Chapter one - Short and noisy electrocardiogram classification based on deep learning

Abstract

Electrocardiogram (ECG) contains valuable data that assist in the initial investigation of cardiovascular diseases. Hence, the study of such electrical signals becomes a beneficial issue for many researchers. In this chapter, we shall propose a modified preprocessing and unique classification technique based on deep learning. A set of modified preprocessing steps has been implemented with the delineation of ECG signals using the wavelet transform (WT) followed by elimination of noise based on the Pan and Tompkins algorithm. Preprocessed signals have been converted to scalogram images based on continuous wavelet transform (CWT). Finally, a unique approach using deep learning algorithm for classification of the preprocessed scalogram images has been proposed here. The proposed model in this chapter shall be analytically verified using publicly available data sets “A” of PhysioNet 2016/ CinC challenge. The results show that deep learning based on a convolutional neural network (CNN) efficiently can be used for predicting the cardiovascular anomalies. The chapter begins with a discussion on short and noisy ECG classification and its importance with a brief overview on ECG signal processing. This is followed up with a basic literature survey and analysis of the deep learning technique used in this particular area as well as a general discussion of deep learning in the field of cardiological signals. This chapter also introduces a novel approach based on decision fusion for predicting the heart abnormality and compares the validated results with other existing methods.