非卧床式咳嗽监测和主动咳嗽计数在不同环境下的有效性
2010/08/11
背景：咳嗽是呼吸系统一个重要的防御机制，慢性咳嗽困扰着许多患者，而且可能预示着存在严重疾病。本文主要描述一项新的咳嗽监测和计数技术（(PulmoTrack-CCTM, KarmelSonix, 以色列海法)）的有效性验证过程。
方法：采用PulmoTrack(R)硬件，对健康志愿者仰卧位、坐立、坐立伴较强的周围杂音、行走和爬楼梯时的气管和胸壁声音、周围声音和胸部运动进行数码记录，每名志愿者总共记录25分钟。咳嗽监测程序用于记录数据，以便分析咳嗽及其次数。监测程序通过鉴别带有咳嗽形式的较大声音，而后基于咳嗽的具体特征进行验证。数据均独立记录，专家通过听声音和观看声像图，对声音进行评价，这些专家对该试验不了解。验证基于下述两个方法：(i) 专家共识作为金标准，比较程序记录的咳嗽和专家标识的咳嗽。将这些咳嗽标记为是与否，阳性与阴性。这些值用于评价咳嗽监测系统的特异性和敏感性；(ii) 更长的时间内记录咳嗽次数（t = 60秒，n = 300）将程序产生的咳嗽计数和专家的咳嗽计数进行作图，形成线性回归方程，计算回归系数（R2）和Bland-Altman图联合分步密度。
结果：共有12名志愿者完成整个项目。咳嗽检测的总体特异性为94％，敏感性为96％，所有环境下检测值相似，除了爬楼梯，该情况下特异性为85％，敏感性为97％。PulmoTrack-CCTM咳嗽计数与专家咳嗽计数之间的回归方程，其回归系数为0.94％。
结论：此项验证在一系列实际情况下，对咳嗽计数程序进行了客观的、定量的评价，这些情况模拟了咳嗽的非卧床式检测。不同声音环境下检测自主咳嗽在临床咳嗽检测中具有重要作用，该程序可能作为一种自动咳嗽检测仪。
（陈欣 审校）
Cough. 2010 May 27;6(1):3. [Epub ahead of print]
Validation of an ambulatory cough detection and counting application using voluntary cough under different conditions.
Vizel E, Yigla M, Goryachev Y, Dekel E, Felis V, Levi H, Kroin I, Godfrey S, Gavriely N.
Abstract
BACKGROUND: While cough is an important defence mechanism of the respiratory system, its chronic presence is bother some and may indicate the presence of a serious disease. We hereby describe the validation process of a novel cough detection and counting technology (PulmoTrack-CCTM, KarmelSonix, Haifa, Israel).
METHODS: Tracheal and chest wall sounds, ambient sounds and chest motion were digitally recorded, using the PulmoTrack(R) hardware, from healthy volunteers coughing voluntarily while (a) laying supine, (b) sitting, (c) sitting with strong ambient noise, (d) walking, and (e) climbing stairs, a total of 25 minutes per subject. The cough monitoring algorithm was applied to the recorded data to detect and count coughs. The detection algorithm first searches for cough ’candidates’ by identifying loud sounds with a cough pattern, followed by a secondary verification process based on detection of specific characteristics of cough. The recorded data were independently and blindly evaluated by trained experts who listened to the sounds and visually reviewed them on a sonogram display. The validation process was based on two methods: (i) Referring to an expert consensus as gold standard, and comparing each cough detected by the algorithm to the expert marking, we marked True and False, positive and negative detections. These values were used to evaluate the specificity and sensitivity of the cough monitoring system. (ii) Counting the number of coughs in longer segments (t = 60 sec, n = 300) and plotting the cough count vs. the corresponding experts’ count whereby the linear regression equation, the regression coefficient (R2) and the joint-distribution density Bland-Altman plots could be determined.
RESULTS: Data were recorded from 12 volunteers undergoing the complete protocol. The overall Specificity for cough events was 94% and the Sensitivity was 96%, with similar values found for all conditions, except for the stair climbing stage where the Specificity was 87% with Sensitivity of 97%. The regression equation between the PulmoTrack-CCTM cough event counts and the Experts’ determination was with R2 of 0.94.
DISCUSSION: This validation scheme provides an objective and quantitative assessment method of a cough counting algorithm in a range of realistic situations that simulate ambulatory monitoring of cough. The ability to detect voluntary coughs under acoustically challenging ambient conditions may represent a useful step towards a clinically applicable automatic cough detector.
