Zahra Mohammadi

Zahra Mohammadi, Siamak Mohammadi

Apnea is a common sleep disorder characterized by breathing interruptions lasting at least ten seconds and occurring more than five times per hour. Accurate, high-temporal-resolution detection of sleep apnea subtypes - Obstructive, Central, and Mixed - is crucial for effective treatment and management. This paper presents an energy-efficient method for classifying these subtypes using a single-lead electrocardiogram (ECG) with high temporal resolution to address the real-time needs of wearable devices. We evaluate a wide range of classical machine learning algorithms and deep learning architectures on 1-second ECG windows, comparing their accuracy, complexity, and energy consumption. Based on this analysis, we introduce SleepLiteCNN, a compact and energy-efficient convolutional neural network specifically designed for wearable platforms. SleepLiteCNN achieves over 95% accuracy and a 92% macro-F1 score, while requiring just 1.8 microjoules per inference after 8-bit quantization. Field Programmable Gate Array (FPGA) synthesis further demonstrates significant reductions in hardware resource usage, confirming its suitability for continuous, real-time monitoring in energy-constrained environments. These results establish SleepLiteCNN as a practical and effective solution for wearable device sleep apnea subtype detection.

Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi

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.