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US 2018/0000403 A1Published application
Method and device for swallowing impairment detection
AssigneeNestec S.A.
InventorsThomas T.K. Chau, Catriona Steele, Ervin Sejdic, Bruno C. Maruzzo
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Abstract
In a method and apparatus for swallowing impairment detection, 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.
Claims26
A device for identifying a possible swallowing impairment in a candidate during execution of a swallowing event, the device comprising:
- an accelerometer configured to acquire axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis of the candidate's throat, the axis-specific vibrational data is representative of the swallowing event;
- and
- a processing module that is a local or remote computing device operatively coupled to the accelerometer, the processing module configured for processing the axis-specific data to (i) extract therefrom distinct axis-specific features representative of the swallowing event, the distinct axis-specific features comprising at least one feature for the A-P axis different than the distinct axis-specific features for the S-I axis, the distinct axis-specific features further comprising at least one feature for the S-I axis different than the distinct axis-specific features extracted for the A-P axis, (ii) perform a comparison of the distinct axis-specific features against preset classification criteria defined for each of swallowing safety and swallowing efficiency, and (iii) classify the vibrational data as one of a first classification or a second classification based on the comparison of the distinct axis-specific features against the preset classification criteria defined for each of the swallowing safety and the swallowing efficiency.
A method for classifying cervical dual-axis accelerometry data acquired in respect of a candidate swallowing event to identify a possible swallowing impairment, the method comprising:
- receiving as input axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, a processing module that is a local or remote computing device operatively coupled to an accelerometer receives the axis-specific vibrational data from the accelerometer;
- extracting distinct axis-specific features representative of the swallowing event from the axis-specific vibrational data, the processing module extracts the distinct axis-specific features, the distinct axis-specific features comprising at least one feature for the A-P axis different than the distinct axis-specific features for the S-I axis, the distinct axis-specific features further comprising at least one feature for the S-I axis different than the distinct axis-specific features extracted for the A-P axis;
- comparing the distinct axis-specific features with preset classification criteria defined as a function of the distinct axis-specific features, the processing module compares the distinct axis-specific features with the preset classification criteria;
- and
- outputting, based on the comparing step, classification of the vibrational data as one of a first classification or a second classification, the processing module outputs the classification.
2 more elements
15:
- The method of claim 10 further comprising filtering that removes one or more candidate movement artifacts from the axis-specific vibrational data and uses wavelets to remove noise from the axis-specific vibrational data.
16:
- The method of claim 10 further comprising removal of one or more candidate movement artifacts from the axis-specific vibrational data by performing a splines-based subroutine on the axis-specific vibrational data.
17:
- A method for identifying a possible swallowing impairment in a candidate by execution of one or more preset swallowing events, the method comprising:
- recording axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis, the axis-specific vibrational data is representative of the one or more preset swallowing events, a processing module that is a local or remote computing device operatively coupled to an accelerometer records the axis-specific vibrational data from the accelerometer;
- extracting distinct axis-specific time-frequency domain features from the axis-specific vibrational data, the processing module extracts the distinct axis-specific time-frequency domain features, the distinct axis-specific time-frequency domain features comprising at least one feature for the A-P axis different than the distinct axis-specific time-frequency domain features for the S-I axis, the distinct axis-specific time-frequency domain features further comprising at least one feature for the S-I axis different than the distinct axis-specific time-frequency domain features extracted for the A-P axis;
- and
- classifying the distinct axis-specific time-frequency domain features as one of a first classification or a second classification, the processing module classifies the distinct axis-specific time-frequency domain features.
2 more elements
18:
- The method of claim 17 further comprising, during the recording step, having the candidate execute the one or more preset swallowing events.
19:
- The method of claim 17 comprising, for execution of two or more preset swallowing events, selectively recording the axis-specific 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.
20:
- The method of claim 17 comprising, for execution of two or more preset swallowing events, successively recording the axis-specific 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.
21:
- The method of claim 17 comprising identifying the candidate as exhibiting a potential swallowing impairment upon classifying the distinct axis-specific time-frequency domain features of at least one of the two or more preset swallowing events as indicative of possible swallowing impairment.
22:
- The method of claim 21, wherein the potential swallowing impairment comprises unsafe swallowing, the method comprising identifying the candidate as a low aspiration risk candidate upon classifying at least three successive swallowing events as not indicative of possible swallowing impairment.
23:
- The method of claim 17, wherein the second classification is indicative of at least one impairment selected from the group consisting of penetration, aspiration, unsafe swallowing and inefficient swallowing.
24:
- The method of claim 17, wherein the second classification is indicative of one or more of unsafe swallowing and inefficient swallowing, the classifying step comprising comparing the distinct axis-specific time-frequency domain features against respective preset classification criteria defined for each of swallowing safety and swallowing efficiency.
25:
- The method of claim 17 further comprising filtering that removes one or more candidate movement artifacts from the axis-specific vibrational data and uses wavelets to remove noise from the axis-specific vibrational data.
26:
- The method of claim 17 further comprising removal of one or more candidate movement artifacts from the axis-specific vibrational data by performing a splines-based subroutine on the axis-specific vibrational data.
Dependencies are inferred from the claim text.