ECG beats classification using waveform similarity and RR interval

TL;DR

This paper introduces a novel ECG beat classification method combining waveform similarity and RR interval features, achieving high accuracy in distinguishing six types of heartbeats.

Contribution

The study presents a new ECG classification approach that integrates waveform similarity with RR interval analysis, improving accuracy over existing methods.

Findings

Achieved 97.52% classification accuracy on MIT/BIH database.

Utilized wavelet transform for ECG denoising and feature extraction.

Successfully classified six types of heartbeats.

Abstract

This paper present an electrocardiogram (ECG) beat classification method based on waveform similarity and RR interval. The purpose of the method is to classify six types of heart beats (normal beat, atrial premature beat, paced beat, premature ventricular beat, left bundle branch block beat and right bundle branch block beat). The electrocardiogram signal is first denoised using wavelet transform based techniques. Heart beats of 128 samples data centered on the R peak are extracted from the ECG signal and thence reduced to 16 samples data to constitute a feature. RR intervals surrounding the beat are also exploited as feature. A database of annotated beats is built for the classifier for waveform comparison to unknown beats. Tested on 46 records in the MIT/BIH arrhythmia database, the method shows classification rate of 97.52%.