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[Automatic detection and classification of atrial fibrillation using RR intervals and multi-eigenvalue]

Zhibo Chen1, Jian Li1, Zhi Li2

  • 1School of Electronic Information, Sichuan University, Chengdu 610041, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|August 21, 2018
PubMed

Insights

This study introduces an automated method for detecting atrial fibrillation (AF) using electrocardiogram (ECG) RR intervals. The approach utilizes robust coefficient of variation, skewness, and Lempel-Ziv complexity for accurate AF classification.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Context:

  • Atrial fibrillation (AF) is a prevalent cardiac arrhythmia requiring accurate clinical diagnosis.
  • Manual electrocardiogram (ECG) interpretation is time-consuming and susceptible to errors due to signal complexity.
  • Automated detection methods are crucial for efficient and reliable AF diagnosis.

Purpose:

  • To develop an automated feature extraction and classification method for atrial fibrillation detection using ECG RR intervals.
  • To address the limitations of manual ECG analysis, including time consumption and potential for misdiagnosis.
  • To improve the accuracy and efficiency of atrial fibrillation diagnosis.

Summary:

  • A novel feature extraction method using robust coefficient of variation (RCV), skewness parameter (SKP), and Lempel-Ziv complexity (LZC) of RR intervals is proposed.
  • These features are used to train a support vector machine (SVM) classifier for automated AF detection.
  • Validation on the MIT-BIH atrial fibrillation database demonstrated high sensitivity (95.81%) and specificity (96.48%).

Impact:

  • The proposed method offers an effective and accurate approach for automatic atrial fibrillation detection.
  • It has the potential to significantly aid clinicians in diagnosing AF, improving patient outcomes.
  • The method's high performance suggests its clinical applicability in real-world diagnostic settings.

Keywords:
Lempel-Ziv complexityatrial fibrillationrobust coefficient of variationskewnesssupport vector machine

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