High-precision snore detection method based on deep learning

Wenjin Liu; Shudong Zhang; Lijuan Zhou

doi:10.1117/12.2660747

16 December 2022 High-precision snore detection method based on deep learning

Wenjin Liu, Shudong Zhang, Lijuan Zhou

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 1250062 (2022) https://doi.org/10.1117/12.2660747
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a sleep-related respiratory disease, and sleep snoring is its most common and direct feature. However, the current snoring detection methods require a lot of medical manpower and medical equipment resources, resulting in many OSAHS patients cannot be treated in time. Therefore, this paper proposes a snore detection method based on deep learning and a snore dataset. The detection method first calculates the time-domain waveform, spectrogram, and Mel-spectrogram for each audio segment in the snore dataset. Then, the snore is recognized by convolution neural network. To better apply this method to mobile devices and intelligent devices, MobileNetV2 is selected as the detection network to classify snoring and non-snoring images. The experimental results show that the proposed method can accurately recognize snores with 95.00% accuracy. And the spectrogram can better reflect the difference between snoring and non-snoring images.

Citation Download Citation

Wenjin Liu, Shudong Zhang, and Lijuan Zhou "High-precision snore detection method based on deep learning", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 1250062 (16 December 2022); https://doi.org/10.1117/12.2660747

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