Complex Support Vector Machine Research Articles

Analysis of ECG-based arrhythmia detection system using machine learning

The 3D Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) are used in this study to analyze and characterize Electrocardiogram (ECG) signals. This technique consists of three stages: ECG signal preprocessing, feature extraction, and ECG signal order. The 3D wavelet transform is a signal preprocessing technique, de-noising, along with wavelet coefficient extraction.•SVM is used to categorize the ECG through each of the nine heartbeat types recognized by the various classifiers. For this work, around 6400 ECG beats were looked at over the China Physiological Signal Challenge (CPSC) 2018 arrhythmia dataset.•The best degree of exactness was acquired when level 4 rough constants with Symlet-8 (Sym8) channel were utilized for arrangement. Utilizing the ECG signals from CPSC 2018 data set, the SVM classifier has a normal precision of 99.02%, which is much better than complex support vector machine (CSVM) 98.5%, and weighted support vector machine (WSVM) 99%.•The suggested approach is far superior to others in terms of accuracy, and classification of several diseases of arrhythmia.

Arrhythmia detection using multi-lead ECG spectra and Complex Support Vector Machine Classifiers

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This work investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. Four beat types, Normal (N), Premature Ventricular Contraction (PVC), Atrial Premature Contraction (APC) and Right Bundle Branch Block Beat (RBBB) are simultaneously presented to a Complex Support Vector Machine (CSVM) classifier. The ECG signals are obtained from the St Petersburg INCART 12-lead Arrhythmia Database (INCARTDB). The detection of ECG Wave (P, QRS, T) is performed with the Wave Form Database (WFDB) Software Package which is used to read the annotation files and find the R (peak) location. For feature extraction, the Discrete Fourier Transform (DFT) is used. Fifty Fourier coefficients were selected for reconstructing individual ECG beats. This ensures moderate dimensionality reduction and de-noising of the input vector to the classifier. ECG beats classification is performed using CSVM with several training and test datasets. Sequential Minimal Optimization (SMO) is used to train the CSVM and compute the hyperplane parameters associated with both the real and complex hyperplanes. Cross validation is used for finding the best parameter values of the SVM and the two Gaussian RBK kernel functions. The aim of the study is to establish the advantage of CSVM over standard SVM in simultaneously detecting different types of arrhythmias on the basis of multi-lead recordings following signal compression in the Fourier domain. Implementation of the algorithms was performed in MATLAB. The CSVM classification algorithm provided better performance than the standard SVM classifier. The classification accuracy of the proposed scheme is 98.25% using CSVM. Future work will concentrate on the further development of ECG signal pre-processing using adaptive wavelet algorithms as well as classification with Clifford SVMs.