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

arXiv: https://arxiv.org/abs/2508.02718 · HTML: https://mlsanity.com/paper/2508.02718/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors](https://mlsanity.com/paper/2105.00528/) (2021)
- [SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches](https://mlsanity.com/paper/2108.11468/) (2021)

## Built on by (most cited first, 1 of 1)

- [Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution](https://mlsanity.com/paper/2508.11664/) (2025)

## Abstract (arXiv)

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.