Interpreting Deviant Heart Patterns: Applying MobileNet CNN Autoencoder for ECG Anomaly Detection

In order to identify abnormalities in electrocardiogram (ECG) data, this research study uses the PTB Diagnostic ECG Database to examine the application of MobileNet (CNN) Autoencoders. The dataset comprises 14,552 samples, which are categorized into two groups: normal heartbeats and heartbeats affected by cardiac problems. The utilization of Transposed Convolution has significantly improved the efficiency of the model. The study emphasizes the critical significance of immediately detecting cardiac irregularities and presents a innovative CNN Autoencoder model that is specifically designed to efficiently encode and decode ECG data. This model aids in the identification of irregular patterns. The procedure involves building a robust Autoencoder that consists of encoder and decoder components, which are trained to minimize errors in reconstruction. The assessment metrics showcase the model's outstanding accuracy (76.93%), precision (55.23%), recall (89.81%), and F1 score (65.40%). This work highlights the need of utilizing deep learning techniques to detect abnormalities in ECG data at an early stage. This technique demonstrates social significant promise in augmenting diagnostic abilities and improved patient results.