Arrhythmia Classification Using Noise Filtering and 1D CNN

Identifying arrhythmias in electrocardiogram (ECG) data is critical for diagnosing and managing heart disorders.However, various types of noise in ECG data frequently pose a challenge to proper arrhythmia classification.To overcome this challenge, this study suggests a three-step process to make it more accurate to classify arrhythmias as normal (N), supraventricular (S), ventricular (V), fusion (F), or unknown (Q).In the first step of the three-step process, we add Gaussian noise to the MIT-BIH ECG data to make the classification model more reliable.Second, ECG signals are notch-filtered to eliminate noise artifacts and preserve cardiac information after Gaussian noise injection.Retaining important cardiac information while reducing noise distortion.Third, the 1D CNN receives denoised ECG data for arrhythmia classification.Five-class arrhythmias can be used to examine the ECG signals, according to the results of the suggested modeling.With a 1% error rate, the 1D-CNN-based classification system can identify N, S, V, F, and Q with 99%, 86%, 96%, 80%, and 99% accuracy.The results suggest that the three-step ECG arrhythmia categorization method improves diagnostic accuracy, enabling early treatment by healthcare experts.Its real-world applications improve cardiovascular diagnosis and patient outcomes.