Paper Info
Title
A Robust Dataset-Agnostic Heart Disease Classifier From Phonocardiogram
Abstract
Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature set for dataset-agnostic classification of normal and cardiac patients. The large and open access database, made available in Physionet 2016 challenge was used for feature selection, internal validation and creation of training models. A second dataset of 41 PCG segments, collected using our in-house smart phone based digital stethoscope from an Indian hospital was used for performance evaluation. Our proposed methodology yielded sensitivity and specificity scores of 0.76 and 0.75 respectively on the test dataset in classifying cardiovascular diseases. The methodology also outperformed three popular prior art approaches, when applied on the same dataset.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037876
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Frequency domain,Phonocardiogram,Stethoscope,Feature selection,Pattern recognition,Computer science,Signal quality,Artificial intelligence,Classifier (linguistics),Discriminative model,Heart disease
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
1
0.41
References 
Authors
2
6
Name
Order
Citations
PageRank
Rohan Banerjee14512.28
Anirban Dutta Choudhury27517.66
Parijat Deshpande3114.10
Sakyajit Bhattacharya423.48
Arpan Pal519551.41
K. M. Mandana672.80