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| artificial intelligence ecg analysis: Advanced Methods and Tools for ECG Data Analysis Gari D. Clifford, Francisco Azuaje, Patrick E. McSharry, 2006 This practical book is the first one-stop resource to offer a thorough, up-to-date treatment of the techniques and methods used in electrocardiogram (ECG) data analysis, from fundamental principles to the latest tools in the field. The book places emphasis on the selection, modeling, classification, and interpretation of data based on advanced signal processing and artificial intelligence techniques. |
| artificial intelligence ecg analysis: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) IEEE Staff, 2021-12-09 We solicit high quality original research papers (including significant work in progress) in any aspect of bioinformatics, genomics, and biomedicine New computational techniques and methods and their application in life science and medical domains are especially encouraged |
| artificial intelligence ecg analysis: Applications of Machine Learning Prashant Johri, Jitendra Kumar Verma, Sudip Paul, 2020-05-04 This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics. |
| artificial intelligence ecg analysis: ECG Signal Processing, Classification and Interpretation Adam Gacek, Witold Pedrycz, 2011-09-18 The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis; Part II deals with techniques and models of computational intelligence that are suitable for signal processing; and Part III details ECG system-diagnostic interpretation and knowledge acquisition architectures. Illustrative material includes: brief numerical experiments; detailed schemes, exercises and more advanced problems. |
| artificial intelligence ecg analysis: Biomedical Signal Processing Ganesh Naik, 2019-11-12 This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing. |
| artificial intelligence ecg analysis: Feature Engineering and Computational Intelligence in ECG Monitoring Chengyu Liu, Jianqing Li, 2020-06-24 This book discusses feature engineering and computational intelligence solutions for ECG monitoring, with a particular focus on how these methods can be efficiently used to address the emerging challenges of dynamic, continuous & long-term individual ECG monitoring and real-time feedback. By doing so, it provides a “snapshot” of the current research at the interface between physiological signal analysis and machine learning. It also helps clarify a number of dilemmas and encourages further investigations in this field, to explore rational applications of feature engineering and computational intelligence in ECG monitoring. The book is intended for researchers and graduate students in the field of biomedical engineering, ECG signal processing, and intelligent healthcare. |
| artificial intelligence ecg analysis: Precision Medicine and Artificial Intelligence Michael Mahler, 2021-03-12 Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine |
| artificial intelligence ecg analysis: Cardiology of the Horse Celia Marr, Mark Bowen, 2011-01-07 Cardiology of the Horse is a multi-author, contemporary reference on equine cardiology. The first section reviews the physiology, pathophysiology and pharmacology of the equine cardiovascular system. The second section describes diagnostic methods from basic to specialist examination skills and the third section addresses the investigation and management of common clinical problems using a problem-orientated approach. Suitable for students, general and specialist practitioners and teachers.An up-to-date account of current clinical practice in equine cardiology covering: - recent developments in research and practice - problem-orientated approaches helpful to both general and specialist practitioners - clinical management of specific groups from foals and racehorses to geriatric patients - cardiac problems related to exercise, anaesthesia and intensive care |
| artificial intelligence ecg analysis: Modeling Decisions for Artificial Intelligence Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani, 2019-08-26 This book constitutes the refereed proceedings of the 16th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2019, held in Milan, Italy, in September 2019. The 30 papers presented in this volume were carefully reviewed and selected from 50 submissions. They discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: aggregation operators and decision making; data science and data mining; and data privacy and security. |
| artificial intelligence ecg analysis: Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Abdulhamit Subasi, 2019-03-16 Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. - Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction - Explains how to apply machine learning techniques to EEG, ECG and EMG signals - Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series |
| artificial intelligence ecg analysis: Advances in Artificial Intelligence Kunal Pal, Bala Chakravarthy Neelapu, J. Sivaraman, 2024-05-21 Artificial Intelligence in health care has become one of the best assisting techniques for clinicians in proper diagnosis and surgery. In biomedical applications, artificial intelligence algorithms are explored for bio-signals such as electrocardiogram (ECG/ EKG), electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG), blood pressure, heart rate, nerve conduction, etc., and for bio-imaging modalities, such as Computed Tomography (CT), Cone-Beam Computed Tomography (CBCT), MRI (Magnetic Resonance Imaging), etc. Advancements in Artificial intelligence and big data has increased the development of innovative medical devices in health care applications. Recent Advances in Artificial Intelligence: Medical Applications provides an overview of artificial intelligence in biomedical applications including both bio-signals and bio-imaging modalities. The chapters contain a mathematical formulation of algorithms and their applications in biomedical field including case studies. Biomedical engineers, advanced students, and researchers can use this book to apply their knowledge in artificial intelligence-based processes to biological signals, implement mathematical models and advanced algorithms, as well as develop AI-based medical devices. - Covers the recent advancements of artificial intelligence in healthcare, including case studies on how this technology can be used - Provides an understanding of the design of experiments to validate the developed algorithms - Presents an understanding of the versatile application of artificial intelligence in bio-signal and bio-image processing techniques |
| artificial intelligence ecg analysis: Biomedical Signal Processing and Artificial Intelligence in Healthcare Walid A. Zgallai, 2020-07-29 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 |
| artificial intelligence ecg analysis: Deep Medicine Eric Topol, 2019-03-12 A Science Friday pick for book of the year, 2019 One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved. |
| artificial intelligence ecg analysis: Fundamentals of Electrocardiography Edward K. Chung, 1984 |
| artificial intelligence ecg analysis: Database Systems for Advanced Applications. DASFAA 2020 International Workshops Yunmook Nah, Chulyun Kim, Seon-Young Kim, Yang-Sae Moon, Steven Euijong Whang, 2020-09-21 The LNCS 12115 constitutes the workshop papers which were held also online in conjunction with the 25th International Conference on Database Systems for Advanced Applications in September 2020. The complete conference includes 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions. DASFAA 2020 presents this year following five workshops: The 7th International Workshop on Big Data Management and Service (BDMS 2020) The 6th International Symposium on Semantic Computing and Personalization (SeCoP 2020) The 5th Big Data Quality Management (BDQM 2020) The 4th International Workshop on Graph Data Management and Analysis (GDMA 2020) The 1st International Workshop on Artificial Intelligence for Data Engineering (AIDE 2020) |
| artificial intelligence ecg analysis: Artificial Intelligence in Medicine Joseph Finkelstein, |
| artificial intelligence ecg analysis: Secure Edge Computing Mohiuddin Ahmed, Paul Haskell-Dowland, 2021-08-12 The internet is making our daily life as digital as possible and this new era is called the Internet of Everything (IoE). Edge computing is an emerging data analytics concept that addresses the challenges associated with IoE. More specifically, edge computing facilitates data analysis at the edge of the network instead of interacting with cloud-based servers. Therefore, more and more devices need to be added in remote locations without any substantial monitoring strategy. This increased connectivity and the devices used for edge computing will create more room for cyber criminals to exploit the system’s vulnerabilities. Ensuring cyber security at the edge should not be an afterthought or a huge challenge. The devices used for edge computing are not designed with traditional IT hardware protocols. There are diverse-use cases in the context of edge computing and Internet of Things (IoT) in remote locations. However, the cyber security configuration and software updates are often overlooked when they are most needed to fight cyber crime and ensure data privacy. Therefore, the threat landscape in the context of edge computing becomes wider and far more challenging. There is a clear need for collaborative work throughout the entire value chain of the network. In this context, this book addresses the cyber security challenges associated with edge computing, which provides a bigger picture of the concepts, techniques, applications, and open research directions in this area. In addition, the book serves as a single source of reference for acquiring the knowledge on the technology, process and people involved in next generation computing and security. It will be a valuable aid for researchers, higher level students and professionals working in the area. |
| artificial intelligence ecg analysis: Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Rani, Geeta, Tiwari, Pradeep Kumar, 2020-10-16 By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students. |
| artificial intelligence ecg analysis: Practical Artificial Intelligence for Internet of Medical Things Ben Othman Soufiene, Chinmay Chakraborty, Faris A. Almalki, 2023-02-28 This book covers the fundamentals, applications, algorithms, protocols, emerging trends, problems, and research findings in the field of AI and IoT in smart healthcare. It includes case studies, implementation and management of smart healthcare systems using AI. Chapters focus on AI applications in Internet of Healthcare Things, provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and AI, with the real-world examples. This book is aimed at Researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics. Features: Focus on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare services Discusses artificial intelligence applications, experiments, core concepts, and cutting-edge themes Demonstrates new approaches to analyzing medical data and identifying ailments using AI to improve overall quality of life Introduces fundamental concepts for designing the Internet of Healthcare Things solutions Includes pertinent case studies and applications This book is aimed at researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics. |
| artificial intelligence ecg analysis: Advancement of Artificial Intelligence in Healthcare Engineering Dilip Singh Sisodia, Ram Bilas Pachori, Lalit Garg, 2020 This book explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of challenging healthcare engineering solutions-- |
| artificial intelligence ecg analysis: Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care Murugan, Thangavel, W., Jaisingh, P., Varalakshmi, 2024-07-22 Artificial intelligence (AI) has emerged as a transformative force across various domains, revolutionizing the way we perceive and address challenges in healthcare. The convergence of AI and healthcare holds immense promise, offering unprecedented opportunities to enhance medical diagnosis, treatment, and patient care. In todays world, the intersection of AI and healthcare stands as one of the most promising frontiers for innovation and progress. Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care embodies this convergence, offering a comprehensive exploration of how AI is revolutionizing various aspects of healthcare delivery. At its core, this book addresses the urgent need for more effective and efficient healthcare solutions in an increasingly complex and data-rich environment. Covering topics such as chronic disease, image classification, and precision medicine, this book is an essential resource for healthcare professionals, medical researchers, AI and machine learning specialists, healthcare administrators and executives, medical educators and students, biomedical engineers, healthcare IT professionals, policy makers and regulators, academicians, and more. |
| artificial intelligence ecg analysis: Machine Learning Algorithms and Applications Mettu Srinivas, G. Sucharitha, Anjanna Matta, 2021-08-10 Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program. |
| artificial intelligence ecg analysis: Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing Rajesh Kumar Tripathy, Ram Bilas Pachori, 2024-06-12 Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals.In addition, this book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered. - Provides details regarding the application of various signal processing and machine learning-based methods for cardiovascular signal analysis - Covers methodologies as well as experimental results and studies - Helps readers understand the use of different cardiac signals such as ECG, PCG, and PPG for the automated detection of heart ailments and other related biomedical applications |
| artificial intelligence ecg analysis: Electrocardiogram in Clinical Medicine Michael J. Lipinski, Andrew E. Darby, Michael C. Bond, Nathan P. Charlton, Korin B. Hudson, Kelly Williamson, 2020-12-07 Offers a guide for a complete understanding of the disease and conditions most frequently revealed in ECGs recorded in the acute, critical, and emergency care settings Electrocardiogram in Clinical Medicine offers an authoritative guide to ECG interpretation that contains a focus and perspective from each of the three primary areas of medical care: acute care, critical care and emergency care. It can be used as a companion with the book ECGs for the Emergency Physician I & II (by Mattu and Brady) or as a stand-alone text. These three books can be described as a cumulative EGG reference for the medical provider who uses the electrocardiogram on a regular basis. Electrocardiogram in Clinical Medicine includes sections on all primary areas of ECG interpretation and application as well as sections that highlight use, devices and strategies. The medical content covers acute coronary syndromes and all related issues, other diseases of the myocardium, morphologic syndromes, toxicology and paediatrics; dysrhythmias will also be covered in detail. This important resource: • Goes beyond pattern recognition in ECGs to offer a real understanding of the clinical syndromes evidenced in ECGs and implications for treatment • Covers the indications, advantages and pitfalls of the use of ECGs for diagnosis in all acute care settings, from EMS to ED to Critical Care • Examines the ECG in toxic, metabolic and environmental presentations; critical information for acute care clinicians who need to be able to differentiate ODs, poisoning and other environmental causes from MI or other cardiac events • Facilitates clinical decision-making Written for practicing ER, general medicine, family practice, hospitalist and ICU physicians and medical students, Electrocardiogram in Clinical Medicine is an important book for the accurate interpretation of EGG results. |
