Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals of Wearable Piezo-Electric Bands

TL;DR

This study demonstrates that wearable piezo-electric bands capturing thoracic and abdominal movements can effectively detect sleep apnea events, with promising accuracy for clinical and homecare applications.

Contribution

Introduces an adaptive non-harmonic model and feature extraction method for sleep apnea detection using wearable piezo-electric signals, achieving high classification accuracy.

Findings

Overall classification accuracy of ~76-82% for apnea detection.

Combining thoracic and abdominal signals improves accuracy.

State machine achieves ~84% event detection accuracy.

Abstract

Physiologically, the thoracic (THO) and abdominal (ABD) movement signals, captured using wearable piezo-electric bands, provide information about various types of apnea, including central sleep apnea (CSA) and obstructive sleep apnea (OSA). However, the use of piezo-electric wearables in detecting sleep apnea events has been seldom explored in the literature. This study explored the possibility of identifying sleep apnea events, including OSA and CSA, by solely analyzing {one or both the THO and ABD signals. An adaptive non-harmonic model was introduced to model the THO and ABD signals, which allows us to design features for sleep apnea events. To confirm the suitability of the extracted features, a support vector machine was applied to classify three categories -- normal and hypopnea, OSA, and CSA. According to a database of} 34 subjects, the overall classification accuracies were on average $75.9\%\pm 11.7\%$ and $73.8\%\pm 4.4\%$, respectively, based on the cross validation. When the features determined from the THO and ABD signals were combined, the overall classification accuracy became $81.8\%\pm 9.4\%$. These features were applied for designing a state machine for online apnea event detection. Two event-by-event accuracy indices, S and I, were proposed for evaluating the performance {of the state machine. For the same database, the} S index was $84.01\%\pm 9.06\%$, and the I index was $77.21\%\pm 19.01\%$. The results indicate the considerable potential of applying the proposed algorithm to clinical examinations for both screening and homecare purposes.