Multi-lead ECG signal analysis using RBFNN-MSO algorithm

In this paper, we present a method for electrocardiogram beat based on multi swarm optimization and radial basis function neural network. ECG is a non-surgical method for measuring and recording the electrical activity of the heart and on many occasions, an experienced cardiologist may not be available on the patient’s site. Therefore, a type of automated ECG analysis is required for the patient to take the electrocardiogram by a general practitioner or paramedical team attending the patient’s location. There is a need for automated ECG analysis. Finally, this paper gives the best analysis methodology for the automated analysis of multi-channel ECG signals. Diagnosis may be affected by the presence of artifacts and noise in multi-channel ECG signals. Some researchers calculated dynamic cutoff frequency parameter from noisy ECG signals to remove noise using the neural network method of Radial Basis function with particle swarm improvement method (RBFNN-PSO). But PSO only has Swarm and it takes a lot of time to give a response. To overcome these limitations, an improved version of the RBFNN-PSO algorithm called Radial Basis Function Neural Network with Multi Swarm Optimization (RBFNN-MSO) has been proposed. Finally, the cutoff frequency parameter is determined by the RBFNN-MSO methodology that is applied to digital low-frequency filters for impulse response (FIR). The next step after removing noise from multi-channel ECG signals is the feature extraction and reduction process. 24 features of the patient’s multi-channel ECG signals are extracted. The next part of the research is divided into two steps. The first step is whether or not the patient’s ECG signals are affected. The vector machine is supported with particle swarm improvement (SVM-PSO) and another way is to support the vector machine with multi swarm improvement (SVM-MSO) to detect ECG signals of the affected patient or not. Finally, SVM-MSO offers greater accuracy compared to SVM-PSO. When compared to all other existing architecture results, they used the rating with 86% of all the test accuracy. But in this paper, our proposed work has 90% overall in different situations. In another point of view also,our proposed work has proven that average accuracy is over 85% even then train data set is small.