis.

9. The device of claim 8, wherein the extracted features comprises a weighted sum of the log energy of each level of the 18 level sym8 wavelet decomposition, and a weighted sum of the entropy of each level of the 12 level sym8 wavelet decomposition, wherein weights in each the weighted sum are defined by preset feature reduction parameters.

10. The device of claim 1, wherein the processing module is configured to classify the swallowing event by comparing the extracted features with preset classification criteria defined by features previously extracted and classified from a known training data set.

11. The device of claim 10, wherein the extracted features are classified as a function of a distance of the extracted features from the classification criteria.

12. The device of claim 11, wherein the extracted features are classified via discriminant analysis using Mahalanobis distances with stratified covariance estimates.

13. The device of claim 1, wherein the processing module comprises a feature extraction module for extracting the one or more features from the axis-specific data, a feature reduction module for identifying, based on preset feature reduction parameters, predominant components of the extracted features, and a classifier configured to classify the vibrational data based on the extracted features and the predominant components thereof.

14. The device of claim 1, for identifying the possible swallowing impairment during execution of multiple successive swallowing events, the processing module configured to classify vibrational data acquired in respect of each of the successive swallowing events as indicative of one of normal swallowing and possibly impaired swallowing.

15. The device of claim 14, comprising an event segmentation module for automatically segmenting vibrational data acquired in respect of each of the successive events for independent processing and classification.

16. The device of claim 14, comprising a user interface for selectively segmenting vibrational data acquired in respect of each of the successive events for independent processing and classification.

17. The device of claim 14, comprising an output, the processing module further configured to process the vibrational data acquired in respect of each of the successive events, and output results thereof in accordance with a preset swallowing impairment assessment protocol.

40. The device of claim 1, comprising a pressure sensor or pressure sensitive film positioned so to monitor a pressure applied by the accelerometer when positioned on the candidate'"'"'s throat to increase data acquisition accuracy.

18. A method for classifying cervical dual-axis accelerometry data acquired in respect of a candidate swallowing event to identify a possible swallowing impairment, comprising:

receiving as input axis-specific vibrational data representative of the swallowing event;

extracting one or more preset features representative of the swallowing event for each axis of the axis-specific vibrational data;

comparing the extracted features with preset classification criteria defined as a function of the preset features; and

outputting, based on the comparing step, classification of the vibrational data as indicative of one of normal swallowing and possibly impaired swallowing.

19. The method of claim 18, wherein the possibly impaired swallowing comprises one or more of unsafe swallowing and inefficient swallowing.

20. The method of claim 18, wherein the possibly impaired swallowing comprises identification of at least one of a penetration risk and an aspiration risk.

21. The method of claim 18, wherein the preset features comprises distinct axis-specific features.

22. The method of claim 21, wherein the distinct axis-specific features comprises a log-energy of anterior-posterior (A-P) axis data, and an entropy of superior-inferior axis data.

23. The method of claim 22, wherein the extracting step comprises extracting a log-energy of each level of an 18 level sym8 wavelet decomposition of said A-P data, and an entropy of each level of a 12 level sym8 wavelet decomposition of said S-I data.

24. The method of claim 23, comprising reducing the extracted features by associating respective weights to each decomposition level of the extracted features.

25. The method of claim 18, comprising reducing the extracted features.

26. The method of claim 18, wherein the comparing step comprises calculating a distance of the extracted features from the classification criteria and selecting a most likely classification as a function of the distance.

27. The method of claim 26, wherein the calculating step comprises performing a discriminant analysis of the extracted features using Mahalanobis distances with stratified covariance estimates.

28. The method of claim 18, wherein the receiving step comprises receiving as input axis-specific vibrational data representative of successive swallowing events, the method comprising segmenting the vibrational data into event-specific data, and repeating the extracting and classifying steps for each the event-specific data.

29. The method of claim 28, wherein the segmenting step is implemented automatically.

30. The method of claim 28, wherein the outputting step comprises outputting results of the comparing step in accordance with a preset swallowing impairment assessment protocol

31. A computer readable-medium having statements and instructions stored thereon for implementation by a processor to automatically implement a method for classifying cervical dual-axis accelerometry data acquired in respect of a candidate swallowing event to identify a possible swallowing impairment, comprising:

receiving as input axis-specific vibrational data representative of the swallowing event;

extracting one or more preset features representative of the swallowing event for each axis of the axis-specific vibrational data;

comparing the extracted features with preset classification criteria defined as a function of the preset features; and

outputting, based on the comparing step, classification of the vibrational data as indicative of one of normal swallowing and possibly impaired swallowing.

32. A method for identifying a possible swallowing impairment in a candidate via execution of one or more preset swallowing events, comprising:

recording dual-axis vibrational data representative of the one or more swallowing events;

extracting one or more swallowing-event specific features for each axis of the dual-axis vibrational data; and

classifying the extracted features as indicative of one of normal swallowing and possibly impaired swallowing.

33. The method of claim 32, further comprising, during the recording step, having the candidate execute the one or more swallowing events.

34. The method of claim 32, comprising, for execution of two or more preset swallowing events, selectively recording the dual-axis vibrational data for each event independently to provide event-specific data, and implementing the extracting and classifying steps on the event-specific data for each of the events.

35. The method of claim 32, comprising, for execution of two or more preset swallowing events, successively recording the dual-axis vibrational data for each of the events, and comprising automatically segmenting the successively recorded data to provide event-specific data, and implementing the extracting and classifying steps on the event-specific data for each of the events.

36. The method of claim 34, comprising identifying the candidate as exhibiting a potential swallowing impairment upon classifying at least one of the event-specific data as indicative of possible swallowing impairment.

37. The method of claim 34, the swallowing impairment comprising unsafe swallowing, the method comprising identifying the candidate as a low aspiration risk candidate upon classifying at least three successive swallowing events as normal events.

38. The method of claim 32, the swallowing impairment comprising at least one impairment selected from the group consisting of penetration, aspiration, unsafe swallowing and inefficient swallowing.

39. The method of claim 32, the swallowing impairment comprising one or more of unsafe swallowing and inefficient swallowing, the classifying step comprising comparing the extracted features against respective preset classification criteria defined for each of swallowing safety and swallowing efficiency.

41. The method of claim 32, comprising, testing a dual-axis vibrational data quality for consistency with preset quality characteristics.