Qurat-Ul-Ain Mubarak1, Muhammad Usman Akram2, Arslan Shaukat2, Farhan Hussain2, Sajid Gul Khawaja2, Wasi Haider Butt2. 1. Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan. Electronic address: quratulain.mubarak242@gmail.com. 2. Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
Abstract
BACKGROUND AND OBJECTIVE: Accurate localization of heart beats in phonocardiogram (PCG) signal is very crucial for correct segmentation and classification of heart sounds into S1 and S2. This task becomes challenging due to inclusion of noise in acquisition process owing to number of different factors. In this paper we propose a system for heart sound localization and classification into S1 and S2. The proposed system introduces the concept of quality assessment before localization, feature extraction and classification of heart sounds. METHODS: The signal quality is assessed by predefined criteria based upon number of peaks and zero crossing of PCG signal. Once quality assessment is performed, then heart beats within PCG signal are localized, which is done by envelope extraction using homomorphic envelogram and finding prominent peaks. In order to classify localized peaks into S1 and S2, temporal and time-frequency based statistical features have been used. Support Vector Machine using radial basis function kernel is used for classification of heart beats into S1 and S2 based upon extracted features. The performance of the proposed system is evaluated using Accuracy, Sensitivity, Specificity, F-measure and Total Error. The dataset provided by PASCAL classifying heart sound challenge is used for testing. RESULTS: Performance of system is significantly improved by quality assessment. Results shows that proposed Localization algorithm achieves accuracy up to 97% and generates smallest total average error among top 3 challenge participants. The classification algorithm achieves accuracy up to 91%. CONCLUSION: The system provides firm foundation for the detection of normal and abnormal heart sounds for cardiovascular disease detection.
BACKGROUND AND OBJECTIVE: Accurate localization of heart beats in phonocardiogram (PCG) signal is very crucial for correct segmentation and classification of heart sounds into S1 and S2. This task becomes challenging due to inclusion of noise in acquisition process owing to number of different factors. In this paper we propose a system for heart sound localization and classification into S1 and S2. The proposed system introduces the concept of quality assessment before localization, feature extraction and classification of heart sounds. METHODS: The signal quality is assessed by predefined criteria based upon number of peaks and zero crossing of PCG signal. Once quality assessment is performed, then heart beats within PCG signal are localized, which is done by envelope extraction using homomorphic envelogram and finding prominent peaks. In order to classify localized peaks into S1 and S2, temporal and time-frequency based statistical features have been used. Support Vector Machine using radial basis function kernel is used for classification of heart beats into S1 and S2 based upon extracted features. The performance of the proposed system is evaluated using Accuracy, Sensitivity, Specificity, F-measure and Total Error. The dataset provided by PASCAL classifying heart sound challenge is used for testing. RESULTS: Performance of system is significantly improved by quality assessment. Results shows that proposed Localization algorithm achieves accuracy up to 97% and generates smallest total average error among top 3 challenge participants. The classification algorithm achieves accuracy up to 91%. CONCLUSION: The system provides firm foundation for the detection of normal and abnormal heart sounds for cardiovascular disease detection.
Authors: Katerina Barnova; Radek Martinek; Rene Jaros; Radana Kahankova; Adam Matonia; Michal Jezewski; Robert Czabanski; Krzysztof Horoba; Janusz Jezewski Journal: PLoS One Date: 2021-08-13 Impact factor: 3.240