  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.
