WITHDRAWN: Robust convolutional neural network for arrhythmia prediction in ECG signals

Abstract

In recent years the contribution of big data in the field of health care is very high, this paper focuses on ECG big data, to classify the presence or absence of arrhythmia in Electrocardiogram (ECG) signals. The main objective of the work is to handle the high feature dimensionality of the ECG dataset more prominently to detect the arrhythmia, unlike, existing models instead of using entire features of the ECG signal only the potential features are involved in detection process. This is achieved by developing a boosted feature selection model which discovers the significant features of the ECG signals which mainly involves in detection of arrhythmia. The gradient boosting random forest tree is used for feature subset selection, with the obtained feature set the classification is done using Robust Convolutional Neural Network (RCNN). RCNN comprised of several layers, it performs the convolutional computation on the reduced feature set instead of using entire features to classify the ECG signals as normal or abnormal. This work used two different types of datasets namely MIT-BIH arrhythmia dataset and UCI arrhythmia dataset. From the simulation results it is observed that while comparing with Long Short-Term Memory (LSTM) and standard Deep Neural Network (DNN) the performance of the proposed robust Convolutional neural network performs produce better accuracy in ECG signal classification.