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Performance of a digital signal processing algorithm for the accurate quantification of cough frequency
PDPI Sumatera Utara, 26 Ags 2021 08:18:28


The ability to measure cough frequency from sound recordings has changed the standards by which new cough treatments are evaluated and is providing insights into the mechanisms underlying cough in respiratory disease [1–5]. Objective measures of the number of coughs over extended time periods can be made using off-the-shelf sound recording devices, with aural counting of cough sounds captured. Although excellent inter-observer agreement can be achieved, this process is extremely laborious and limits the size and scope of possible studies [6, 7]; thus, there is a need for more efficient cough quantification methods. Accurate cough detection is, however, complicated by the substantial variability in cough acoustics both within and between individuals. There is also the challenge of distinguishing cough from large amounts of speech and an infinite array of environmental noises that may be captured during ambulatory sound recordings. Indeed, fully automated cough detection systems have failed to achieve sufficient accuracy to be useful, despite apparent success in preliminary tests [8, 9]. A semi-automated algorithm is in use in clinical studies but has only undergone preliminary validation, reporting modest sensitivity in small numbers of individuals (82.3–86%) in recordings made by a now obsolete mp3 player/recorder [10, 11]. The influence of user input, and the robustness of this algorithm to detect cough in different respiratory diseases and in recordings made using different recording devices with different acoustic encoding have not been assessed.

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