Validation of an ambulatory cough detection and counting application using voluntary cough under different conditions

Abstract

Abstract Background While cough is an important defence mechanism of the respiratory system, its chronic presence is bothersome and may indicate the presence of a serious disease. We hereby describe the validation process of a novel cough detection and counting technology (PulmoTrack-CC™, KarmelSonix, Haifa, Israel). Methods Tracheal and chest wall sounds, ambient sounds and chest motion were digitally recorded, using the PulmoTrack® 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 (R 2) 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-CC™ cough event counts and the Experts' determination was with R 2 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.