Revealing Sleep Dynamics With PCT-CRV: A Novel Approach for Automatic Sleep Staging and Tracking Transitions Using...

Polysomnography (PSG)-based accurate sleep staging is essential to monitor sleep quality and sleep-related disorders. Despite previous attempts for improving the performance of automatic sleep staging, there are certain limitations: 1) neglecting synchronization patterns in their time-frequency (TF) domain, 2) not utilizing both local and global features within sleep epochs, and 3) neglecting correlation patterns for tracking transitions between sleep stages. To address them, we propose a novel framework based on the polynomial chirplet transform-derived characteristic response vector (PCT-CRV) for the assessment of sleep stages. In this work, we perform the time-domain PCT (TPCT) and frequency-domain PCT (FPCT) to enhance the TF representation of nonstationary PSG signals. From these PCT representations, we construct correlation matrices across their frequency bins within short-time windows to obtain characteristic response vectors (CRVs), which are the sums of eigenvectors, weighted by their corresponding eigenvalues. Subsequently, a comprehensive set of local and global features is derived from PCT-CRVs, which is subjected to various machine learning-based classifiers. Our PCT-CRV excels on three datasets, surpassing existing methods, and outperforming wavelet-based and synchrosqueezed-based CRV methods. Furthermore, to track transitions of sleep stages, we form sub-band PCT-CRVs using eigenvectors with maximum information, depending upon the physics of our problem. We hypothesize that sleep stages are characterized by specific correlation profiles, within different frequency bins. Hence, sub-band PCT-CRVs corresponding to the dominant eigenvectors, would detect transition of sleep stages across all epochs. All these results highlight the efficacy of our method in tracking sleep stage transitions and improving their classification performance.