ECG beat classification using empirical mode decomposition and mixture of features |
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Authors: | Santanu Sahoo Monalisa Mohanty Suresh Behera |
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Affiliation: | 1. Department of Electronics and Communication Engineering, Siksha “O” Anusandhan University, Odisha, India;2. Department of Cardiology, IMS and SUM Hospital, Siksha “O” Anusandhan University, Odisha, India |
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Abstract: | Computer-aided analysis is useful in predicting arrhythmia conditions of the heart by analysing the recorded ECG signals. In this work, we proposed a method to detect, extract informative features to classify six types of heartbeat of ECG signals obtained from the MIT-BIH Arrhythmia database. The powerful discrete wavelet transform (DWT) is used to eliminate different sources of noises. Empirical mode decomposition (EMD) with adaptive thresholding has been used to detect precise R-peaks and QRS complex. The significant features consists of temporal, morphological and statistical were extracted from the processed ECG signals and combined to form a set of features. This feature set is classified with probabilistic neural network (PNN) and radial basis function neural network (RBF-NN) to recognise the arrhythmia beats. The process achieved better result with sensitivity of 99.96%, and positive predictivity of 99.81 with error rate of 0.23% in detecting the QRS complex. In class-oriented scheme, the arrhythmia conditions are classified with accuracy of 99.54%, 99.89% using PNN and RBF-NN classifier respectively. The obtained result confirms the superiority of the proposed scheme compared to other published results cited in literature. |
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Keywords: | Cardiac arrhythmia discrete wavelet transform empirical mode decomposition adaptive thresholding features NN classifier |
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