Heartbeat Classification Using Normalized RR Intervals and Morphological Features

• Published in: Mathematical problems in engineering Vol. 2014; no. 1
• Main Authors: Lin, Chun-Cheng, Yang, Chun-Min
• Format: Journal Article
• Language: English
• Published: New York Hindawi Publishing Corporation 01.01.2014; John Wiley & Sons, Inc
• Subjects: Accuracy; Classification; Discriminant analysis; Feature extraction; Heart; Heart rate; Intervals; Linear prediction; Mathematical models; Mathematical problems; Methods; Neural networks; Noise prediction; Performance prediction; Prediction models; Preprocessing; Signal classification; Studies; Wavelet; Wavelet analysis
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2014/712474

This study developed an automatic heartbeat classification system for identifying normal beats, supraventricular ectopic beats, and ventricular ectopic beats based on normalized RR intervals and morphological features. The proposed heartbeat classification system consists of signal preprocessing, feature extraction, and linear discriminant classification. First, the signal preprocessing removed the high-frequency noise and baseline drift of the original ECG signal. Then the feature extraction derived the normalized RR intervals and two types of morphological features using wavelet analysis and linear prediction modeling. Finally, the linear discriminant classifier combined the extracted features to classify heartbeats. A total of 99,827 heartbeats obtained from the MIT-BIH Arrhythmia Database were divided into three datasets for the training and testing of the optimized heartbeat classification system. The study results demonstrate that the use of the normalized RR interval features greatly improves the positive predictive accuracy of identifying the normal heartbeats and the sensitivity for identifying the supraventricular ectopic heartbeats in comparison with the use of the nonnormalized RR interval features. In addition, the combination of the wavelet and linear prediction morphological features has higher global performance than only using the wavelet features or the linear prediction features.