CISUC - Voluntary Cough Detection by Internal Sound Analysis
CISUC

Voluntary Cough Detection by Internal Sound Analysis

Authors

Abstract

Cough can be defined as a forced expulsive onrush, normally against a closed glottis, producing a characteristic sound. It can be an indicator of many respiratory diseases, and
its counting and classification is an important aspect. We propose a method on internal sound signal to automatically identify, count and (partly) qualify cough sounds. Our approach relies on explosive phase detection, because of its acoustic and spectral
distinctive characteristics, and its potential for accurate onset detection of cough sounds. The features analyzed, related with tonality, pitch, timbre and frequency, prove to be very relevant in our explosive phase detection approach. Our results show a recall value of 86.6% and a precision value of 84.3%, for a wide testing population with and without respiratory perturbations. The internal sound analysis reveals advantageous in external
noise reduction, therefore internal sounds are highlighted and better characterized.

Subject

Clinical Informatics

Related Project

WELCOME - Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Comorbidities

Conference

7th International Conference on BioMedical Engineering and Informatics – BMEI 2014, October 2014

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Cited by

Year 2016 : 1 citations

 Pramono, R.X.A., Imtiaz, S.A. and Rodriguez-Villegas, E., 2016. A Cough-Based Algorithm for Automatic Diagnosis of Pertussis. PloS one, 11(9), p.e0162128.