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Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration

机译:基于气管声的呼吸监测:声音术中的连续时间频率处理结合对音乐仪术语呼吸

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摘要

Patients with central respiratory paralysis can benefit from diaphragm pacing to restore respiratory function. However, it would be important to develop a continuous respiratory monitoring method to alert on apnea occurrence, in order to improve the efficiency and safety of the pacing system. In this study, we present a preliminary validation of an acoustic apnea detection method on healthy subjects data. Thirteen healthy participants performed one session of two 2-min recordings, including a voluntary respiratory pause. The recordings were post-processed by combining temporal and frequency detection domains, and a new method was proposed—Phonocardiogram-Derived Respiration (PDR). The detection results were compared to synchronized pneumotachograph, electrocardiogram (ECG), and abdominal strap (plethysmograph) signals. The proposed method reached an apnea detection rate of 92.3%, with 99.36% specificity, 85.27% sensitivity, and 91.49% accuracy. PDR method showed a good correlation of 0.77 with ECG-Derived Respiration (EDR). The comparison of R-R intervals and S-S intervals also indicated a good correlation of 0.89. The performance of this respiratory detection algorithm meets the minimal requirements to make it usable in a real situation. Noises from the participant by speaking or from the environment had little influence on the detection result, as well as body position. The high correlation between PDR and EDR indicates the feasibility of monitoring respiration with PDR.
机译:中枢呼吸麻痹的患者可以从隔膜起搏中受益以恢复呼吸功能。然而,发展连续呼吸监测方法对于警报呼吸暂停发生警报是很重要的,以提高起搏系统的效率和安全性。在这项研究中,我们介绍了对健康受试者数据的声学呼吸暂停检测方法的初步验证。十三个健康的参与者进行了一个两次2分钟的录音,包括自愿呼吸暂停。通过结合时间和频率检测域来处理录制,并提出了一种新方法 - PhoneCardocogram型呼吸(PDR)。将检测结果与同步的肺痘痘,心电图(ECG)和腹带(LethysMograph)信号进行比较。该方法达到呼吸暂停检测率为92.3%,特异性为99.36%,灵敏度为85.27%,精度为91.49%。 PDR方法显示出0.77与ECG衍生的呼吸(EDR)的良好相关性。 R-R间隔和S-S间隔的比较也表明了0.89的良好相关性。这种呼吸检测算法的性能符合最小的要求,使其可以在真实情况下使用。通过讲话或来自环境来源的噪音对检测结果以及机身位置影响不大。 PDR和EDR之间的高相关表明了监测PDR呼吸的可行性。

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