Analysis of Arrhythmia Classification on ECG Dataset

TL;DR

This paper reviews various techniques for classifying arrhythmia from ECG signals, highlighting methods, datasets, and challenges to improve automatic detection for better clinical decision-making.

Contribution

It provides an analysis of existing arrhythmia classification methods using ECG data, discussing their techniques, performance, and limitations to guide future research.

Findings

Deep learning methods like CNNs and LSTMs show high accuracy.

Preprocessing and feature extraction are crucial for effective classification.

Challenges include dataset variability and real-time detection issues.

Abstract

The heart is one of the most vital organs in the human body. It supplies blood and nutrients in other parts of the body. Therefore, maintaining a healthy heart is essential. As a heart disorder, arrhythmia is a condition in which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is used to analyze the arrhythmia problem from the ECG signals because of its fewer difficulties and cheapness. The heart peaks shown in the ECG graph are used to detect heart diseases, and the R peak is used to analyze arrhythmia disease. Arrhythmia is grouped into two groups - Tachycardia and Bradycardia for detection. In this paper, we discussed many different techniques such as Deep CNNs, LSTM, SVM, NN classifier, Wavelet, TQWT, etc., that have been used for detecting arrhythmia using various datasets throughout the previous decade. This work shows the analysis of some arrhythmia classification on the ECG dataset. Here, Data preprocessing, feature extraction, classification processes were applied on most research work and achieved better performance for classifying ECG signals to detect arrhythmia. Automatic arrhythmia detection can help cardiologists make the right decisions immediately to save human life. In addition, this research presents various previous research limitations with some challenges in detecting arrhythmia that will help in future research.