Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals

Yun Chi Yeh, Wen June Wang, Che Wun Chiou

Research output: Contribution to journalArticlepeer-review

111 Scopus citations

Abstract

This work describes a Linear Discriminant Analysis (LDA) method to analyze ECG signals for diagnosing cardiac arrhythmias effectively. The proposed method can accurately classify and differentiate normal (NORM) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC) and atrial premature contractions (APC). ECG signal analysis comprises three main stages: (i) QRS waveform detection; (ii) qualitative features selection; and (iii) heartbeat case determination. The available ECG records in the MIT-BIH arrhythmia database are utilized to illustrate the effectiveness of the proposed method. Experimental results show that the correct diagnosis rates are 98.97%, 91.07%, 95.09%, 92.63% and 84.68% for NORM, LBBB, RBBB, VPC and APC, respectively.

Original languageEnglish
Pages (from-to)778-789
Number of pages12
JournalMeasurement: Journal of the International Measurement Confederation
Volume42
Issue number5
DOIs
StatePublished - Jun 2009

Keywords

  • ECG signal
  • Linear discriminant analysis
  • MIT-BIH arrhythmia database

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