Decoding Abnormal Heart Patterns: A CNN Autoencoder Approach to ECG Anomaly Detection

This research study investigates the use of Convolutional Neural Network (CNN) Autoencoders for detecting anomalies in electrocardiogram (ECG) data utilising the PTB Diagnostic ECG Database. The dataset consists of 14,552 samples that are classified into two categories: normal heartbeats and heartbeats influenced by cardiac abnormalities. The use of Transposed Convolution has greatly enhanced the performance of the model. The work highlights the crucial importance of promptly identifying cardiac abnormalities and introduces a CNN Autoencoder model specifically developed to effectively encode and decode ECG data, facilitating the identification of irregular patterns. The process entails constructing a resilient Autoencoder including encoder and decoder components, which are taught to minimise mistakes in reconstruction. The assessment measures demonstrate the model's exceptional accuracy (76.93%), precision (55.23%), recall (89.81%), and F1 score (65.40%). This study emphasises the significance of using deep learning methods to identify anomalies in ECG data at an early stage. This approach shows great potential for enhancing diagnostic skills and improving patient outcomes.