Probing Aberrant Heart Rhythms Utilizing CNN Autoencoders for Enhanced ECG Anomaly Recognition

An application of Convolutional Neural Network (CNN) Autoencoders for anomaly identification in electrocardiogram (ECG) data is investigated in this study using the PTB Diagnostic ECG Database. Heartbeats affected by cardiac abnormalities and normal heartbeats make up the two groups of 14,552 samples in all. Transposed convolution has been used to greatly enhance model performance. This work highlights the need of early cardiac issue detection and introduces a CNN Autoencoder model specifically made to effectively encode and decode ECG data, therefore allowing for the identification of abnormal patterns. Construction of a strong Autoencoder with trained encoder and decoder components to lower reconstruction errors is the process. The astonishing F1 score (65.40%), recall (89.81%), precision (55.23%), and accuracy (76.93%) of the model are demonstrated by the assessment measures. This paper emphasises the importance of early anomaly identification in ECG data using deep learning methods. Higher diagnostic skills and improved patient outcomes are very much possible with this approach.