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Deep learning-based snore sound analysis for the detection of night-time breathing disorders
- 1 San Francisco Bay University
- 2 Computer Information Technology, Northern Arizona University, Flagstaff, AZ, U.S.
- 3 Huacong Qingjiao Information Technology (Beijing) Co., Ltd.
- 4 Stevens Institute of Technology
- 5 San Francisco Bay University
* Author to whom correspondence should be addressed.
Abstract
Snoring, a prevalent symptom of obstructive sleep apnea, is believed to impact 57% of men and 40% of women in the United States. Night-time breathing disorders present significant challenges to both diagnosis and treatment, impacting millions of individuals worldwide. Traditional methods like CPAP machines and lifestyle changes face barriers such as discomfort, low adherence, and high costs, prompting the need for innovative solutions. This paper presents a novel approach using artificial intelligence, specifically deep learning, to create a snore sound analysis-based alerting system. This system aims to detect sleep disorders by analyzing snore patterns, providing a non-intrusive, cost-effective, and user-friendly alternative to traditional methods. By training models on snore sound characteristics, we've achieved promising results in identifying sleep apnea, showcasing the potential of this system in transforming the detection and management of night-time breathing disorders.
Keywords
Deep Learning, Sleep Apnea Detection, Snore Sound Analysis, Artificial Intelligence in Healthcare, Machine Learning
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Cite this article
Dang,B.;Ma,D.;Li,S.;Qi,Z.;Zhu,E.Y. (2024). Deep learning-based snore sound analysis for the detection of night-time breathing disorders. Applied and Computational Engineering,76,109-114.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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