A Knowledge-Driven Framework For Ecg Representation And Interpretation For Wearable Applications

The increasing use of wearable technology creates the need for reliable signal representations with low storage and transmission cost, as well as interpretable models that can be used to translate signals into meaningful constructs. We propose a knowledge-driven sparse representation of the electrocardiogram (ECG) that takes into account the characteristic structure of the corresponding signal through the use of appropriately designed parametric dictionaries containing Hermite and amplitude-modulated sinusoidal atoms for the P, T waves and QRS complex, respectively. We further demonstrate how these atoms can be used to automatically interpret the ECG morphology through the QRS detection and beat classification. Our results indicate relative errors of the order of 10(-2), compression rates 10 times smaller than the actual signal, as well as reliable QRS detection (93%) and beat classification (78%). These are discussed in terms of developing efficient and reliable wearable ECG applications.