arXiv:2508.11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysomnography are costly and impractical for long-term, home-based monitoring. This study presents an energy-efficient approach for detecting four sleep stages (wake, rapid eye movement (REM), light sleep, deep sleep) using a single-lead electrocardiogram (ECG) signal. We evaluate various machine learning and deep learning models, introducing two windowing strategies: (1) a 5-minute window with
Advances in machine learning and deep learning, coupled with the need for more accessible health monitoring, are enabling sophisticated remote diagnostics.
This development allows for scalable, non-invasive, and cost-effective monitoring of critical health indicators like sleep, moving diagnostics from clinics to homes.
Sleep disorder diagnosis and management can become more proactive and continuous, reducing reliance on expensive and inconvenient traditional methods.
- · Med-tech companies
- · Remote monitoring platforms
- · AI developers
- · Patients with sleep disorders
- · Traditional sleep clinics reliant on polysomnography
- · Makers of complex multi-sensor sleep diagnostic equipment
Widespread adoption of single-lead ECG for sleep classification, improving early detection and management of sleep-related health issues.
Integration of similar energy-efficient AI models into consumer wearables, broadening health insights and enabling preventative care.
Reduced healthcare costs associated with sleep disorder diagnosis and treatment, shifting resources to preventative and longitudinal care models.
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