Authors:
Berke Kizir
and
Beren Semiz
Affiliation:
Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey
Keyword(s):
Seismocardiogram, Respiration Rate, Apnea, Health Monitoring.
Abstract:
Sleep constitutes one-third of human life and plays a critical role in physical repair, mental functioning, and memory consolidation. Although polysomnography (PSG) has been used to assess sleep performance; this test requires participants to visit a sleep clinic and have multiple sensors attached to their bodies. Hence, there is a need for alternative methods which can provide sleep monitoring outside clinical settings, but with clinical standards. In this work, a novel hierarchical framework was built to leverage the seismocardiogram (SCG) signals in apnea detection and respiration pace assessment using a simulated data collection protocol. In the first step of the framework, a binary Light Gradient-Boosting Machine (LGBM) model was trained to detect the breath-holding (apnea) episodes. If the prediction was not a breath-holding state, the data was fed into a multi-class LGBM model to distinguish between normal, slow and fast breathing episodes. Overall, the binary LGBM resulted in
an accuracy, recall, precision and f1-score of 0.99, 0.95, 0.87 and 0.91, respectively; whereas for the multi-class case all metrics were 0.96. Additionally, the optimum window length to achieve real-time detection was determined as 5 seconds. The results show that the SCG signals hold substantial information regarding the changes in breathing patterns, thus could potentially be leveraged in the design of wearable systems as an alternative to the PSG test.
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