Guntupalli Kalpalatha K, Alapat Philip M, Bandi Venkata D, Kushnir Igal
Baylor College of Medicine, Ben Taub General Hospital, 1504 Taub Loop, Houston, Texas 77030, USA.
J Asthma. 2008 Dec;45(10):903-7. doi: 10.1080/02770900802386008.
Computerized lung-sound analysis is a sensitive and quantitative method to identify wheezing by its typical pattern on spectral analysis. We evaluated the accuracy of the VRI, a multi-sensor, computer-based device with an automated technique of wheeze detection. The method was validated in 100 sound files from seven subjects with asthma or chronic obstructive pulmonary disease and seven healthy subjects by comparison of auscultation findings, examination of audio files, and computer detection of wheezes. Three blinded physicians identified 40 sound files with wheezes and 60 sound files without wheezes. Sensitivity and specificity were 83% and 85%, respectively. Negative predictive value and positive predictive value were 89% and 79%, respectively. Overall inter-rater agreement was 84%. False positive cases were found to contain sounds that simulate wheezes, such as background noises with high frequencies or strong noises from the throat that could be heard and identified without a stethoscope. The present findings demonstrate that the wheeze detection algorithm has good accuracy, sensitivity, specificity, negative predictive value and positive predictive value for wheeze detection in regional analyses with a single sensor and multiple sensors. Results are similar to those reported in the literature. The device is user-friendly, requires minimal patient effort, and, distinct from other devices, it provides a dynamic image of breath sound distribution with wheeze detection output in less than 1 minute.
计算机化肺音分析是一种通过频谱分析中典型模式来识别哮鸣音的灵敏且定量的方法。我们评估了VRI的准确性,这是一种基于计算机的多传感器设备,具有自动检测哮鸣音的技术。通过比较听诊结果、检查音频文件以及计算机检测哮鸣音,该方法在来自7名哮喘或慢性阻塞性肺疾病患者和7名健康受试者的100个声音文件中得到了验证。三名不知情的医生识别出40个有哮鸣音的声音文件和60个无哮鸣音的声音文件。敏感性和特异性分别为83%和85%。阴性预测值和阳性预测值分别为89%和79%。总体评分者间一致性为84%。发现假阳性病例包含模拟哮鸣音的声音,如高频背景噪音或无需听诊器就能听到并识别的来自喉咙的强烈噪音。目前的研究结果表明,哮鸣音检测算法在单传感器和多传感器的区域分析中对哮鸣音检测具有良好的准确性、敏感性、特异性、阴性预测值和阳性预测值。结果与文献报道的相似。该设备使用方便,患者只需付出 minimal effort,并且与其他设备不同,它能在不到1分钟内提供带有哮鸣音检测输出的呼吸音分布动态图像。