Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review
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Tarun Kumar Vashishth
Abstract
Biomedical signal processing plays a vital role in analyzing and interpreting signals obtained from various physiological processes. With the advent of machine learning techniques, researchers have explored their potential to revolutionize biomedical signal processing by enabling more accurate and efficient analysis. In this comprehensive review, we delve into the application of machine learning models in biomedical signal processing, highlighting their benefits, challenges, and recent advancements. The field of biomedical signal processing encompasses a wide range of signals, such as electrocardiogram, oxygen rate, pulse, electroencephalogram and electromyogram, each requiring specific processing techniques. We begin by providing an overview of the various types of biomedical signals and relative characteristics. The electrocardiogram (ECG) is a widely studied signal that represents the heart electrical activity. We discuss challenges associated with analysis of ECG, like removal of noise, detection of beat, and arrhythmia classification. We then explore how machine learning models, such as convolutional neural networks and recurrent neural networks, have been applied to improve ECG analysis accuracy, including abnormality detection, arrhythmia classification, and risk prediction. The electroencephalogram (EEG) is another significant biomedical signal that records the brain’s electrical activity. We examine the complexities involved in EEG processing, such as artifact removal, feature extraction, and brain state classification. Machine learning algorithms, including deep learning models such as deep neural networks (DNNs) with generative adversarial networks (GANs), have demonstrated promising results in EEG-based tasks, such as seizure detection, cognitive state recognition, and brain-computer interface (BCI) systems.
Abstract
Biomedical signal processing plays a vital role in analyzing and interpreting signals obtained from various physiological processes. With the advent of machine learning techniques, researchers have explored their potential to revolutionize biomedical signal processing by enabling more accurate and efficient analysis. In this comprehensive review, we delve into the application of machine learning models in biomedical signal processing, highlighting their benefits, challenges, and recent advancements. The field of biomedical signal processing encompasses a wide range of signals, such as electrocardiogram, oxygen rate, pulse, electroencephalogram and electromyogram, each requiring specific processing techniques. We begin by providing an overview of the various types of biomedical signals and relative characteristics. The electrocardiogram (ECG) is a widely studied signal that represents the heart electrical activity. We discuss challenges associated with analysis of ECG, like removal of noise, detection of beat, and arrhythmia classification. We then explore how machine learning models, such as convolutional neural networks and recurrent neural networks, have been applied to improve ECG analysis accuracy, including abnormality detection, arrhythmia classification, and risk prediction. The electroencephalogram (EEG) is another significant biomedical signal that records the brain’s electrical activity. We examine the complexities involved in EEG processing, such as artifact removal, feature extraction, and brain state classification. Machine learning algorithms, including deep learning models such as deep neural networks (DNNs) with generative adversarial networks (GANs), have demonstrated promising results in EEG-based tasks, such as seizure detection, cognitive state recognition, and brain-computer interface (BCI) systems.
Kapitel in diesem Buch
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
Kapitel in diesem Buch
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
