ANALYSIS FEATURE EXTRACTION FOR OPTIMIZING ARRHYTHMIA CLASSIFICATION FROM ELECTROCARDIOGRAM SIGNALS - Satria Mandala; Ramadhan, Yusril (Informatika, Universitas Jenderal Soedirman, 2024)
Heart disease is the primary cause of death globally, with arrhythmias, such as Premature Atrial Contraction (PAC), Atrial Fibrillation (AF), and Premature Ventricular Contraction (PVC), being critical heart rhythm abnormalities. Although numerous studies have utilized feature extraction from electrocardiogram (ECG) signals to detect these conditions, optimal accuracy has not been achieved. Therefore, this research aims to identify relevant features and achieve better results by using dynamic feature extraction methods. The extracted features used are RR Interval, PR Interval, and QRS Complex. By combining 2 feature extractions - RR Interval & PR Interval, RR Interval & QRS Complex, and PR Interval & QRS Complex - this study achieves a high level of accuracy on the RR Interval & QRS Complex feature extraction, reaching 97.60%, with a specificity of 98.30% and sensitivity of 96.58%.