Interpretable morphological features for efficient single-lead automatic ventricular ectopy detection.

Journal Article (Journal Article)

OBJECTIVE: We designed an automatic, computationally efficient, and interpretable algorithm for detecting ventricular ectopic beats in long-term, single-lead electrocardiogram recordings. METHODS: We built five simple, interpretable, and computationally efficient features from each cardiac cycle, including a novel morphological feature which described the distance to the median beat in the recording. After an unsupervised subject-specific normalization procedure, we trained an ensemble binary classifier using the AdaBoost algorithm RESULTS: After our classifier was trained on subset DS1 of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database, our classifier obtained an F1 score of 94.35% on subset DS2 of the same database. The same classifier achieved F1 scores of 92.06% on the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia database and 91.40% on the MIT-BIH Long-term database. A phenotype-specific analysis of model performance was afforded by the annotations included in the St. Petersburg INCART Arrhythmia database CONCLUSION: The five features this novel algorithm employed allowed our ventricular ectopy detector to obtain high precision on previously unseen subjects and databases SIGNIFICANCE: Our ventricular ectopy detector will be used to study the relationship between premature ventricular contractions and adverse patient outcomes such as congestive heart failure and death.

Full Text

Duke Authors

Cited Authors

  • Malik, J; Loring, Z; Piccini, JP; Wu, H-T

Published Date

  • December 3, 2020

Published In

Volume / Issue

  • 65 /

Start / End Page

  • 55 - 63

PubMed ID

  • 33516949

Pubmed Central ID

  • 33516949

Electronic International Standard Serial Number (EISSN)

  • 1532-8430

Digital Object Identifier (DOI)

  • 10.1016/j.jelectrocard.2020.11.014


  • eng

Conference Location

  • United States