Automated prediction of apnea-hypopnea index using wearable devices

Abstract

A method and system for determining Apnea-Hypopnea Index (AHI) values are disclosed. The method comprises determining at least one sensor stream using at least one detected physiological signal, and processing the at least one sensor stream to automatically determine the AHI values. The system includes a sensor to detect at least one physiological signal, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine at least one sensor stream using at least one detected physiological signal and to process the at least one sensor stream to automatically determine the AHI values.

What is claimed is: 
     
         1 . A method for determining Apnea-Hypopnea Index (AHI) values, the method comprising:
 determining at least one sensor stream using at least one detected physiological signal; and   processing the at least one sensor stream to automatically determine the AHI values.   
     
     
         2 . The method of  claim 1 , wherein the at least one detected physiological signal is detected by a wearable device and includes any of an ECG signal and an accelerometer signal. 
     
     
         3 . The method of  claim 2 , wherein the determining step is performed by an electronic module of the wearable device, further wherein the at least one sensor stream includes any of an RR interval, an amplitude of the QRS waveform (RWA), an area of the QRS waveform (RA), a MEMS derived respiration signal, a signal magnitude area (SMA) of an accelerometer signal, and a posture angle. 
     
     
         4 . The method of  claim 1 , wherein the processing step further comprises:
 preprocessing the at least one sensor stream;   performing feature extraction on the at least one preprocessed sensor stream to provide a feature vector; and   performing machine learning optimization using the feature vector.   
     
     
         5 . The method of  claim 1 , wherein the processing step is performed by any of a wearable device, an external device, a relay/cloud processor, a smartphone device, and a cloud computing system. 
     
     
         6 . The method of  claim 4 , wherein the preprocessing step further comprises any of eliminating wearable device off instances, removing trends, detecting and removing outliers, detecting and removing artifacts, normalization of ECG derived respiration signals, and normalization of MEMS derived respiration signals. 
     
     
         7 . The method of  claim 4 , wherein the performing feature extraction step utilizes any of time-domain analysis, statistical analysis, nonlinear analysis, frequency-domain analysis, and posture analysis to extract features from the at least one preprocessed sensor stream. 
     
     
         8 . The method of  claim 4 , further comprising:
 determining the feature vector using a combination of the feature extraction and patient information.   
     
     
         9 . The method of  claim 4 , wherein the performing machine learning optimization step utilizes the feature vector and an optimized classifier model to perform epoch classification and to determine a number of epochs with events per hour (EPH). 
     
     
         10 . The method of  claim 9 , further comprising:
 performing regression analysis to map the EPH to the determined AHI values; and   minimizing mean square error (MSE) of the determined AHI values using leave-one-out cross-validation (LOOCV).   
     
     
         11 . A wearable device for determining Apnea-Hypopnea Index (AHI) values, the wearable device comprising a sensor to detect at least one physiological signal, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to:
 convert the at least one detected physiological signal into at least one sensor stream; and   process the at least one sensor stream to automatically determine the AHI values.   
     
     
         12 . The wearable device of  claim 11 , wherein the at least one physiological signal includes any of an ECG signal and an accelerometer signal. 
     
     
         13 . The wearable device of  claim 12 , wherein the at least one sensor stream includes any of an RR interval, an amplitude of the QRS waveform (RWA), an area of the QRS waveform (RA), a MEMS derived respiration signal, a signal magnitude area (SMA) of an accelerometer signal, and a posture angle. 
     
     
         14 . The wearable device of  claim 11 , wherein to process further comprises to:
 perform preprocessing on the at least one sensor stream;   perform feature extraction on the at least one preprocessed sensor stream to provide a feature vector; and   perform machine learning optimization using the feature vector.   
     
     
         15 . The wearable device of  claim 14 , wherein any of the preprocessing, feature extraction, and machine learning optimization is performed by a processor external to the wearable device, wherein the external processor includes any of an external device, a relay/cloud processor, a smartphone device, and a cloud computing system. 
     
     
         16 . The wearable device of  claim 14 , wherein the preprocessing further comprises any of eliminating wearable device off instances, removing trends, detecting and removing outliers, detecting and removing artifacts, normalization of ECG derived respiration signals, and normalization of MEMS derived respiration signals. 
     
     
         17 . The wearable device of  claim 14 , wherein the feature extraction utilizes any of time-domain analysis, statistical analysis, nonlinear analysis, frequency-domain analysis, and posture analysis to extract features from the at least one preprocessed sensor stream. 
     
     
         18 . The wearable device of  claim 14 , wherein the application, when executed by the processor, further causes the processor to:
 determine the feature vector using a combination of the feature extraction and patient information.   
     
     
         19 . The wearable device of  claim 14 , wherein the machine learning optimization utilizes the feature vector and an optimized classifier model to perform epoch classification and to determine a number of epochs with events per hour (EPH). 
     
     
         20 . The wearable device of  claim 1 , wherein the application, when executed by the processor, further causes the processor to:
 perform regression analysis to map the EPH to the determined AHI values; and   minimize mean square error (MSE) of the determined AHI values using leave-one-out cross-validation (LOOCV).