Unsupervised Anomaly Detection in ECG Signals Using Denoising Autoencoders: A Comparative Study

Anomaly detection is essential in various domains, including healthcare, where early identification of irregular patterns in data can significantly impact patient outcomes. This paper presents a novel approach to unsupervised anomaly detection using the ECG5000 dataset, focusing on electrocardiogram (ECG) data. We introduce multiple autoencoder architectures—linear, convolutional, and LSTM-based—reframing the traditionally supervised classification task as an unsupervised anomaly detection problem. By disregarding original labels, we emphasize the models’ ability to generalize across different ECG abnormalities. Our extensive experiments reveal that a denoising linear autoencoder outperforms more complex architectures, achieving an accuracy of 97.73%, within 0.7% of the current state-of-the-art. Furthermore, we conduct a comprehensive analysis of the latent space representations, providing insights into the models’ feature extraction capabilities. These findings suggest that our approach not only reduces model complexity but also maintains high accuracy, offering a viable solution for real-time anomaly detection in medical settings.