ECG Arrhythmia Detection Using Convolution Neural Network and PCA

Abstract

According to the WHO reports heart disease has been consistently ranked among the top ten causes of human death Therefore there have been many studies dedicated to exploring accurate detection algorithms for arrhythmia These algorithms include the use of morphological image analysis statistical analysis wavelet transform and convolutional neural network (CNN) The goal of this study is to integrate features extracted by principal component analysis (PCA) into CNN to develop an algorithm that can accurately predict the type of Electrocardiography (ECG) arrhythmia The one-dimensional (1D) ECG data and annotations were adopted from the MIT-BIH arrhythmia database First data augmentation is performed to obtain 4 times number of data sources To provide a suitable signal for the input of the CNN network the 1D ECG input signal is treated as a 2D gray image with signal sampling information as one dimension and the signal amplitude as the other dimension To keep the important information of the 1D ECG signal the signal’s principal components are also extracted The 2D image gives the signal timing information and the principal components shows the importance of the wave This information is combined to do the type classification of arrhythmia The experimental results show that the proposed method can perform very well for arrhythmia type classification The experimental results show the proposed method can achieve 99 83% accuracy and 99 33% sensitivity

Date of Award	2020
Original language	English
Supervisor	Ming-Shi Wang (Supervisor)