Murata Akira, Ohota Nao, Shibuya Atsuo, Ono Hiroshi, Kudoh Shoji
Department of Pulmonary Medicine, Infection and Oncology, Nippon Medical School, Tokyo.
Intern Med. 2006;45(6):391-7. doi: 10.2169/internalmedicine.45.1449. Epub 2006 Apr 17.
Cough consisting of an initial deep inspiration, glottal closure, and an explosive expiration accompanied by a sound is one of the most common symptoms of respiratory disease. Despite its clinical importance, standard methods for objective cough analysis have yet to be established.
We investigated the characteristics of cough sounds acoustically, designed a program to discriminate cough sounds from other sounds, and finally developed a new objective method of non-invasive cough counting. In addition, we evaluated the clinical efficacy of that program.
We recorded cough sounds using a memory stick IC recorder in free-field from 2 patients and analyzed the intensity of 534 recorded coughs acoustically according to time domain. First we squared the sound waveform of recorded cough sounds, which was then smoothed out over a 20 ms window. The 5 parameters and some definitions to discriminate the cough sounds from other noise were identified and the cough sounds were classified into 6 groups. Next, we applied this method to develop a new automatic cough count program. Finally, to evaluate the accuracy and clinical usefulness of this program, we counted cough sounds collected from another 10 patients using our program and conventional manual counting. And the sensitivity, specificity and discriminative rate of the program were analyzed.
This program successfully discriminated recorded cough sounds out of 1902 sound events collected from 10 patients at a rate of 93.1%. The sensitivity was 90.2% and the specificity was 96.5%.
Our new cough counting program can be sufficiently useful for clinical studies.
咳嗽由最初的深呼吸、声门关闭以及伴有声音的爆发性呼气组成,是呼吸系统疾病最常见的症状之一。尽管其在临床上很重要,但客观咳嗽分析的标准方法尚未建立。
我们从声学角度研究咳嗽声音的特征,设计一个程序以将咳嗽声音与其他声音区分开来,并最终开发一种新的无创咳嗽计数客观方法。此外,我们评估了该程序的临床疗效。
我们使用记忆棒式IC录音机在自由声场中记录了2名患者的咳嗽声音,并根据时域对记录的534次咳嗽的强度进行了声学分析。首先,我们对记录的咳嗽声音的波形进行平方,然后在20毫秒的窗口上进行平滑处理。确定了5个参数和一些用于将咳嗽声音与其他噪声区分开的定义,并将咳嗽声音分为6组。接下来,我们应用此方法开发了一个新的自动咳嗽计数程序。最后,为了评估该程序的准确性和临床实用性,我们使用我们的程序和传统的人工计数方法对从另外10名患者收集的咳嗽声音进行计数。并分析了该程序的敏感性、特异性和判别率。
该程序成功地从10名患者收集的1902个声音事件中辨别出记录的咳嗽声音,辨别率为93.1%。敏感性为90.2%,特异性为96.5%。
我们新的咳嗽计数程序对临床研究可能非常有用。