Deep Learning for Data Analytics

Foundations, Biomedical Applications, and Challenges

Himansu Das, Chittaranjan Pradhan, Nilanjan Dey

• Publisher: Academic Press
• Year: 2020
• ISBN:
  9780128226087
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.

• Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications.
• Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks
• Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning

Chapter one

Short and noisy electrocardiogram classification based on deep learning

Sinam Ajitkumar Singh¹ and Swanirbhar Majumder², ¹Department of ECE, NERIST, Nirjuli, India, ²Department of IT, Tripura University, Agartala, India

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.

Keywords

ECG; PCG; wavelet transform; CNN; scalogram; deep learning

1.1 Introduction

Cardiac abnormalities are the signs of disorder of the heart. Abnormalities include arrhythmias, coronary artery disease, mitral valve prolapse, and congenital heart disease. The study of heart characteristics is one of the necessary measures in assessing the cardiovascular system. Electrocardiogram (ECG) and phonocardiogram (PCG) are two existing problems in which the former produces due to the electrical movements of the heart while the latter produces due to the routine motion of the heart sounds. In the available literature, several researchers have employed different approaches that assist in evaluating the morphological characteristics of the ECG signals; they signify various cardiovascular abnormalities by analyzing the ECG [1–4]. The different approaches include support vector machines (SVM) [5], multilayer perceptron (MLP) [6], learning vector quantization (LVQ) [7], high order statistic [8], and K nearest neighbors (KNN) [9].

Arrhythmia is the most common of the diseases associated with heart abnormalities. As a result, most of the literature has dealt with arrhythmia classification [1,2,5,7–9]. The most efficient approach for predicting arrhythmia is the exploration of ECG signals [10]. The study of specific characteristics of ECG recording like beats, morphological and statistical features gives meaningfully correlated clinical data that further helps in predicting ECG pattern. Automated ECG classification is a complicated task as the features associated with morphological and temporal characteristics differ for different subjects under various conditions. Diagnosis of cardiovascular abnormalities using ECG recording has a definite drawback as the ECG signal varies from person to person, and an abnormal ECG signal has different morphological characteristics for related disorders. However, two distinct sets of disorders may have a similar characteristic on an ECG signal. These can cause a problem in the diagnosis of heart abnormalities using the ECG signal [10–12]. The anomalies of t...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Preface
• Chapter one. Short and noisy electrocardiogram classification based on deep learning
• Chapter two. Single-layer convolution neural network for cardiac disease classification using electrocardiogram signals
• Chapter three. Generalization performance of deep autoencoder kernels for identification of abnormalities on electrocardiograms
• Chapter four. Deep learning for early diagnosis of Alzheimer’s disease: a contribution and a brief review
• Chapter five. Musculoskeletal radiographs classification using deep learning
• Chapter six. Deep-wavelet neural networks for breast cancer early diagnosis using mammary termographies
• Chapter seven. Deep learning on information retrieval and its applications
• Chapter eight. Electrical impedance tomography image reconstruction based on autoencoders and extreme learning machines
• Chapter nine. Crop disease classification using deep learning approach: an overview and a case study
• Index