Adhi Pramono Renard Xaviero, Anas Imtiaz Syed, Rodriguez-Villegas Esther
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:7153-7156. doi: 10.1109/EMBC.2019.8857792.
Cough is a common symptom that manifests in numerous respiratory diseases. In chronic respiratory diseases, such as asthma and COPD, monitoring of cough is an integral part in managing the disease. This paper presents an algorithm for automatic detection of cough events from acoustic signals. The algorithm uses only three spectral features with a logistic regression model to separate sound segments into cough and non-cough events. The spectral features were derived using simple calculation from two frequency bands of the sound spectrum. The frequency bands of interest were chosen based on its characteristics in the spectrum. The algorithm achieved high sensitivity of 90.31%, specificity of 98.14%, and F1-score of 88.70%. Its low-complexity and high detection performance demonstrate its potential for use in remote patient monitoring systems for real-time, automatic cough detection.
咳嗽是许多呼吸道疾病中常见的症状。在慢性呼吸道疾病，如哮喘和慢性阻塞性肺疾病中，咳嗽监测是疾病管理的一个组成部分。本文提出了一种从声学信号中自动检测咳嗽事件的算法。该算法仅使用三个频谱特征和一个逻辑回归模型，将声音片段分为咳嗽和非咳嗽事件。频谱特征是通过对声谱的两个频段进行简单计算得出的。感兴趣的频段是根据其在频谱中的特征选择的。该算法实现了90.31%的高灵敏度、98.14%的特异性和88.70%的F1分数。其低复杂度和高检测性能表明了它在远程患者监测系统中用于实时自动咳嗽检测的潜力。
