Patch-type wearable electrocardiography and impedance pneumography for sleep staging: A multi-modal deep learning approach

Abstract

Sleep staging is critical for investigating sleep quality and detecting disorders. Polysomnography (PSG) remains the gold standard, but is costly and impractical for routine monitoring. This study evaluates the feasibility of a patch-type wearable device using single-lead electrocardiography (ECG) and impedance pneumography (IPG) for multi-stage sleep classification. Data from 92 patients were collected using a wearable ECG-IPG device. Preprocessing entailed bandpass filtering, segmentation into 5-min windows with 30-s overlaps, and feature extraction in time, frequency, and nonlinear domains. Three classification methods were tested and validated using 5-fold patient-independent cross-validation across 2-class (Wake, Sleep), 3-class (Wake, rapid eye movement (REM), and Non-REM), and 4-class (Wake, REM, N1, and N2) tasks. The combined approach achieved the highest accuracy in the 2-class task (accuracy: 83.6%, area under the receiver operating characteristic (AUROC): 86.0%). For 3- and 4-class tasks, feature-based methods outperformed the others, with the RCNN achieving the best F1-score (0.618 in 3-class and 0.552 in 4-class). Modality analysis revealed that IPG + R-R interval (RRI) + motion sensors provided the highest performance, with IPG and RRI identified as the most effective in sleep staging. Feature reduction using maximum relevance and minimum redundancy (mRMR) identified the top 15 features that retained 99% of the performance of the full feature set while reducing the training time by 73%. These findings highlight the feasibility of a portable ECG-IPG system for sleep staging, balancing accuracy and computational efficiency. The proposed approach has the potential to enable continuous sleep monitoring and personalized health management in real-world applications.