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March 6, 2026IEEE Journal of Biomedical and Health Informatics

Revealing Sleep Dynamics with PCT-CRV: A Novel Approach for Automatic Sleep Staging and Tracking Transitions using PSG Signals

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Authors

TZTehreem Fatima ZaidiADAbhishek DixitDJDeepak Joshi

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Overview

Novel approach improves sleep staging and transition tracking in polysomnography, suggesting enhanced monitoring of sleep quality.

Key Points

  • This research aims to develop an effective framework for automatic sleep staging and tracking transitions using PSG signals.
  • Developed a novel framework based on polynomial chirplet transform-derived characteristic response vector (PCT-CRV).
  • Performed time-domain PCT and frequency-domain PCT to enhance time-frequency representation of PSG signals.
  • Constructed correlation matrices and derived local and global features from PCT-CRVs for machine learning classifiers.
  • PCT-CRV outperformed existing methods, including wavelet-based approaches.
  • Successfully tracked transitions between sleep stages using sub-band PCT-CRVs.
  • Demonstrated significant improvements in classification performance on three datasets.

Cite This Study

Zaidi et al. (2026) studied this question.

synapsesocial.com/papers/69aa6f3c531e4c4a9ff5955fhttps://doi.org/10.1109/jbhi.2026.3668939
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