Online anomaly detection for long-term ECG monitoring using wearable devices. (April 2019)

• Record Type: Journal Article
• Authors: Carrera, Diego; Rossi, Beatrice; Fragneto, Pasqualina; Boracchi, Giacomo
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 482
• Page End: 492
• Publication Date: 2019-04
• Subjects: Online and long-term ECG monitoring -- Anomaly detection -- Domain adaptation -- Wearable devices -- Sparse representations; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.11.019
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel solution to perform online ECG monitoring and detect anomalous heartbeats. We learn dictionaries yielding sparse representations to describe normal heartbeats. We track heart-rate variations by transforming the user-specific dictionaries. Transformations are user-independent and learned from a publicly available dataset. Our solution is optimized to perform online ECG monitoring on wearable devices. Abstract: Many successful algorithms for analyzing ECG signals leverage data-driven models that are learned for each specific user. Unfortunately, a few algorithmic challenges are still to be addressed before employing these models in wearable devices, thus enabling online and long-term monitoring. In particular, since the heartbeats morphology changes with the heart rate, models learned in resting conditions need to be adapted to analyze ECG signals recorded during everyday activities. We propose an online ECG monitoring solution where normal heartbeats of each specific user are modeled by dictionaries yielding sparse representations, and heartbeats that do not conform to this model are detected as anomalous. We track heart rate variations by adapting the user-specific dictionary with a set of user-independent, linear, transformations. Our experiments demonstrate that these transformations can be successfully learned from a public dataset of ECG signals and that, thanks to an optimized anomaly-detection algorithm, our solution enables online and long-term ECG monitoring.