TY - EJOU
AU - Sathishkumar, S.
AU - Priya, R. Devi
TI - A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification
T2 - Intelligent Automation \& Soft Computing
PY - 2023
VL - 35
IS - 1
SN - 2326-005X
AB - Electrocardiogram (ECG) is a diagnostic method that helps to assess and record the electrical impulses of heart. The traditional methods in the extraction of ECG features is inneffective for avoiding the computational abstractions in the ECG signal. The cardiologist and medical specialist find numerous difficulties in the process of traditional approaches. The specified restrictions are eliminated in the proposed classifier. The fundamental aim of this work is to find the R-R interval. To analyze the blockage, different approaches are implemented, which make the computation as facile with high accuracy. The information are recovered from the MIT-BIH dataset. The retrieved data contain normal and pathological ECG signals. To obtain a noiseless signal, Gabor filter is employed and to compute the amplitude of the signal, DCT-DOST (Discrete cosine based Discrete orthogonal stock well transform) is implemented. The amplitude is computed to detect the cardiac abnormality. The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified. The Genetic algorithm (GA) retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification. In addition, the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement. Finally, the RBFNN (Radial basis function neural network) is applied, which diminishes the local minima present in the signal. It shows enhancement in characterizing the ordinary and anomalous ECG signals.
KW - Electrocardiogram signal; gabor filter; discrete cosine based discrete orthogonal stock well transform; genetic algorithm; radial basis function neural network
DO - 10.32604/iasc.2023.023817