Machine Learning for Biomedical Engineers

1. Auflage 2026
ISBN: 978-1-041-06794-8
Verlag: Taylor & Francis Ltd

Buch, Englisch, 344 Seiten, Format (B × H): 178 mm x 254 mm

This book combines machine learning and biomedical engineering to address practical issues in healthcare and biomedical research concentrating on real-world applications including bioinformatics, customized medicine, medical imaging analysis, disease detection, and health monitoring. It contains case studies and examples that show how various machine learning algorithms are used on biomedical data sets. The ethical issues and difficulties unique to using machine learning in biomedical settings, such as data privacy, algorithm bias, and regulatory compliance are also covered.

- Provides a broad introduction to machine learning in biomedicine and biomedical engineering

- Discusses ethical considerations and explainability pertinent to machine learning in bioengineering

- Explores step-by-step tutorials, coding examples, and real-world case studies

- Reviews feature selection, training and evaluating models, preprocessing data, validation techniques tailored to biomedical data

- Includes MATLAB and Python coding programs

This book is aimed at graduate students and researchers in bioengineering and machine learning.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Jeyakumar, Vijay

N, Venkateswaran

Bhatia, Dinesh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik

Inhaltsverzeichnis

1. Introduction to Machine Learning 2. Overview of Medical Data Modalities and Their Representation 3. Methods for Biomedical Data Pre-processing in Machine Learning 4. Feature Engineering of Biomedical Data 5. Machine Learning for Medical Data Classification 6. Deep Learning Models for Medical Data Analysis 7. Deep Learning and Machine Learning Techniques for Early Prediction of Alzheimer 8. A Cardiac Disease Classification of ECG Signal using Hybrid Fuzzy Machine Learning Algorithm 9. Vision Language Model Based Health Data Retrieval and Trend Analysis System for Chronic Diseases in Balochistan 10. High-performance computing in healthcare 11. Ethical and Legal Considerations for Machine Learning and Deep Learning in Biomedical Engineering 12. Machine Learning-Driven Dyslexia Detection Based on Eye-Tracking Data 13. Acoustic Respiratory Analysis for the Screening of Chronic Obstructive Pulmonary Disease using Machine Learning Techniques 14. Comprehensive Feature Insights into Gait Dynamics in Neurodegenerative Diseases: From Spatio-Temporal to Spatio-Spectral Measures 15. AI-Driven Muscle Coordination Prediction System for Upper Limb Movement Using EMG Signals 16. Real-Time Intelligent Patient Monitoring: A Federated Learning and TinyML-Based Approach 17. AI-Driven Face Recognition and Mask Detection for Secure Attendance Management in the Post-Pandemic Era 18. Automated Machine Learning Techniques for predicting genetic disease by classifying chromosome image

Über Autor(innen)

Vijay Jeyakumar is an Associate Professor in the Department of Biomedical Engineering at SSN College of Engineering, Chennai, with more than 18 years of academic and research experience. He completed his Ph.D. in Medical Informatics (2015) and M.E., Medical Electronics (2008) from Anna University.

Currently, Dr. Vijay is supervising five Ph.D. students and has successfully guided three research scholars. He is a recognized supervisor of Anna University, Chennai, and a doctoral committee member for several scholars of deemed-to-be Universities. He has published over 80 papers in peer-reviewed journals and conferences and authored 14 book chapters on topics such as medical image retrieval, brain-computer interfaces, machine learning, and deep learning. He has secured funding from organizations including IEEE (USA), NAAC, CSIR, TANSCST, and ISTE for workshops, seminars, and training programs and is involved in four projects funded by DST, TANSCST, AICTE, NASF, and SSN Trust.

He has also received the P K Das Memorial Award and the Imagine Award 2024 for Educational Excellence from Autodesk Corporation. He is the recipient of SSN best teacher award. His six patenting applications are published by Intellectual property India and his three patents are granted. Additionally, he is a Board of Studies member nominated by Anna University, Chennai, and has chaired the Biomedical Engineering boards at both Anna University and Vel Tech University, Chennai. He has been invited as an Academic Auditor by several Technical Institutions.

Dr. J. Vijay is an Editorial board member for three journals and a reviewer for peer-reviewed journals like the Journal of Neuro Computing, Journal of Biomedical & Science Engineering, and many more. Also, he has visited several southeast universities to know modern educational pedagogy initiatives.

Venkateswaran N is a Professor in the Department of Electronics and Communication Engineering at Sri Sivasubramaniya Nadar College of Engineering, Chennai, where he also serves as the Coordinator of the Internal Quality Assurance Cell (IQAC). He holds a Ph.D. in Information and Communication Engineering from Anna University and an M.Tech from Pondicherry University.

With over 28 years of experience in teaching and research, complemented by six years of industry experience, Dr. Venkateswaran has developed extensive expertise in image and signal processing, biomedical signal processing, deep learning, wireless communication, machine learning, and photonics system design.

He has authored and co-authored more than 120 research papers published in reputed international journals and conferences. In addition to his research contributions, he has actively organized faculty development programs, workshops, and academic events, contributing significantly to academic and professional development within the engineering community. In recognition of his excellence in teaching, he received the Best Teacher Award for the academic year 2021–2022. He also successfully mentored a student team that secured first place in a Smart India Hackathon 2022 contest.

A long-standing member of IEEE, Dr. Venkateswaran has played an active role in the IEEE Signal Processing Society, Madras Chapter, where he served as Secretary and later as Chair. He was also the Organizing Chair of WiSPNET 2021 and has facilitated several IEEE Signal Processing Society Distinguished Lectures, strengthening academic–industry collaboration and knowledge exchange.

Dr. Venkateswaran also served as invited speaker and has chaired technical sessions at several prestigious international conferences, including IEEE TENCON 2016 in Singapore and the 53rd IETE Mid-Term Symposium in Nepal. More recently, he participated in ICASSP 2024 in Seoul and ICASSP 2025, further enhancing his engagement with the global research community.

Dinesh Bhatia earned his Ph.D. in Biomechanics and Rehabilitation Engineering from MNNIT Allahabad in 2010, after completing his Bachelor’s (2002) and Master’s (2004) degrees in Biomedical Engineering from Mumbai University. He also holds an MBA with dual specialization from IMT Ghaziabad (2007). He is currently a Professor in the Department of Biomedical Engineering at North Eastern Hill University (NEHU), Shillong, where he previously served as Associate Professor since 2013. Before joining NEHU, he worked as Assistant Professor (Sr. Grade) at DCRUST, Murthal, from 2006 to 2013.

He received the prestigious BOYSCAST Young Scientist Award (2011–12) to pursue research in osteoarthritis at Florida International University, USA, and the INAE Fellowship Award in 2011. He was also selected by ICMR as one of India’s twelve young biomedical scientists for research in sensory prosthetics at the University of Glasgow (2014–15). His international training includes biomechanics and gait analysis in Germany and neuromodulation techniques in Russia.

With over 20 years of teaching and research experience, Prof. Bhatia has published more than 350 articles, 14 books, and 36 book chapters, and holds multiple patents. His research spans muscle mechanics, joint dynamics, medical instrumentation, rehabilitation engineering, signal and image processing, and sustainable healthcare innovations.