Article: An efficient AR modelling-based electrocardiogram signal analysis for health informatics Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2022 Vol.14 No.1 pp.74 - 89 Abstract: Today, health informatics not only requires correct but also timely diagnosis much before the occurrence of critical stage of the underlying disease. Electrocardiogram (ECG) is one such non-invasive diagnostic tool to establish an efficient computer-aided diagnosis (CAD) system. In this paper, autoregressive (AR) modelling is proposed that is an efficient technique to process ECG signals by estimating its coefficients. In this paper, two parameters viz. atrial tachycardia (AT) and premature atrial contractions (PAC) are considered for evaluating the performance of the proposed methodology for a total of 17 recordings (6 real time and 11 from MIT-BIH arrhythmia database). As compared to K-nearest neighbour (KNN) and principal component analysis (PCA) with AR modelling [also known as Yule-Walker (YW) and Burg method], KNN classifier coupled with Burg method (i.e., Burg + KNN) yielded good results at model order 9. A sensitivity (Se) of 99.95%, specificity (Sp or PPV) of 99.97%, detection error rate (DER) of 0.071%, accuracy (Acc) of 99.93% and mean time discrepancy (MTD) of 0.557 msec are obtained. Consistent higher values of all the performance parameters can lead to the development of an autonomous CAD tool for timely detection of heart diseases as required in health informatics. Inderscience Publishers - linking academia, business and industry through research

Title: An efficient AR modelling-based electrocardiogram signal analysis for health informatics

Authors: Varun Gupta; Monika Mittal; Vikas Mittal; Anshu Gupta

Addresses: KIET Group of Institutions, Delhi-NCR, Ghaziabad, UP, India ' EE Department, National Institute of Technology, Kurukshetra-136119, Haryana, India ' ECE Department, National Institute of Technology, Kurukshetra-136119, Haryana, India ' Education Department, K.L.P.G. College, Meerut-250001, UP, India

Abstract: Today, health informatics not only requires correct but also timely diagnosis much before the occurrence of critical stage of the underlying disease. Electrocardiogram (ECG) is one such non-invasive diagnostic tool to establish an efficient computer-aided diagnosis (CAD) system. In this paper, autoregressive (AR) modelling is proposed that is an efficient technique to process ECG signals by estimating its coefficients. In this paper, two parameters viz. atrial tachycardia (AT) and premature atrial contractions (PAC) are considered for evaluating the performance of the proposed methodology for a total of 17 recordings (6 real time and 11 from MIT-BIH arrhythmia database). As compared to K-nearest neighbour (KNN) and principal component analysis (PCA) with AR modelling [also known as Yule-Walker (YW) and Burg method], KNN classifier coupled with Burg method (i.e., Burg + KNN) yielded good results at model order 9. A sensitivity (S_e) of 99.95%, specificity (S_p or PPV) of 99.97%, detection error rate (DER) of 0.071%, accuracy (Acc) of 99.93% and mean time discrepancy (MTD) of 0.557 msec are obtained. Consistent higher values of all the performance parameters can lead to the development of an autonomous CAD tool for timely detection of heart diseases as required in health informatics.

Keywords: electrocardiogram; ECG; AR coefficients; atrial tachycardia; premature atrial contractions; PAC; KNN classifier; PCA classifier; Burg method; Yule-Walker.

DOI: 10.1504/IJMEI.2022.119314

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.1, pp.74 - 89

Received: 15 Nov 2019
Accepted: 30 May 2020
Published online: 01 Dec 2021 *

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Title: An efficient AR modelling-based electrocardiogram signal analysis for health informatics

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