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

TitleEnhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation.
Publication TypeJournal Article
Year of Publication2022
AuthorsHonarvar H, Agarwal C, Somani S, Vaid A, Lampert J, Wanyan T, Reddy VY, Nadkarni GN, Miotto R, Zitnik M, Wang F, Glicksberg BS
JournalCardiovasc Digit Health J
Volume3
Issue5
Pagination220-231
Date Published2022 Oct
ISSN2666-6936
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.

DOI10.1016/j.cvdhj.2022.07.074
Alternate JournalCardiovasc Digit Health J
PubMed ID36310683
PubMed Central IDPMC9596304
Grant ListRF1 AG072449 / AG / NIA NIH HHS / United States
Division: 
Institute of Artificial Intelligence for Digital Health
Category: 
Faculty Publication