Abstract
BackgroundCough recordings have been undertaken for many years but the analysis of cough frequency and the temporal relation to trigger factors have proven problematic. Because cough is episodic, data collection over many hours is required, along with real-time aural analysis which is equally time-consuming.A method has been developed for the automatic recognition and counting of coughs in sound recordings.MethodsThe Hull Automatic Cough Counter (HACC) is a program developed for the analysis of digital audio recordings. HACC uses digital signal processing (DSP) to calculate characteristic spectral coefficients of sound events, which are then classified into cough and non-cough events by the use of a probabilistic neural network (PNN). Parameters such as the total number of coughs and cough frequency as a function of time can be calculated from the results of the audio processing.Thirty three smoking subjects, 20 male and 13 female aged between 20 and 54 with a chronic troublesome cough were studied in the hour after rising using audio recordings.ResultsUsing the graphical user interface (GUI), counting the number of coughs identified by HACC in an hour long recording, took an average of 1 minute 35 seconds, a 97.5% reduction in counting time. HACC achieved a sensitivity of 80% and a specificity of 96%. Reproducibility of repeated HACC analysis is 100%.ConclusionAn automated system for the analysis of sound files containing coughs and other non-cough events has been developed, with a high robustness and good degree of accuracy towards the number of actual coughs in the audio recording.
Highlights
Cough recordings have been undertaken for many years but the analysis of cough frequency and the temporal relation to trigger factors have proven problematic
Using the graphical user interface (GUI), counting the number of coughs identified by Hull Automatic Cough Counter (HACC) in an hour long recording, took an average of 1 minute 35 seconds, a 97.5% reduction in counting time
HACC achieved a sensitivity of 80% and a specificity of 96%
Summary
Cough recordings have been undertaken for many years but the analysis of cough frequency and the temporal relation to trigger factors have proven problematic. A method has been developed for the automatic recognition and counting of coughs in sound recordings. Whilst the recognition of a single cough event is relatively easy, the assessment of cough frequency over a long period of time remains difficult both for clinical and research purposes. The simple recording of cough sound using a microphone and cassette recorder allows for counting of the cough events, analysis is very time consuming even with the application of sound activated recording or methods for removing silence [7,8,13,14]. Automatic cough recognition from ambulatory multi-channel physiological recordings have been reported [19]. We describe a method for automatic recognition and counting of coughs solely from sound recordings which reduces the processing time and removes the need for trained listeners
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