Enhancing interpretability and reducing uncertainties in deep learning of electrocardiograms using a sub-waveform representation

In electrocardiogram (ECG) deep learning (DL), researchers traditionally use the full duration of waveforms that create redundancies in feature learning and result in inaccurate predictions with large uncertainties. In this work, we introduce a new sub-waveform representation that leverages the rhythmic pattern of ECG waveforms by aligning the heartbeats to enhance the DL predictive capabilities. As a case study, we investigate the impact of waveform representations on DL predictions for identification of left ventricular dysfunction. We provide the explanation of how the sub-waveform representation opens up a new space for feature learning and minimizing uncertainties. By developing a novel scoring system, we carefully examine the feature interpretation and the clinical relevance. We note that the proposed representation enhances predictive power by engineering only at the waveform level (data-centric) rather than changing neural network architecture (model-centric). We expect that this added control over granularity of data will improve the ECG-DL modeling for developing new AI technologies in the cardiovascular space.