Temporally Continuous Automated Sleep-Wake Classification Using Deep Learning.

ABSTRACT:

Introduction

Segmenting sleep into fixed 30-second epochs remains central to current sleep scoring practice, yet it imposes rigid boundaries that may not accurately reflect the true temporal sleep dynamics. We aimed to develop a deep learning-based, high-temporal-resolution sleep-wake classifier leveraging temporally continuous manual reference scoring without fixed epoch boundaries and transfer learning techniques to facilitate progress toward a more physiologically consistent sleep assessment.

Methods

Three independent datasets were utilized, of which two included sleep-wake scoring manually conducted in a temporally continuous manner. A U-Net based model was initially trained on a large dataset scored using 30-second epochs, with post hoc scoring modifications (n=2

SUBMITTER: Somaskandhan P 

PROVIDER: S-EPMC12704636 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature