A new technique for ECG signal classification genetic algorithm Wavelet Kernel extreme learning machine

The examination and classification of Electrocardiogram (ECG) records have become particularly significant for diagnosing heart diseases. Machine learning methods are widely used in classifying ECG signals. In this study, Physikalisch-Technische Bundesanstalt Diagnostic ECG Database (PTBDB) from Physionet Database was used to classify ECG signals. Pan-Tompkins algorithm and Discrete Wavelet Transform (DWT) methods were used for extracting critical points such as QRS complex, PR, ST and QT of ECG signal. Afterwards, Traditional Extreme Learning Machine (ELM) was implemented to the ECG signal. Finally, Genetic Algorithm on software Genetic Algorithm Wavelet Kernel Extreme Learning Machine was improved for the determination of the coefficients, which were used in the Wavelet Kernel Extreme Learning Machine algorithm, in which wavelet function was accomplished. In this scope, it was observed that best classification performance values were reached to Acc 95%, Se 100% and Spe 80% with the implementation developed with the Genetic algorithm.