SleepLiteCNN: Energy-Efficient Sleep Apnea Subtype Classification with 1-Second Resolution Using Single-Lead ECG

• URL: http://arxiv.org/abs/2508.02718v1
• Date: Fri, 01 Aug 2025 00:04:40 GMT
• Title: SleepLiteCNN: Energy-Efficient Sleep Apnea Subtype Classification with 1-Second Resolution Using Single-Lead ECG
• Authors: Zahra Mohammadi, Siamak Mohammadi,
• Abstract summary: Sleep apnea is a common sleep disorder characterized by breathing interruptions lasting at least ten seconds and occurring more than five times per hour.<n>This paper presents an energy-efficient method for classifying sleep apnea subtypes using a single-lead electrocardiogram (ECG) with high temporal resolution.<n>We introduce SleepLiteCNN, a compact and energy-efficient convolutional neural network specifically designed for wearable platforms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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