Redefining ECG Detection using CNN Autoencoder Approach through Deep Learning

This research delves into the application of Convolutional Neural Network (CNN) Autoencoders for anomaly detection in electrocardiogram (ECG) data, leveraging the PTB Diagnostic ECG Database. The dataset comprises 14,552 samples, categorized into normal heartbeats and those affected by cardiac abnormalities. Utilizing Transposed Convolution has significantly boosted the model's performance. This study underscores the critical importance of early detection of cardiac abnormalities and introduces a CNN Autoencoder model designed to efficiently encode and decode ECG data, enabling the identification of irregular patterns. The process involves developing a robust Autoencoder with encoder and decoder components trained to minimize reconstruction errors. Evaluation metrics reveal the model's impressive accuracy (76.93%), precision (55.23%), recall (89.81%), and F1 score (65.40%). This research highlights the pivotal role of deep learning techniques in early-stage anomaly detection in ECG data, demonstrating significant potential for enhancing diagnostic capabilities and improving patient outcomes.