PulseGuard: Intelligent Arrhythmia Detection and Classification through ECG Signal Analysis
Keywords:
Classification, ECG Signal, Machine Learning, QRST Detection, Sequential Feature SelectionAbstract
Arrhythmias are abnormal heart rates. The heart condition Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT), included in Arrhythmias, are the leading causes of sudden cardiac arrest. It is essential for successful defibrillation treatment to identify potentially serious arrhythmias as early as possible. Heart disorders were studied in several ways. Among them, an ECG (electrocardiography) test is regarded as the most effective noninvasive type of inquiry. Most widely utilized arrhythmia detection, as well as classification methods, rely only on surface Electrocardiogram analysis. So, an algorithm corresponding to shape statistical features and spectral kurtosis features analysis using supervised machine learning algorithms to increase the efficiency of heart diagnostics. The proposed prediction framework considers the feature selection technique with a Support Vector Machine, Naïve Bayes, Linear Discriminant Analysis, K-nearest neighbor, and Decision Tree for early arrhythmia diagnosis. The empirical results on the publicly available MIT-BIH Arrhythmia database with supervised classification achieve an efficient prediction accuracy of 93.75% for the decision tree. This study also seeks to develop a predictive model with a feature selection technique for detecting arrhythmias that improves heart diagnostics' accuracy.
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