Electrocardiogram (ECG) is widely used for the diagnosis of cardiac arrhythmia conditions. An automatic classification of four beat types Normal (N), premature ventricular contraction (PVC), Supraventricular premature or ectopic beat (SVPB) and Fusion of ventricular and normal beat (FUSION) is implemented using a Multi-class Support Vector Machine (MSVM) and Complex Support Vector Machine (CSVM) algorithms [1]. The ECG signals used in these studies were obtained from the European ST-T Database. A number of beats from different leads and patients were selected for training and evaluating classifier performance. Successful ECG arrhythmia classification usually requires optimizing the following procedures: Pre-processing and beat detection, feature extraction and selection, and classifier optimization. Pre-processing and R peak detection is performed with the WFDB Software Package. This reads the annotation and finds the R (peak) location. R (peak) location used as a reference to detect peaks in other wave such P and T and extract ECG beat. ECG beats are extracted after windowing the signal using 106 samples before the R and 106 samples after the R-peak. Discrete Cosine and Sine transforms or the Discrete Fourier Transform (DFT) were used for feature extraction and dimensionality reduction of the input vector at the input of the classifier. Studies after selecting either 100 or 50 Fourier coefficients for reconstructing individual ECG beats in the feature selection phase were performed. MATLAB software routines were used to train and validate both the CSVM and the Multi-class Support Vector Machine (MSVM) classifier. A Complex kernel function, (Gaussian RBK) with 5-fold cross validation was used for adjusting the kernel values. Sequential minimal optimization (SMO) [2] was used to train the CSVM and compute the corresponding complex hyper-plane parameters. The aim of the study was to improve multi-class SVM by extending traditional SVM algorithms to complex spaces so - s to simultaneously classify four types of heartbeats. Results illustrate that the proposed beat classifier is very reliable, and that it may be adopted for automatic detection of arrhythmia conditions and classification. Accuracies between 86% and 94% are obtained for MSVM and CSVM classification respectively. Using CSVM, a 4 classes problem can be classified rapidly by decomposing it into two distinct SVM tasks. Moreover, the present research confirmed that the use of selected number of Fourier coefficients to approximate the ECG beat signal and compress the input features to the classifier can lead to high classification accuracies and improve the generalization ability of the CSVM classifier. Future work on wavelet pre-processing to further compress the input space of the classifier by generating wavelets on the basis of higher order moment criteria [3] as well as alternative approaches for extending the CSVM input and output spaces to arbitrary dimension using Clifford algebra SVM [4] will be discussed at the conference.
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