This book presents an interdisciplinary paradigms of computational intelligence techniques and biomedical signal processing. The computational intelligence techniques outlined in the book will help to develop various ways to enhance and utilize signal processing algorithms in the field of biomedical signal processing. In this book, authors have discussed research, discoveries and innovations in computational intelligence, signal processing, and biomedical engineering that will be beneficial to engineers working in the field of health care systems. The book provides fundamental and initial level theory and implementation tools, so that readers can quickly start their research in these interdisciplinary domains.

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-

Biomedical Signal Processing and Artificial Intelligence in Healthcare is a new volume in the Developments in Biomedical Engineering and Bioelectronics series. This volume covers the basics of biomedical signal processing and artificial intelligence. It explains the role of machine learning in relation to processing biomedical signals and the applications in medicine and healthcare. The book provides background to statistical analysis in biomedical systems. Several types of biomedical signals are introduced and analyzed, including ECG and EEG signals. The role of Deep Learning, Neural Networks, and the implications of the expansion of artificial intelligence is covered. Biomedical Images are also introduced and processed, including segmentation, classification, and detection. This book covers different aspects of signals, from the use of hardware and software, and making use of artificial intelligence in problem solving.Dr Zgallai's book has up to date coverage where readers can find the latest information, easily explained, with clear examples and illustrations. The book includes examples on the application of signal and image processing employing artificial intelligence to Alzheimer, Parkinson, ADHD, autism, and sleep disorders, as well as ECG and EEG signals. Developments in Biomedical Engineering and Bioelectronics is a 10-volume series which covers recent developments, trends and advances in this field. Edited by leading academics in the field, and taking a multidisciplinary approach, this series is a forum for cutting-edge, contemporary review articles and contributions from key 'up-and-coming' academics across the full subject area. The series serves a wide audience of university faculty, researchers and students, as well as industry practitioners. - Coverage of the subject area and the latest advances and applications in biomedical signal processing and Artificial Intelligence - Contributions by recognized researchers and field leaders - On-line presentations, tutorials, application and algorithm examples

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book: Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface Highlights the latest machine learning and deep learning methods for neural signal processing Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

??Applications of Artificial Intelligence in Healthcare and Biomedicine provides ?updated knowledge on the applications of artificial intelligence in medical image analysis. The book starts with an introduction to Artificial Intelligence techniques for Healthcare and Biomedicine. In 16 chapters it presents artificial applications in Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG), signal analysis, Computed Tomography (CT), Magnetic Resonance Imaging (MR) and Ultrasound image analysis. It equips researchers with tools for early breast cancer detection from mammograms using artificial intelligence (AI), AI models to detect lung cancer using histopathological images and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images. It also presents present 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Applications of Artificial Intelligence in Healthcare and Biomedicine closes with a chapter on AI-based approach to forecast diabetes patients' hospital re-admissions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about the applications of Artificial Intelligence and its impact in medical/biomedical image analysis. Provides knowledge on Artificial Intelligence algorithms for clinical data analysis Gives insights into both AI applications in biomedical signal analysis, biomedical image analysis, and applications in healthcare, including drug discovery Equips researchers with tools for early breast cancer detection

Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field. - Covers advances in the multimodal signal processing and artificial intelligence assistive HMIs - Presents theories, algorithms, realizations, applications, approaches, and challenges that will have their impact and contribution in the design and development of modern and effective HMI (Human Machine Interaction) system - Presents different aspects of the multimodal signals, from the sensing to analysis using hardware/software, and making use of machine/ensemble/deep learning in the intended problem-solving

It is with great pleasure to present the proceedings of the International Conference on Computational Intelligence and Mathematical Applications (ICCIMA 2023), held on 21-22 December 2023, at Panipat Institute of Engineering and Technology, Panipat. This conference brought scholars, researchers, professionals, and intellectuals together from diverse fields to exchange ideas, share insights, and foster collaborations in Optimization, Computational Intelligence and Mathematical Applications. The ICCIMA 2023 served as a platform for contributors to demonstrate their latest findings, discuss emerging trends, and explore innovations to the problems that different disciplines are currently experiencing. The conference’s scope and depth of themes reflect our community’s rich diversity of interests and levels of competence.