心跳
Mel倒谱
人工神经网络
睡眠呼吸暂停
计算机科学
睡眠(系统调用)
语音识别
特征提取
脑电图
呼吸暂停
模式识别(心理学)
呼吸不足
人工智能
听力学
医学
多导睡眠图
心理学
麻醉
神经科学
计算机安全
操作系统
作者
Yan Shang,Bin Guo,Zijun Zhao
出处
期刊:Journal of physics
[IOP Publishing]
日期:2023-11-01
卷期号:2637 (1): 012007-012007
标识
DOI:10.1088/1742-6596/2637/1/012007
摘要
Abstract Sleep apnea hypopnea syndrome (OSAHS) is a high-incidence disease with serious harm and potential dangers. Currently, the traditional scheme for monitoring sleep quality mainly focuses on monitoring two physiological signals: electroencephalogram (EEG) and heartbeat. However, in the sleep state, respiration is also an important physiological signal. This paper proposes a sleep apnea detection method based on snoring sound analysis using deep learning. Firstly, snoring sound signals are preprocessed and feature extraction is performed using Mel-frequency cepstral coefficients (MFCC). The extracted features are then used to train a DS-MS neural network model, and the optimal detection model is obtained through iterations. The experimental results show that the accuracy of the proposed detection model can reach 94.17%.
科研通智能强力驱动
Strongly Powered by AbleSci AI