Martinek J, Tatar M, Javorka M
Department of Pathological Physiology, Jessenius Faculty of Medicine, Comenius University, Martin, Slovakia.
J Physiol Pharmacol. 2008 Dec;59 Suppl 6:433-40.
Objective monitoring of cough sound for extended period is an important step toward a better understanding of this symptom. Because ambulatory cough monitoring systems are not commercially available, we prepared own monitoring system, which is able to distinguish between voluntary cough sound and speech in healthy volunteers. 20-min sound records were obtained using portable digital voice recorder. Characteristics of the sound events have been calculated in time and frequency domains and by a nonlinear analysis. Based on selected parameters, classification tree was constructed for the classification of cough and non-cough sound events. We validated the usefulness of our algorithm developed against manual counts of cough obtained by a trained observer. The median sensitivity value was 100% (the interquartile range was 98-100) and the median specificity was 95% (the interquartile range was 90-97). In conclusion, we developed an algorithm to distinguish between voluntary cough sound and speech with a high degree of accuracy.
长时间客观监测咳嗽声音是更好地理解这一症状的重要一步。由于动态咳嗽监测系统尚无商业产品,我们自行制备了监测系统,该系统能够区分健康志愿者的自主咳嗽声音和言语。使用便携式数字录音机获取20分钟的声音记录。已在时域、频域以及通过非线性分析计算了声音事件的特征。基于选定的参数,构建了分类树以对咳嗽和非咳嗽声音事件进行分类。我们针对由训练有素的观察者手动计数的咳嗽情况,验证了所开发算法的有效性。中位灵敏度值为100%(四分位间距为98 - 100),中位特异性为95%(四分位间距为90 - 97)。总之,我们开发了一种算法,能够高度准确地区分自主咳嗽声音和言语。