The automatic recognition and counting of cough
Samantha J Barry1, Adrie D Dane, Alyn H Morice
1Department of Chemistry, Faculty of Science and the Environment, University of Hull, Cottingham Road, Hull, HU6 7RX, UK. s.j.barry@chem.hull.ac.uk
Cough (London, England)
|September 30, 2006
Summary
A new automated system, the Hull Automatic Cough Counter (HACC), accurately counts coughs in audio recordings. This significantly reduces analysis time, making cough frequency studies more efficient.
Area of Science:
- Medical Technology
- Digital Signal Processing
- Artificial Intelligence in Healthcare
Background:
- Traditional cough analysis is time-consuming and problematic due to the episodic nature of coughs.
- Manual analysis of prolonged cough recordings requires extensive real-time aural assessment.
- A need exists for automated methods to quantify cough frequency and temporal patterns.
Purpose of the Study:
- To develop and validate an automated system for recognizing and counting cough events in digital audio recordings.
- To assess the accuracy, efficiency, and reproducibility of the automated cough counting method.
Main Methods:
- The Hull Automatic Cough Counter (HACC) program utilizes digital signal processing (DSP) and a probabilistic neural network (PNN).
- HACC analyzes spectral coefficients of sound events to classify them as cough or non-cough.
- The system was tested on audio recordings from 33 subjects with chronic cough.
Main Results:
- HACC significantly reduced manual counting time by 97.5%, with an average analysis time of 1 minute 35 seconds per hour recording.
- The system demonstrated a sensitivity of 80% and a specificity of 96% for cough detection.
- Reproducibility of HACC analysis was found to be 100%.
Conclusions:
- An automated system for cough event analysis has been successfully developed.
- The Hull Automatic Cough Counter (HACC) offers high robustness and accuracy in quantifying coughs.
- This automated approach streamlines the analysis of cough frequency and temporal relationships.
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