Abstract
One of the abnormalities in the heart that can be assessed from an ECG signal is premature ventricle contraction (PVC). PVC is a form of arrhythmia in the form of irregularity in beat ECG signals. In this study, a multilevel wavelet entropy method was developed to distinguish PVC and normal ECG signals automatically. Data was taken from the MIT-BIH arrhythmia database with the process carried out is normalization, median filtering, beat-parsing, MWE calculation and classification using SVM. The results of the experiment showed that MWE level 5 with DB2 as mother wavelet and Quadratic SVM as classifier resulted in the highest accuracy of 94.9%. MWE level 5 means only five features needed for classification. The number of features is very little compared to previous research with a quite high accuracy.
Highlights
The proposed method merged with support vector machine (SVM) was expected to give high accuracy for premature ventricle contraction (PVC) classification using ECG signal.The Health of someone’s heart could be seen from the ECG signal generated from heart activities
WE was only calculated on one decomposition level, but on this research, WE was calculated on several decomposition levels, so it is called multilevel wavelet entropy (MWE)
WE was calculated at each decomposition level so that it produces one WE value at each decomposition level
Summary
The proposed method merged with SVM was expected to give high accuracy for PVC classification using ECG signal. ECG signals are rated by rhythm, shape, and orientation [1]. Rhythm, and ECG signal orientation indicate the existence of heart abnormalities. Arrhythmia is caused by heartbeat irregularity, speed interference, or problem in heart electricity signal transmission [2]. The various method has been developed to detect PVC or other arrhythmia using a digital signal processing technique. Kaya and Pehlivan used PCA, ICA, and SOM for data reduction on PVC ECG signal. Previous researches chose to use feature extraction technique while some only used signal dimensions reduction method. The entropy wavelet-based method called the multilevel wavelet entropy (MWE) was used for PVC classification. WE measurement on several decomposition levels is expected to give more complete information about ECG signal energy distribution
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