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Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals

Abstract: 

In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of substantial noise does not guarantee highly effective analysis of cardiac condition. Our proposed method intelligently identifies and eliminates noisy PCG signal and consequently detects pathological abnormality condition. We further present a unified model of hybrid feature selection method. Our feature selection model is diversity optimized and cost-sensitive over conditional likelihood of the training and validation examples that maximizes classification model performance. We employ multi-stage hybrid feature selection process involving first level filter method and second level wrapper method. We achieve 85% detection accuracy by using publicly available MIT-Physionet challenge 2016 datasets consisting of more than 3000 annotated PCG signals.

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Paper Details

Authors:
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal
Submitted On:
27 April 2018 - 2:44am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Chetanya Puri
Paper Code:
BISP-L3.6
Document Year:
2018
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ICASSP_Paper_2030_final.pdf

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[1] Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, "Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3184. Accessed: May. 20, 2018.
@article{3184-18,
url = {http://sigport.org/3184},
author = {Arijit Ukil; Soma Bnadyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal },
publisher = {IEEE SigPort},
title = {Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals},
year = {2018} }
TY - EJOUR
T1 - Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals
AU - Arijit Ukil; Soma Bnadyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3184
ER -
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. (2018). Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals. IEEE SigPort. http://sigport.org/3184
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, 2018. Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals. Available at: http://sigport.org/3184.
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. (2018). "Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals." Web.
1. Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3184