Paper Info
Title
Automatic Cough Detection In Acoustic Signal Using Spectral Features
Abstract
Cough is a common symptom that manifests in numerous respiratory diseases. In chronic respiratory diseases, such as asthma and COPD, monitoring of cough is an integral part in managing the disease. This paper presents an algorithm for automatic detection of cough events from acoustic signals. The algorithm uses only three spectral features with a logistic regression model to separate sound segments into cough and non-cough events. The spectral features were derived using simple calculation from two frequency bands of the sound spectrum. The frequency bands of interest were chosen based on its characteristics in the spectrum. The algorithm achieved high sensitivity of 9031%, specificity of 98.14%, and F1-score of 88.70%. Its low-complexity and high detection performance demonstrate its potential for use in remote patient monitoring systems for real-time, automatic cough detection.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857792
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
COPD,Computer vision,Mel-frequency cepstrum,Pattern recognition,Computer science,Remote patient monitoring,Feature extraction,Frequency spectrum,Artificial intelligence,Hidden Markov model,Radio spectrum
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Renard Xaviero Adhi Pramono100.34
Syed Anas Imtiaz2226.03
Esther O. Rodríguez-Villegas3247.08