Artefact detection and quality assessment of ambulatory ECG signals

Highlights • A novel way for ECG quality assessment is proposed, based on the posterior probability of an artefact detection classifier.• A good performance was obtained when testing the classifier on two independent (re)labelled datasets, thereby showing its robustness. The performance was better, compared to a heuristic method and comparable to another machine learning algorithm.• A significant correlation was observed between the proposed quality assessment and the annotators level of agreement.• Significant decreases in quality level were observed for different noise levels.

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