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Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise

Yao Chen1,2, Xiao Wang1,2, Yonghan Jung1,3

  • 1Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, United States of America.

Physiological Measurement
|September 6, 2018
PubMed

Insights

We developed a novel algorithm to detect atrial fibrillation from electrocardiograms (ECGs), achieving 81% accuracy. This method aids in stroke risk stratification by classifying various cardiac dysrhythmias.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Atrial fibrillation detection is crucial for stroke risk stratification.
  • Accurate classification of cardiac dysrhythmias from electrocardiograms (ECGs) remains a challenge.

Purpose of the Study:

  • To develop and evaluate a novel methodology for classifying ECGs into normal, atrial fibrillation, and other cardiac dysrhythmias.
  • To assess the algorithm's performance using the PhysioNet Challenge 2017 database.

Main Methods:

  • Utilized piecewise linear splines for feature extraction from ECG waveforms.
  • Employed a gradient boosting algorithm (XGBoost) for classification, incorporating morphological and heart rate variability features.

Main Results:

  • Achieved an average F1 score of 81% in 10-fold cross-validation.
  • Attained an 81% F1 score on an independent testing set, comparable to top-performing methods in the PhysioNet Challenge 2017.

Conclusions:

  • The developed algorithm demonstrates strong performance in multi-label short ECG classification.
  • The methodology effectively utilizes selected morphological features for accurate cardiac dysrhythmia detection.
Abstract

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