FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
This work addresses the need for explainable and efficient anomaly detection in ECG data for at-risk patients, representing an incremental improvement over existing autoencoder methods.
The paper tackled the problem of detecting anomalies in electrocardiogram data by replacing the decoder in autoencoder models with a reconstruction head based on prior knowledge of ECG shape, resulting in up to a 0.31 increase in AUROC, half the model size, and four orders of magnitude faster processing time.
Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. However, these models do not consider the specific patterns of ECG leads and are unexplainable black boxes. In contrast, we replace the decoding part of the AE with a reconstruction head (namely, FMM-Head) based on prior knowledge of the ECG shape. Our model consistently achieves higher anomaly detection capabilities than state-of-the-art models, up to 0.31 increase in area under the ROC curve (AUROC), with as little as half the original model size and explainable extracted features. The processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameters extraction and anomaly detection.