Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare.
Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare.
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DOI：
10.1155/2023/6197564
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发表时间：
2023
影响因子：
--
通讯作者：
Wang, Haibin
中科院分区：
文献类型：
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作者：
Fang, Yu;Liu, Dongbo;Jiang, Zhongwei;Wang, Haibin
睡眠相关呼吸障碍（sbd）会导致睡眠质量下降，增加患心脑血管疾病的风险，严重者可导致死亡。本文旨在通过呼吸声信号检测与sbd相关的呼吸状态。利用力矩波形分析对呼吸周期进行定位和分割。本文提出了一套基于Mel频率倒谱分析的呼吸信号有用特征。最后，通过提取的Mel-scale指数来区分正常和异常的睡眠呼吸状态。年轻健康的测试者和患有阻塞性睡眠呼吸暂停的患者使用所提出的方法进行测试。检测异常呼吸状态的平均准确率可达93.1%。家庭保健有助于预防睡眠不足，提高睡眠质量。
Sleep-related breathing disorders (SBDs) will lead to poor sleep quality and increase the risk of cardiovascular and cerebrovascular diseases which may cause death in serious cases. This paper aims to detect breathing states related to SBDs by breathing sound signals. A moment waveform analysis is applied to locate and segment the breathing cycles. As the core of our study, a set of useful features of breathing signal is proposed based on Mel frequency cepstrum analysis. Finally, the normal and abnormal sleep breathing states can be distinguished by the extracted Mel-scale indexes. Young healthy testers and patients who suffered from obstructive sleep apnea are tested utilizing the proposed method. The average accuracy for detecting abnormal breathing states can reach 93.1%. It will be helpful to prevent SBDs and improve the sleep quality of home healthcare.