Abstract
Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.
Highlights
Electrocardiogram (ECG) signal which is the recording of the cardiac electrical activity provides the important information about heart’s condition
The interpretation of the important signal information in the ST is apparent which will be beneficial to extract the important features from the ECG signal [12]
The first approach uses the wavelet transform (WT) based features combined with temporal features, the second approach uses the ST based features along with temporal features, whereas the third approach uses the mixture of ST and WT based features along with temporal feature set
Summary
Electrocardiogram (ECG) signal which is the recording of the cardiac electrical activity provides the important information about heart’s condition. In [8], Jiang and Kong have used the Hermite transform coefficients and time intervals between two neighboring R-peaks of ECG signals based features and block based neural network as a classifier and classified five types of ECG beat with an accuracy of 96.6%. In this method, there are around 1015 parameters/thresholds which are set empirically with respect to the dataset used. The proposed methods classify the five classes of ECG beat recommended by the AAMI standard and experimental results are compared with the other existing feature extraction techniques.
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