Quality Aware Sleep Stage Classification over RIP Signals with Persistence Diagrams

Automated sleep stage classification is a valuable tool for analyzing sleep patterns and has numerous applications in wearable healthcare systems. However, the accuracy of sleep stage classification using signals from wearable devices can be affected by data quality issues such as signal interference or packet loss. In this study, we present an algorithm that addresses packet loss in respiratory inductive plethysmography (RIP) signals for sleep stage detection. RIP signals can be conveniently collected using abdominal and thoracic belts. By exploring the rich structural patterns in such signals, we utilize persistence diagrams to uncover macro-structures for sleep stage classification, which is particularly suitable for high data missing rates. Our model achieves a promising performance of 76% accuracy and a 0.54 Cohen's kappa coefficient for three-stage classification. Additionally, we evaluate the model across different missing data rates and highlight the superior fault tolerance of persistence diagram features compared to other conventional temporal and spectral features.