The recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). The main features extracted in the proposed work are minimum, maximum, mean, standard deviation, and median. The experiments were conducted on forty-five ECG records in MIT BIH arrhythmia database and in MIT BIH noise stress test database. The proposed model has an overall accuracy of 99.7 % with a sensitivity of 99.7 % and a positive predictive value of 100 %. The detection error rate for the proposed model is 0.0004. This paper also includes a study of the cardiac arrhythmia recognition using an IoMT (Internet of Medical Things) approach. © 2020 Elsevier B.V.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Arumugam M

Bian G.-B.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Artificial Intelligence in Medicine

A systematic and profound reading of an electrocardiogram (ECG) is needed to identify the different kinds of cardiac diseases called Arrhythmia. The manual identification of the changes in the ECG pattern over a long period is challenging. This work can be automatized by developing algorithms that run perfectly on a computer or on a smartphone to identify the causes of arrhythmia. The proposed work includes three stages of analysis: (1) the ECG noise suppression, (2) RR and PR intervals extraction from the ECG signal, and the (3) ECG classification. The proposed methodology accurately identified the locations and amplitudes of P, Q, R, S, and T subwaves of the ECG signal using a dedicated wavelet design. Experimental results of the MIT-BIH arrhythmia database records indicate the energy levels of the ECG signal at a decomposition level of 4 and 8 as 3.694e+09 and 7.148e+09, respectively. These energy levels are used in deciding the wavelet decomposition levels for feature extraction and classification of the ECG signal. A decomposition level of eight is proposed in this work for perfect feature extraction and classification of the ECG signal. An analysis of subband frequencies obtained in the decomposition of the ECG signal is also performed. The proposed methodology gives a sensitivity of 99.58% and positive predictive value of 95.92% in the ECG examination. © 2019 John Wiley &amp; Sons, Ltd.

Concurrency and Computation: Practice and Experience

Arrhythmia identification and classification using wavelet centered methodology in ECG signals

A well-recorded ECG contains complete information of various heart diseases. The detection of cardiac diseases encompasses ECG signal pre-processing, feature extraction and classification of the cardiac disease from the detected abnormality. A minute change in ECG due to noise signals changes the characteristics of ECG which results in a wrong diagnosis of the heart disease. The main focus of this paper is on developing and implementing a novel smart filter design for filtering the core noise signals that distort the original ECG. The core noise signals are identified as power line interference (PLI), baseline wander and electromyography (EMG). The comparison of the proposed filter design in terms of signal to noise ratio and power spectral density indicates that the proposed filter design has a good response characteristic for noise filtration. The eradication of these noise signals helps to achieve accurate identification of heart disease and makes the life of physicians easier. Copyright © 2018 Inderscience Enterprises Ltd.

International Journal of Embedded Systems

An intelligent paradigm for denoising motion artefacts in ECG preprocessing: smart filters

Premature death and disability from sudden cardiac arrest continue to be a serious public health burden. Electrocardiography (ECG) is a ubiquitous vital sign health monitoring method used in the healthcare systems. The early detection of abnormality in ECG signal for cardiac disease leads to timely diagnosis and prevents the person from death. Normally the surgeons have to study a large amount of ECG data to search for abnormal beat in ECG. If the surgeons fail to note the abnormal cycles that are very less in number, it leads to fatigue. In the proposed research work, an intelligent ECG digital system is designed to interpret these abnormal signals which reduce the tedious work of interpreting ECG. The proposed work monitors the heart beat continuously in a convenient manner and with maximum accuracy for diagnosing. The ECG signals are acquired in real time and amplified through an Instrumentation amplifier. The resulting signals are processed for the cancellation of noise through a low pass filter and notch filter. Further processing of these signals in the microcontroller detects the abnormalities of cardiac arrest. The result is communicated through GSM which reduces the burden of the doctors to a greater extent. These signals are stored in the Secure Data (SD) card to have a complete history of the signals before and after the occurrence of the cardiac event. The proposed research work combines the capabilities of real-time monitoring of ECG signals and the abnormal symptom-reporting systems. © Springer International Publishing Switzerland 2015.

Advances in Intelligent Systems and Computing Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1

Journal	Data powered by TypesetArtificial Intelligence in Medicine
Publisher	Data powered by TypesetElsevier BV
ISSN	0933-3657
Open Access	0