Cutting-Edge Convolutional Neural Network Autoencoders for Anomaly Detection in ECG Signals: Advancements in Early Cardiac Diagnosis

The current research deals with the complex domain of ECG signal processing and classification using convolutional neural network auto-encoders. Much attention was placed on the PTB Diagnostic ECG Database, which includes a total amount of 14,552 ECG recordings diagnostic leads, curated under one database. These signals are classified into normal heartbeats and those that have some cardiac abnormalities, sampled at a high frequency of 125Hz hence providing high resolution data for analysis. The objective of this paper is to construct a more robust Autoencoder model, which would be most suitable for detecting anomalous ECG signals. Therefore, the architecture shall capitalize on the ability of CNNs in capturing and reconstructing intricate patterns in ECG data so that deviations that could indicate some potential heart condition could also be identified. The CNN Autoencoder demonstrated very good performance in experiments: the overall accuracy reached 78%. For normal heartbeats, model precision and recall were 0.97 and 0.72, while for anomalous signals, these metrics are 0.57 and 0.93. The ‘macro average’: ‘precision’, ‘recall’, and ‘F1-score’ were 0.77, 0.83, and 0.77 correspondingly, indicating balanced performance across both categories. Even the weighted averages entail robustness in this respect: in particular, 0.85, 0.78, and 0.79 for ‘precision’, ‘recall’, and ‘F1-score’. These results demonstrate, therefore, that CNN autoencoders are highly efficient in detecting cardiac anomalies in ECG signals and prove their possible utility for diagnostic processes. This paper has therefore advanced massive improvements in computer-based cardiac monitoring and diagnosis systems by providing a hopeful avenue for the early detection of cardiac anomalies and intervention measures.