Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation

Hossein Honarvar, Chirag Agarwal, Sulaiman Somani, Akhil Vaid, Joshua Lampert, Tingyi Wanyan, Vivek Y. Reddy, Girish N. Nadkarni, Riccardo Miotto, Marinka Zitnik, Fei Wang, Benjamin S. Glicksberg

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural networks (CNNs) and traditionally use the full duration of raw ECG waveforms that create redundancies in feature learning and result in inaccurate predictions with large uncertainties. Objective: For enhancing these predictions, we introduced a sub-waveform representation that leverages the rhythmic pattern of ECG waveforms (data-centric approach) rather than changing the CNN architecture (model-centric approach). Results: We applied the proposed representation to a population with 92,446 patients to identify left ventricular dysfunction. We found that the sub-waveform representation increases the performance metrics compared to the full-waveform representation. We observed a 2% increase for area under the receiver operating characteristic curve and 10% increase for area under the precision-recall curve. We also carefully examined three reliability components of explainability, interpretability, and fairness. We provided an explanation for enhancements obtained by heartbeat alignment mechanism. By developing a new scoring system, we interpreted the clinical relevance of ECG features and showed that sub-waveform representation further pushes the scores towards clinical predictions. Finally, we showed that the new representation significantly reduces prediction uncertainties within subgroups that contributes to individual fairness. Conclusion: We expect that this added control over the granularity of ECG data will improve the DL modeling for new artificial intelligence technologies in the cardiovascular space.

Original languageEnglish
Pages (from-to)220-231
Number of pages12
JournalCardiovascular Digital Health Journal
Volume3
Issue number5
DOIs
StatePublished - Oct 2022

Keywords

  • Deep learning, Cardiology, Electrocardiograms, Sub-waveform representation, Machine Learning

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