Why the study?
Existing machine learning procedures used in medical devices for ECG analysis are rigid, vulnerable, and black-box methods lacking interpretable physical features.
The FMMecg model provides a physically meaningful, robust, and highly accurate automated method for ECG wave detection and morphological characterization, outperforming several existing machine learning approaches.
Advances automated ECG analysis; leaves open prospective clinical validation before practice adoption.
A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artefacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow to all the different patterns in heartbeats be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet, the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.
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Rueda et al. (2020) studied this question.
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