Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning

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 such 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, problem statement 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.

本書介紹了生物醫學領域中的現代技術進展和革命。當代感測、物聯網（IoT）和機器學習算法和架構的進步，為移動醫療帶來了新的方法。需要對危急健康狀況的患者進行持續觀察，以便在日常生活活動中（如運動、行走和睡眠期間）監測其健康狀況。這可以通過智能混合現代物聯網框架、無線生物醫學植入物和雲計算來實現。這些解決方案目前正在由醫療和政府機構、研究實驗室和生物醫學公司進行開發和測試。生物醫學信號（如心電圖（ECG）、腦電圖（EEG）、肌電圖（EMG）、心音圖（PCG）、慢性阻塞性肺疾病（COP）、眼球電圖（EoG）、光電容積描記（PPG））和影像模態（如正子發射斷層掃描（PET）、磁共振成像（MRI）和電腦斷層掃描（CT））通過生物醫學傳感器和設備進行非侵入性獲取、測量和處理。這些信號和影像代表了人體心血管、神經、視覺和腦系統的活動和狀態。需要對這些信號和影像進行多通道感測，以達到適當的細粒度，以實現有效的監測和診斷。這將產生大量的數據，手動分析是不可行的。因此，自動化醫療系統正在進化過程中。這些系統主要基於生物醫學信號和影像的獲取和感測、預處理、特徵提取和分類階段。本書描述了當代生物醫學信號感測、預處理、特徵提取和基於智能機器和深度學習的分類算法。每一章節都以重要性、問題陳述和動機開始，並提供了自成一體的描述。因此，每一章節都可以獨立閱讀。據編輯所知，本書是關於非侵入性生物醫學信號感測和處理以及機器和深度學習的進展的綜合彙編。我們相信，本書中提出的理論、算法、實現、應用、方法和挑戰將對現代和有效的醫療系統的設計和開發產生影響和貢獻。

Saeed Mian Qaisar obtained his M.S and Ph.D. degrees in Electrical and Computer Engineering from the Grenoble Alpes University, France in 2005 and 2009 respectively. Subsequently, he did a postdoctoral stay at the University of Bordeaux, France. Afterward, he worked at different R&D positions in France. Currently, he is working as an Associate Professor & Researcher of the Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia. He has been awarded with the Queen Effat Award for Excellence in Teaching, May 2016. He has two patents to his credit and has more than 200 published journal articles, book chapters and conference papers. He is serving as an editor of several international journals and is also on the technical and review committees of several international journals and conferences. His current area of research interest includes Signal processing, Event-driven systems, Circuits and systems, Machine learning, Deep Learning, Sampling theory, and Embedded Systems.

Humaira Nisar has a B.S (Honours) in Electrical Engineering from the University of Engineering and Technology, Lahore, Pakistan, M.S in Nuclear Engineering from Quaid-i-Azam University, Islamabad, Pakistan, another M.S in Mechatronics, and Ph.D. in Information and Mechatronics from Gwangju Institute of Science and Technology, Gwangju, South Korea.

She has more than twenty years of research experience. Currently, she is working as a Full Professor in the Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Kampar, Malaysia. Her research interests include signal and image processing, biomedical imaging, neuro-signal processing and analysis, Brain-Computer Interface, and Neurofeedback. She has published more than 200 international journal and conference papers. She is a senior member of IEEE.

Abdulhamit Subasi is specialized in Artificial Intelligence, Machine Learning, Biomedical Signal and Image Analysis. Concerning application of machine learning to different fields, he wrote more than 30 book chapters and more than 200 published journal and conference papers. He is also author of the books, "Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques" and "Practical Machine Learning for Data Analysis Using Python". Moreover, he is the Editor of the book "Applications of Artificial Intelligence in Medical Imaging". He worked at many institutions as an academician and Georgia Institute of Technology, Georgia,

USA, as a researcher. He has been awarded with the Queen Effat Award for Excellence in Research, May 2018. He worked as a professor of computer science at Effat University, Jeddah, Saudi Arabia between 2015 and 2020. Since 2020, he has been working as a Professor at Faculty of Medicine, University of Turku, Turku, Finland.

Saeed Mian Qaisar於2005年和2009年分別在法國格勒諾布爾阿爾卑斯大學獲得電氣與計算機工程的碩士和博士學位。隨後，他在法國波爾多大學進行博士後研究。之後，他在法國的不同研發職位工作。目前，他在沙特阿拉伯吉達的Effat大學電氣與計算機工程系擔任副教授和研究員。他於2016年5月獲得了「Queen Effat卓越教學獎」。他擁有兩項專利，並發表了200多篇期刊文章、專書章節和會議論文。他擔任多個國際期刊的編輯，並且還是多個國際期刊和會議的技術和審查委員會成員。他目前的研究興趣包括信號處理、事件驅動系統、電路和系統、機器學習、深度學習、取樣理論和嵌入式系統。

Humaira Nisar擁有巴基斯坦拉合爾工程技術大學的電氣工程學士學位、巴基斯坦伊斯蘭堡Quaid-i-Azam大學的核工程碩士學位、韓國光州科學技術學院的機電一體化碩士學位和信息與機電學博士學位。她擁有超過二十年的研究經驗。目前，她在馬來西亞金寶的拉曼大學電子工程系擔任正教授。她的研究興趣包括信號和圖像處理、生物醫學成像、神經信號處理和分析、腦-計算機界面和神經反饋。她發表了200多篇國際期刊和會議論文。她是IEEE的高級會員。

Abdulhamit Subasi專攻人工智能、機器學習、生物醫學信號和圖像分析。在機器學習應用於不同領域方面，他撰寫了30多篇專書章節和200多篇發表的期刊和會議論文。他還是書籍《使用機器學習技術進行生物醫學信號分析的實用指南》和《使用Python進行數據分析的實用機器學習》的作者。此外，他還是書籍《人工智能在醫學影像中的應用》的編輯。他曾在多個機構擔任學者，並在美國喬治亞理工學院擔任研究員。他於2018年5月獲得了「Queen Effat卓越研究獎」。他在2015年至2020年期間在沙特阿拉伯吉達的Effat大學擔任計算機科學教授。自2020年以來，他一直在芬蘭圖爾庫大學醫學院擔任教授。