Automated ECG Signal Analysis for Cardiac Arrhythmia Detection

Cardiovascular disease continues to be the largest cause of death worldwide, quick and precise cardiac rhythm (ECG) analysis is essential. We created an automatic diagnostic solution after realizing that manual interpretation is slow and prone to mistakes when dealing with large amounts of monitoring data. First, we used baseline correction and filtering to assure signal purity using the MIT-BIH Arrhythmia Database as our benchmark. Importantly, we next used data augmentation (segmentation of records) to enhance our dataset in order to increase the number and diversity of training samples. Our main contribution is a 1D Convolutional Neural Network (CNN) frame-work that eliminates the need for manual feature engineering by automatically identifying minute patterns in these segmented pulse signals. With an overall accuracy of 0.98 on the test set, the model’s results show remarkable reliability. Importantly, with detection rates (Recall) above 0.97, the system demonstrated its capacity to safely “catch” high-risk events such as Ventricular (“V”) and Questionable (“Q”) beats. This work validates a strong, computationally efficient, and highly accurate AI solution, despite the model’s slight tendency to confuse the common Normal beat (‘N’) with a few complex arrhythmias. This work is a major step toward developing a smart, reliable, and scalable diagnostic tool for real-time patient care in place of manual ECG interpretation.