We are happy to announce that at ICPRAI 2024 Giacomo presented our paper titled “FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge”. This work shows the benefit of taking the underlying model of the heart (via Frequency Modulated Möbius waves) into account when performing ECG anomaly detection. Our model achieves up to 0.31 increase in area under the ROC curve (AUROC) when compared to the state of the art models. Moreover, 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 parameter extraction and anomaly detection.
This is joint work with Giacomo Verardo (KTH), Magnus Boman (KI), Samuel Bruchfeld (KI), Marco Chiesa (KTH), Sabine Koch (KI), Gerald Q. Maguire Jr. (KTH), and Dejan Kostic (KTH).
The paper received an Honorable Mention in the competition for the Best Paper Award, and is available at this link, while the full abstract is below:
Detecting anomalies in electrocardiogram data is crucial to identify deviations from normal heartbeat patterns and provide 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 explicitly consider the specific patterns of ECG leads, thus compromising learning efficiency. 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. The code is available at: https://github.com/giacomoverardo/FMM-Head.
