Arrhythmia Detection and Classification by Using Modified Recurrent Neural Network Ajina Mohamed Ameer * and M. Victor Jose Department of CSE, Noorul Islam Centre for Higher Education, Kumaracoil, 629180, India *Corresponding Author: Ajina Mohamed Ameer. Email: ajinama345@gmail.com Received: 27 August 2021; Accepted: 08 December 2021 Abstract: This paper presents a novel approach for arrhythmia detection and clas- sification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identi- fication. In the research methodology, there are 4 main steps. Acquisition and pre- processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modi fied RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in the pre- processing. Feature extraction is done with morphological and dynamic features to detect morphological arrhythmia the shape morphological properties of the ECG signal. Dynamic arrhythmia could be diagnosed by having some feature of the ECG signal such as amplitude and position of the QRS peak. Using the modified pan Tompkins algorithm, arrhythmia was detected. Load the ECG signal after getting QRS complex R and p peak of ECG signal detected. For the deep learning classification modified RNN is used as a classifier. The modified RNN is trained independently for each of the 17 classes using training and validation data, Data from the validation phase is utilized to calculate network parameters tweaking. The acquired results demonstrate the proposed method’ s effectiveness. The overall classification accuracy for 17 cardiac arrhythmias was 95.33%. For each 10 s ECG data, the classification time was 0.015 s. The proposed technique is compared in terms of accuracy to that of other existing techniques, revealing that the new method outperforms them. Keywords: Arrhythmia detection; ECG signal; deep learning; modified recurrent neural network; pan Tompkins 1 Introduction The most basic and extensively used way of identifying cardiac events is electrocardiography (ECG) (or irregular heartbeat irregularities) since it is a non-invasive and painless examination [1] that can reveal important information about cardiovascular health [2] and disease [3]. Arrhythmia of the heart is a common symptom of high blood pressure. The latter is a major societal issue [4–6] because of 1) its This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.023924 Article ech T Press Science