Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning
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Beschreibung
This book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and sleeping. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalities suchas positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described. Each chapter starts with the importance, problem statementand motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors’ knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems.
This book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and sleeping. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalitiessuch as positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described. Each chapter starts with the importance, problemstatement and motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors’ knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems.
Advances and applications in biomedical signal & image processing, machine and deep learning are presented The cross disciplinary research in the fields of signal acquisition and sensing, measurement, analysis and biological sciences is described The role of machine & deep learning in relation to processing biomedical signals and the applications in medicine and healthcare is evaluated Several types of biomedical signals are introduced and their role in the prediction, diagnosis and prognosis of healthy and unhealthy conditions are explored Alternative therapy technique based on the biofeedback for health and wellbeing is investigated. The implications of classification of neural signals using machine & deep learning methods are covered
Autor*in
Saeed Mian Qaisar
Themen in »Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning«
Artificial intelligence Biomedical signal processing Classification Chronic obstructive pulmonary (COP) Compression Dimension reduction Deep Learning Electrocardiogram (ECG) Electroencephalogram (EEG) Electromyography (EMG) Electrooculography (EOG) Empirical mode decomposition Fourier analysis Features extraction Non-invasive sensing