TY - GEN
T1 - Detection and Classification of Cardiovascular Disease from Phonocardiogram using Deep Learning Models
AU - Netto, Ann Nita
AU - Abraham, Lizy
N1 - Funding Information:
The first author would like to acknowledge CERD of A.P.J. Abdul Kalam Technological University, Kerala, India for providing PhD fellowship.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/4
Y1 - 2021/8/4
N2 - Cardiovascular disease (CVD) is one of the prime reason for death in India and across the globe. Rural areas of India suffer from shortage of cardiologist and medical facilities. Hence there is a need for the development of an efficient, automated heart disease detection system that can analyse the phonocardiogram to detect the disease. The paper proposes deep learning architectures for anomaly detection from heart sounds. The work classifies the unsegmented phonocardiograms into five classes, four cardiovascular diseases and normal(N). The detected pathological conditions are mitral valve prolapse (MVP), mitral stenosis (MS), mitral regurgitation (MR) and aortic stenosis (AS). Features are extracted using Mel Frequency Cepstral Coefficient (MFCCs) and learning and classification are performed using deep learning methods such as Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and a combination of 1DCNN and LSTM. A total of 1960 phonocardiogram (PCG) segments are used to develop the models with 392 segments in each class. We have achieved an accuracy of 99.1%, 98.2%, 99.4% for CNN, LSTM and 1DCNN-LSTM respectively.
AB - Cardiovascular disease (CVD) is one of the prime reason for death in India and across the globe. Rural areas of India suffer from shortage of cardiologist and medical facilities. Hence there is a need for the development of an efficient, automated heart disease detection system that can analyse the phonocardiogram to detect the disease. The paper proposes deep learning architectures for anomaly detection from heart sounds. The work classifies the unsegmented phonocardiograms into five classes, four cardiovascular diseases and normal(N). The detected pathological conditions are mitral valve prolapse (MVP), mitral stenosis (MS), mitral regurgitation (MR) and aortic stenosis (AS). Features are extracted using Mel Frequency Cepstral Coefficient (MFCCs) and learning and classification are performed using deep learning methods such as Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and a combination of 1DCNN and LSTM. A total of 1960 phonocardiogram (PCG) segments are used to develop the models with 392 segments in each class. We have achieved an accuracy of 99.1%, 98.2%, 99.4% for CNN, LSTM and 1DCNN-LSTM respectively.
KW - Aortic stenosis
KW - Convolutional neural network
KW - Deep learning
KW - Long short term memory
KW - Mel frequency cepstral coefficient
KW - Mitral regurgitation
KW - Mitral stenosis
KW - Mitral valve prolapse
KW - Phonocardiogram
UR - http://www.scopus.com/inward/record.url?scp=85116622118&partnerID=8YFLogxK
U2 - 10.1109/ICESC51422.2021.9532766
DO - 10.1109/ICESC51422.2021.9532766
M3 - Conference contribution
AN - SCOPUS:85116622118
T3 - Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021
SP - 1646
EP - 1651
BT - Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 August 2021 through 6 August 2021
ER -
