Ganesh R. Naik – författare

Signal Processing-Driven AI for Healthcare examines how AI techniques can be applied across four major biosignals-EEG, EMG, EOG, and ECG-to derive clinically meaningful insights. As biomedical data becomes increasingly multimodal, there is a rising need for integrated methodologies that unite these signals within robust, explainable AI pipelines suitable for healthcare environments. This book provides a unified framework that spans data acquisition, preprocessing, feature extraction, modeling, evaluation, and deployment, with an emphasis on reproducibility, practical Python-based implementations, and real-world translation to clinical workflows.Integrates AI-driven signal processing across EEG, EMG, EOG, and ECGPresents end-to-end workflows from data acquisition to deploymentDemonstrates multimodal fusion and clinical decision supportEmphasizes interpretability, validation, and regulatory considerationsFeatures supplementary website to host the datasetOffers ready-to-use Python notebooks and scripts optimized for Google Collaboration

This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal processing of neuroscience applications using modern data science techniques. Provides comprehensible review on biomedical signals nature and acquisition aspects. Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas. Includes computational intelligence, machine learning and biomedical signal processing and analysis. Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis. This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science.

This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal processing of neuroscience applications using modern data science techniques. Provides comprehensible review on biomedical signals nature and acquisition aspects. Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas. Includes computational intelligence, machine learning and biomedical signal processing and analysis. Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis. This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science.

Wearable and nearable technologies are revolutionizing modern healthcare by enabling continuous, non-invasive, and real-time monitoring of physiological, behavioral, and environmental factors. Advancements in sensing technologies, embedded electronics, signal processing, and artificial intelligence have greatly enhanced the capabilities of wearables and nearables, making them integral to preventive healthcare, personalized medicine, remote patient monitoring, and digital therapeutics. From smartwatches and rings to ambient, nearable sensors installed in homes and healthcare settings, these technologies are revolutionizing how health data is collected, processed, and interpreted.This edited book covers original research, implementations, and comprehensive reviews to discuss recent achievements and emerging trends in wearable and nearable technology for health-related applications. The book covers aspects of signal acquisition, preprocessing, feature extraction, machine learning, and deep learning methodologies specific to data from wearable and nearable technologies. It also covers practical issues related to data reliability, clinical validation, regulatory issues, and ethical issues, as well as cloud computing, IoT, and IoMT infrastructures.Features:· Explores advanced sensing and signal processing techniques for wearable and nearable health technologies.· Discusses real-time monitoring, cloud-based analytics, and IoT/IoMT integration.· Addresses clinical validation, regulatory, and ethical challenges of wearable and nearable systems.· Presents case studies and applications in smart health monitoring, sleep analysis, cardiac, and respiratory assessment.· Provides a comprehensive reference for graduate students, researchers, and professionals in biomedical and health informatics.This book bridges the gap between theoretical foundations and practical applications in healthcare and may prove a useful resource for researchers, practitioners, and professionals working in biomedical engineering, signal processing, and intelligent health systems.

Non-invasive measurements of cardio-respiratory signals has improved diagnosis and prognosis in human health. This edited book invites original theoretical, practical, and review chapters aimed at proposing advancements in cardio-respiratory signal processing methods for healthcare applications. Exemplary themes of interest covered in this title include cardio-respiratory signal processing challenges using complex physiological data, novel HRV analysis, and data extracted from modern wearables and biosensors.Features:Provides a detailed explanation and signal processing analysis of cardio-respiratory signals.Reports novel signal processing and time-frequency methods for cardio-respiratory signals.Presents the theoretical basis of cardio-respiratory analysis and state-of-the-art applications.Explains new and improved techniques and theories related to cardio-respiratory signal analysis.Combines the primary knowledge of cardio-respiratory signal analysis and processing, from theory and applications.This book is aimed at researchers and graduate students in biomedical signal processing, signal processing, and electrical engineering.

Time-frequency analysis is critical in biomedical signal analysis, which helps diagnose and monitor physiological conditions such as heart rate variability, seizure detection, and brain-computer interfacing. This edited book includes original theoretical, practical, and review chapters aimed at proposing advancements in time-frequency signal processing methods for biomedical healthcare applications. Exemplary themes of interest include biomedical signal processing challenges in complex physiological data, signals from remote sensors, wearables, and nearables such as ballistography-based sensors.Features:Discusses detailed time-frequency signal processing applications for simple to complex biomedical researchReports novel time-frequency techniques used for biomedical signalsPresents the theoretical basis of time-frequency analysis and state-of-the-art applications tailored for various biomedical problemsProvides a forum for presenting new and improved techniques and theories related to time-frequency analysisCombines the primary knowledge of time-frequency signal analysis and processing, from theory and applicationsThis book is aimed at graduate students and researchers in bioengineering and signal processing.

Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS.

This book collects new results, concepts and further developments of NMF. The open problems discussed include, e.g. in bioinformatics: NMF and its extensions applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining etc. The research results previously scattered in different scientific journals and conference proceedings are methodically collected and presented in a unified form. While readers can read the book chapters sequentially, each chapter is also self-contained. This book can be a good reference work for researchers and engineers interested in NMF, and can also be used as a handbook for students and professionals seeking to gain a better understanding of the latest applications of NMF.

Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.

This book collects new results, concepts and further developments of NMF. This book can be a good reference work for researchers and engineers interested in NMF, and can also be used as a handbook for students and professionals seeking to gain a better understanding of the latest applications of NMF.

This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA.

This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail.

This book discusses recent advances in wearable technologies and personal monitoring devices, covering topics such as skin contact-based wearables (electrodes), non-contact wearables, the Internet of things (IoT), and signal processing for wearable devices.

This book discusses recent advances in wearable technologies and personal monitoring devices, covering topics such as skin contact-based wearables (electrodes), non-contact wearables, the Internet of things (IoT), and signal processing for wearable devices.