Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution: A Comprehensive Study

• URL: http://arxiv.org/abs/2508.11664v1
• Date: Wed, 06 Aug 2025 06:45:30 GMT
• Title: Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution: A Comprehensive Study
• Authors: Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi,
• Abstract summary: Sleep stage classification is crucial for diagnosing and managing disorders such as sleep apnea and insomnia.<n>We present an energy-efficient pipeline that detects four sleep stages from a single-lead ECG.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sleep stage classification is crucial for diagnosing and managing disorders such as sleep apnea and insomnia. Conventional clinical methods like polysomnography are costly and impractical for long-term home use. We present an energy-efficient pipeline that detects four sleep stages (wake, REM, light, and deep) from a single-lead ECG. Two windowing strategies are introduced: (1) a 5-minute window with 30-second steps for machine-learning models that use handcrafted features, and (2) a 30-second window with 10-second steps for deep-learning models, enabling near-real-time 10-second resolution. Lightweight networks such as MobileNet-v1 reach 92 percent accuracy and 91 percent F1-score but still draw significant energy. We therefore design SleepLiteCNN, a custom model that achieves 89 percent accuracy and 89 percent F1-score while lowering energy use to 5.48 microjoules per inference at 45 nm. Applying eight-bit quantization preserves accuracy and further reduces power, and FPGA deployment confirms low resource usage. The proposed system offers a practical solution for continuous, wearable ECG-based sleep monitoring.

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