International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 10 Issue: 5 DOI: https://doi.org/10.17762/ijritcc.v10i5.5542 Article Received: 10 March 2022 Revised: 22 March 2022 Accepted: 25 April 2022 Publication: 31 May 2022 ___________________________________________________________________________________________________________________ 1 IJRITCC | May 2022, Available @ http://www.ijritcc.org Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods Akhil Mathew Philip 1 , Dr. S Hemalatha 2 1 Research Scholar, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore, India 2 Research Guide, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore, India Abstract: The "Cardio Vascular Diseases (CVDs)" had already attained worrisome proportions in both advanced and emerging nations in recent times. Physically inactive behaviors, altered eating, and occupational routines, and reduced daily fitness were all recognized as crucial contextual elements, in addition to genetics. Considering CVDs have such a significant morbidity and mortality, accurate and early diagnosis of cardiac disease by "ElectroCardioGram (ECG)" allows clinicians to decide suitable therapy for a multitude of cardiovascular disorders. The interpretation of ECG signal is an important bio-signal processing area that involves the application of computer science and engineering to detect and visualize the functional status of the heart. Therefore, in the present work, a detailed study on ECG signals denoising and abnormalities detection using different techniques were performed. Annoying distortions and noisy particles are common in ECG signals. The "Biased Finite Impulse Response (BFIR)" preprocessing filtering is employed in this research to eliminate the noises in the raw ECG signals. The "Nonlinear-Hamilton" segmentation method is employed to segment the 'R' peak signals. To decrease the extraneous features included in the segmented ECG data, the innovative "Enhanced Principal Component Analysis (EPCA)" was applied for feature extraction. A unique "Enhanced version of the Support Vector Machine (ESVM)" framework with a "Weighting Kernel" based technique is proposed for classifying the ECG data. The 'Q', 'R', and 'S' waves in the given ECG data will be identified by this framework, allowing it to characterize the cardiac rhythm. The evaluation metrics of the EPCA-ESVM proposed method is comparatively analyzed with our previous approach EPSO. To estimate the results for the dataset from MIT-BIH it was experimented with by the EPSO and the EPCA-ESVM methods focused upon different parameters such as Accuracy, F1-score, etc. The final findings of the EPCA-ESVM method were good than the EPSO method in which the accuracy is higher even though unbalanced data were present. Keywords: Cardio Vascular Diseases, ECG, Enhanced Principal Component Analysis, Enhanced Support Vector Machine. 1. INTRODUCTION Globally, CVDs and stroke are the leading causes of mortality, as per the "World Health Organization (WHO)". CVDs resulted in the mortality of approximately 20.3 million individuals in 2019, accounting for 33% of all fatalities globally. Heart health studies have become more important for medicinal scientists, particularly those who are interested in technical, preventative, or therapeutic breakthroughs in the field of CVDs. As a result, experts recently focused their attention on conventional methods of cardiovascular assessment deployed in the household, health centers, and emergency rooms [1]. Many heart disorders may be detected using a basic, affordable, and risk-free ECG assessment, which was a much more frequent medical cardiac assessment. There have been a lot of studies done on ECG interpretation in a previous couple of decades. The majority of ECG signal reflects a crucial indicator for cardiac functioning evaluation since it shows the electrical events that occur with a trigeminal and a trigeminal-atrioventricular rhythm. In this way, the ECG offers adequate data about a patient's cardiac condition [2]. The CVDs include a multitude of illnesses distinguished by abnormalities in the heart's electrical activity, such as rapidity or slowness, or indeed waveform deformity. ECG waveform analysis may identify any abnormality, whether it is a change in heartbeat or morphology structure, that may point to a pathological condition. Medical professionals who use ECG signals for medical processing and analysis take a lengthy time frame and are often impractical or impossible in distant places, such as those requiring long-term surveillance. As a result, albeit difficult for real-time ECG processing, an "Automated Arrhythmia Beat Classification" is critically needed [3].