Automatic Identification of Cough Events from Acoustic Signals

Cough is a common symptom of numerous respiratory diseases. In certain cases, such as asthma and COPD, early identification of coughs is useful for the management of these diseases. This paper presents an algorithm for automatic identification of cough events from acoustic signals. The algorithm is based on only four features of the acoustic signals including LPC coefficient, tonality index, spectral flatness and spectral centroid with a logistic regression model to label sound segments into cough and non-cough events. The algorithm achieves sensitivity of of 86.78%, specificity of 99.42%, and F1-score of 88.74%. Its high performance despite its small size of feature-space demonstrate its potential for use in remote patient monitoring systems for automatic cough detection using acoustic signals.

[1]  Kevin McGuinness,et al.  P159 Validation of the VitaloJAK™ 24 Hour Ambulatory Cough Monitor , 2012, Thorax.

[2]  Thierry Dutoit,et al.  Audio and Contact Microphones for Cough Detection , 2012, INTERSPEECH.

[3]  David H. Evans,et al.  Detection of cough signals in continuous audio recordings using hidden Markov models , 2006, IEEE Transactions on Biomedical Engineering.

[4]  S. Birring,et al.  ERS guidelines on the assessment of cough , 2007, European Respiratory Journal.

[5]  Huihui Wang,et al.  Novel feature extraction method for cough detection using NMF , 2017, IET Signal Process..

[6]  Renard Xaviero Adhi Pramono,et al.  A Cough-Based Algorithm for Automatic Diagnosis of Pertussis , 2016, PloS one.

[7]  J. D. Johnston,et al.  Estimation of perceptual entropy using noise masking criteria , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[8]  Vinayak Swarnkar,et al.  Automatic cough segmentation from non-contact sound recordings in pediatric wards , 2015, Biomed. Signal Process. Control..

[9]  Pablo Casaseca-de-la-Higuera,et al.  Robust Detection of Audio-Cough Events Using Local Hu Moments , 2019, IEEE Journal of Biomedical and Health Informatics.

[10]  Robert H. Gilman,et al.  Validation of an Automated Cough Detection Algorithm for Tracking Recovery of Pulmonary Tuberculosis Patients , 2012, PloS one.

[11]  Paolo Soda,et al.  An automated and unobtrusive system for cough detection , 2017, 2017 IEEE Life Sciences Conference (LSC).

[12]  Alyn H. Morice,et al.  Objective Measurement of Cough Frequency During COPD Exacerbation Convalescence , 2016, Lung.

[13]  B. Chipps,et al.  Cough in the pediatric population. , 2010, The Journal of pediatrics.

[14]  F. Mccool,et al.  Global physiology and pathophysiology of cough: ACCP evidence-based clinical practice guidelines. , 2006, Chest.

[15]  I. Pavord,et al.  Prevalence, pathogenesis, and causes of chronic cough , 2008, The Lancet.

[16]  N. Team.  Chronic obstructive pulmonary disease in over 16s: diagnosis and management , 2018 .

[17]  Kofi Odame,et al.  Deep Neural Networks for Identifying Cough Sounds , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[18]  Tomasz P. Zielinski,et al.  Joint Application of Audio Spectral Envelope and Tonality Index in an E-Asthma Monitoring System , 2015, IEEE Journal of Biomedical and Health Informatics.

[19]  Zheng Wang,et al.  Cough detection using deep neural networks , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).