Apparatus and method for detecting swallowing activity

Abstract

An apparatus and method for detecting swallowing activity is provided. In an embodiment, a method includes receiving an electronic signal from an accelerometer that represents swallowing activity, extracting at least two features from the signal, classifying the signal as a type of swallowing activity based on the extracted features, and generating an output of the classification. Exemplary activities include swallows, aspirations, movement and vocal artifacts. By indicating whether an activity is a swallow or an aspiration, the manner in which a patient afflicted with an increased likelihood for aspirations is fed can be adjusted to increase the likelihood of achieving a swallow instead of an aspiration during feeding. In turn this could reduce hospitalizations for aspiration pneumonia in patients with acute or chronic injury.

1 . A method for detecting swallowing activity comprising the steps of: 
 receiving an electronic signal representing swallowing activity;    extracting at least two features from said signal;    classifying said signal as a type of swallowing activity based on said features; and,    generating an output representing said classification.    
     
     
         2 . The method of  claim 1  wherein said electronic signal is generated by an accelerometer.  
     
     
         3 . The method of  claim 2  wherein said features include at least one of stationarity, normality and dispersion ratio.  
     
     
         4 . The method of  claim 3  wherein said classifying step is performed using a radial basis neural network.  
     
     
         5 . The method of  claim 1  wherein said swallowing activity includes at least one of a swallow and an aspiration.  
     
     
         6 . The method of  claim 2  wherein said extracting step includes stationarity as one of said features, said extracting step of stationarity including the following sub-steps: 
 dividing said signal into a plurality of non-overlapping bins;    determining a total number of total number of reverse arrangements, (A Total ,) in a mean square sequence is determined;    extracting said stationarity feature (z), determined according to the following equation:            z   =         A   Total     -     μ   A         σ   A               where:    μ A  is the mean number of reverse arrangements expected for a stationary signal of the same length.    σ A  is the standard deviation for an equal length stationary signal    
     
     
         7 . The method of  claim 6  wherein each of said bins is between about one ms and about nine ms in length.  
     
     
         8 . The method of  claim 6  wherein each of said bins is between about three ms and about seven ms in length.  
     
     
         9 . The method of  claim 6  wherein each of said bins is about five milliseconds (“ms”) in length.  
     
     
         10 . The method of  claim 2  wherein said extracting step includes normality as one of said features, said extracting step of normality including the following sub-steps: 
 standardizing said signal to have zero mean and unit variance (“s”).    dividing said standardized signal into a plurality of bins (“I”) each of about 0.4 Volts, where              ⌈         max   ⁡     (   s   )       -     min   ⁡     (   s   )         0.4     ⌉     ,           and wherein a highest bin extends to infinity and a lowest bin extends to negative infinity.    determining observed frequencies (“n”) for each said bin by counting the number of samples in the standardized signal (“s”) that fell within each said bin.    determining expected frequencies {circumflex over (m)} for each said bin is determined under the assumption of normality, using a Chi-square (X 2 ) statistic using the following:                X   ^     2     =       ∑     i   =   1     I     ⁢           ⁢         (       n   i     -       m   ^     i       )     2         m   ^     i                 determining said normality feature using the following:      log 10 ({circumflex over (X)} 2 )    
     
     
         11 . The method of  claim 2  wherein said extracting step includes dispersion ratio as one of said features, said extracting step of dispersion ratio including the following sub-steps: 
 determining a mean absolute deviation of said signal according to the following:              S   1     =         1   n     ⁢     ∑     i   =   1     n       ⁢           |       x   i     -     med   ⁡     (   x   )         |             determining an interquartile range, S 2 , of said signal    extracting said dispersion ratio according to the following:              S   1       S   2             
     
     
         12 . A device for detecting swallowing activity comprising: an input device for receiving an electronic signal from a sensor, said electronic signal representing swallowing activity; a microcomputer connected to said input device and operable to extract at least two features from said signal; said microprocessor further operable to classify said signal as a type of swallowing activity based on said features; and, an output device connected to said microcomputer for generating an output representing said classification.  
     
     
         13 . The device  claim 12  wherein said sensor is an accelerometer.  
     
     
         14 . The device of  claim 13  wherein said features include at least one of stationarity, normality and dispersion ratio.  
     
     
         15 . The device of  claim 14  wherein said classifying is performed using a radial basis neural network.  
     
     
         16 . The device of  claim 12  wherein said swallowing activity includes at least one of a swallow and an aspiration.  
     
     
         17 . The device of  claim 13  wherein said extracting includes stationarity as one of said features, said extracting of stationarity including: 
 dividing said signal into a plurality of non-overlapping bins;    determining a total number of total number of reverse arrangements, (A Total ,) in a mean square sequence is determined;    extracting said stationarity feature (z), determined according to the following equation:            z   =         A   Total     -     μ   A         σ   A               where:    μ A  is the mean number of reverse arrangements expected for a stationary signal of the same length.    σ A  is the standard deviation for an equal length stationary signal    
     
     
         18 . The device of  claim 17  wherein each of said bins is between about one ms and about nine ms in length.  
     
     
         19 . The device of  claim 17  wherein each of said bins is between about three ms and about seven ms in length.  
     
     
         20 . The device of  claim 17  wherein each of said bins is about about five milliseconds (“ms”) in length.  
     
     
         21 . The device of  claim 13  wherein said extracting includes normality as one of said features, said extracting of normality including: 
 standardizing said signal to have zero mean and unit variance (“s”).    dividing said standardized signal into a plurality of bins (“I”) each of about 0.4 Volts, where              ⌈         max   ⁡     (   s   )       -     min   ⁡     (   s   )         0.4     ⌉     ,           and wherein a highest bin extends to infinity and a lowest bin extends to negative infinity;    determining observed frequencies (“n”) for each said bin by counting the number of samples in the standardized signal (“s”) that fell within each said bin;    determining expected frequencies {circumflex over (m)} for each said bin is determined under the assumption of normality, using a Chi-square (X 2 ) statistic using the following:                X   ^     2     =       ∑     i   =   1     I     ⁢           ⁢         (       n   i     -       m   ^     i       )     2         m   ^     i                 determining said normality feature using the following:      log 10 ({circumflex over (X)} 2 )    
     
     
         22 . The device of  claim 13  wherein said extracting includes dispersion ratio as one of said features, said dispersion ratio including: 
 determining a mean absolute deviation of said signal according to the following:              S   1     =         1   n     ⁢     ∑     i   =   1     n       ⁢           |       x   i     -     med   ⁡     (   x   )         |             determining an interquartile range, S 2 , of said signal    extracting said dispersion ratio according to the following:              S   1       S   2