Cough. 2010 May 27;6(1):3. [Epub ahead of print]
方法：采用PulmoTrack(R)硬件，对健康志愿者仰卧位、坐立、坐立伴较强的周围杂音、行走和爬楼梯时的气管和胸壁声音、周围声音和胸部运动进行数码记录，每名志愿者总共记录25分钟。咳嗽监测程序用于记录数据，以便分析咳嗽及其次数。监测程序通过鉴别带有咳嗽形式的较大声音，而后基于咳嗽的具体特征进行验证。数据均独立记录，专家通过听声音和观看声像图，对声音进行评价，这些专家对该试验不了解。验证基于下述两个方法：(i) 专家共识作为金标准，比较程序记录的咳嗽和专家标识的咳嗽。将这些咳嗽标记为是与否，阳性与阴性。这些值用于评价咳嗽监测系统的特异性和敏感性；(ii) 更长的时间内记录咳嗽次数（t = 60秒，n = 300）将程序产生的咳嗽计数和专家的咳嗽计数进行作图，形成线性回归方程，计算回归系数（R2）和Bland-Altman图联合分步密度。
结果：共有12名志愿者完成整个项目。咳嗽检测的总体特异性为94％，敏感性为96％，所有环境下检测值相似，除了爬楼梯，该情况下特异性为85％，敏感性为97％。PulmoTrack-CCTM咳嗽计数与专家咳嗽计数之间的回归方程，其回归系数为0.94％。
结论：此项验证在一系列实际情况下，对咳嗽计数程序进行了客观的、定量的评价，这些情况模拟了咳嗽的非卧床式检测。不同声音环境下检测自主咳嗽在临床咳嗽检测中具有重要作用，该程序可能作为一种自动咳嗽检测仪。
（陈欣 审校）
Cough. 2010 May 27;6(1):3. [Epub ahead of print]
Validation of an ambulatory cough detection and counting application using voluntary cough under different conditions.
Vizel E, Yigla M, Goryachev Y, Dekel E, Felis V, Levi H, Kroin I, Godfrey S, Gavriely N.
Abstract
BACKGROUND: While cough is an important defence mechanism of the respiratory system, its chronic presence is bother some and may indicate the presence of a serious disease. We hereby describe the validation process of a novel cough detection and counting technology (PulmoTrack-CCTM, KarmelSonix, Haifa, Israel).
METHODS: Tracheal and chest wall sounds, ambient sounds and chest motion were digitally recorded, using the PulmoTrack(R) hardware, from healthy volunteers coughing voluntarily while (a) laying supine, (b) sitting, (c) sitting with strong ambient noise, (d) walking, and (e) climbing stairs, a total of 25 minutes per subject. The cough monitoring algorithm was applied to the recorded data to detect and count coughs. The detection algorithm first searches for cough ’candidates’ by identifying loud sounds with a cough pattern, followed by a secondary verification process based on detection of specific characteristics of cough. The recorded data were independently and blindly evaluated by trained experts who listened to the sounds and visually reviewed them on a sonogram display. The validation process was based on two methods: (i) Referring to an expert consensus as gold standard, and comparing each cough detected by the algorithm to the expert marking, we marked True and False, positive and negative detections. These values were used to evaluate the specificity and sensitivity of the cough monitoring system. (ii) Counting the number of coughs in longer segments (t = 60 sec, n = 300) and plotting the cough count vs. the corresponding experts’ count whereby the linear regression equation, the regression coefficient (R2) and the joint-distribution density Bland-Altman plots could be determined.
RESULTS: Data were recorded from 12 volunteers undergoing the complete protocol. The overall Specificity for cough events was 94% and the Sensitivity was 96%, with similar values found for all conditions, except for the stair climbing stage where the Specificity was 87% with Sensitivity of 97%. The regression equation between the PulmoTrack-CCTM cough event counts and the Experts’ determination was with R2 of 0.94.
DISCUSSION: This validation scheme provides an objective and quantitative assessment method of a cough counting algorithm in a range of realistic situations that simulate ambulatory monitoring of cough. The ability to detect voluntary coughs under acoustically challenging ambient conditions may represent a useful step towards a clinically applicable automatic cough detector.
Cough. 2010 May 27;6(1):3. [Epub ahead of print]
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