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A low-complexity algorithm for detection of atrial fibrillation using an ECG.

Nadi Sadr1,2,3, Madhuka Jayawardhana1,2, Thuy T Pham4

  • 1School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.

Physiological Measurement
|May 24, 2018
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Summary

This study introduces an automated method for detecting atrial fibrillation (AF) using short, single-lead electrocardiogram (ECG) recordings. The developed algorithm achieves a 78% overall F1 score, demonstrating its potential for efficient cardiac rhythm analysis.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Atrial fibrillation (AF) is a common arrhythmia requiring accurate detection.
  • Automated analysis of electrocardiogram (ECG) signals can aid in diagnosing cardiac conditions.
  • Short-duration ECG recordings pose challenges for reliable rhythm analysis.

Purpose of the Study:

  • To develop and evaluate an automated method for detecting atrial fibrillation (AF) from single-lead ECGs up to 60 seconds.
  • To classify ECG recordings into four categories: normal, AF, other, and noisy rhythms.
  • To assess the performance of different machine learning classifiers for AF detection.

Main Methods:

  • Utilized features derived from RR interbeat intervals, including time, frequency, and distribution domains.
  • Trained and evaluated three classifiers: linear discriminant analysis, quadratic discriminant analysis, and quadratic neural network (QNN).
  • Selected QNN as the best-performing classifier and validated its performance on an independent test set of 3658 ECG signals.

Main Results:

  • The QNN classifier achieved an F1 score of 0.90 for normal rhythms and 0.75 for AF.
  • The method obtained F1 scores of 0.68 for other rhythms and 0.32 for noisy rhythms.
  • The overall F1 score across all classes on the test set was 0.78.

Conclusions:

  • The developed automated method effectively detects atrial fibrillation from short single-lead ECG recordings.
  • The algorithm's low computational cost, due to RR interval features and a simple neural network, makes it suitable for low-power devices.
  • This approach offers a promising tool for accessible and efficient cardiac rhythm monitoring.