| artificial intelligence ecg analysis: Artificial Intelligence and Evolutionary Computations in Engineering Systems Subhransu Sekhar Dash, Paruchuri Chandra Babu Naidu, Ramazan Bayindir, Swagatam Das, 2018-03-19 The book is a collection of high-quality peer-reviewed research papers presented in the International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES 2017). The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. Researchers from academia and industry have presented their original work and ideas, information, techniques and applications in the field of communication, computing and power technologies. |
| artificial intelligence ecg analysis: Artificial Intelligence Applications for Health Care Mitul Kumar Ahirwal, Narendra D. Londhe, Anil Kumar, 2022-04-15 This book takes an interdisciplinary approach by covering topics on health care and artificial intelligence. Data sets related to biomedical signals (ECG, EEG, EMG) and images (X-rays, MRI, CT) are explored, analyzed, and processed through different computation intelligence methods. Applications of computational intelligence techniques like artificial and deep neural networks, swarm optimization, expert systems, decision support systems, clustering, and classification techniques on medial datasets are explained. Survey of medical signals, medial images, and computation intelligence methods are also provided in this book. Key Features Covers computational Intelligence techniques like artificial neural networks, deep neural networks, and optimization algorithms for Healthcare systems Provides easy understanding for concepts like signal and image filtering techniques Includes discussion over data preprocessing and classification problems Details studies with medical signal (ECG, EEG, EMG) and image (X-ray, FMRI, CT) datasets Describes evolution parameters such as accuracy, precision, and recall etc. This book is aimed at researchers and graduate students in medical signal and image processing, machine and deep learning, and healthcare technologies. |
| artificial intelligence ecg analysis: Artificial Intelligence Marco Antonio Aceves-Fernandez, 2018-06-27 Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area. |
| artificial intelligence ecg analysis: A Biologist’s Guide to Artificial Intelligence Ambreen Hamadani, Nazir A Ganai, Hamadani Henna, J Bashir, 2024-03-15 A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms. Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning Equips with new data mining strategies an easy interface into the world of Artificial Intelligence Enables researchers to enhance their own sphere of researching Artificial Intelligence |
| artificial intelligence ecg analysis: Biomedical Signal and Image Processing with Artificial Intelligence Chirag Paunwala, Mita Paunwala, Rahul Kher, Falgun Thakkar, Heena Kher, Mohammed Atiquzzaman, Norliza Mohd. Noor, 2023-01-09 This book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies. The book covers recent developments in both medical images and signals analyzed by artificial intelligence techniques. The authors also cover topics related to development based artificial intelligence, which includes machine learning, neural networks, and deep learning. This book will provide a platform for researchers who are working in the area of artificial intelligence for biomedical applications. Provides insights into medical signal and image analysis using artificial intelligence; Includes novel and recent trends of decision support system for medical research; Outlines employment of evolutionary algorithms for biomedical data, big data analysis for medical databases, and reliability, opportunities, and challenges in clinical data. |
| artificial intelligence ecg analysis: Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI) Aditya Khamparia, Deepak Gupta, Ashish Khanna, Valentina E. Balas, 2022-04-09 The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. It will discuss the advantages in dealing with big and complex data by using explainable AI concepts in the field of biomedical sciences. The book explains both positive as well as negative findings obtained by explainable AI techniques. It features real time experiences by physicians and medical staff for applied deep learning based solutions. The book will be extremely useful for researchers and practitioners in advancing their studies. |
| artificial intelligence ecg analysis: Deep Learning Techniques and Optimization Strategies in Big Data Analytics Thomas, J. Joshua, Karagoz, Pinar, Ahamed, B. Bazeer, Vasant, Pandian, 2019-11-29 Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry. |
| artificial intelligence ecg analysis: Rapid Interpretation of ECGs in Emergency Medicine Jennifer L. Martindale, David F. M. Brown, 2012-04-20 For a busy clinician in the Emergency Department, the ability to spot a lethal cardiac condition is critical. Rapid Interpretation of ECGs in Emergency Medicine fills a gap in ECG training in an easy-to-use, highly visual format. ECG patterns, gathered from patient records and from the files of physicians at the Harvard-affiliated hospitals, represent the range of pathologies that hospitalists, internal medicine physicians, family medicine physicians, and emergency medicine physicians must recognize. The format of Rapid Interpretation of ECGs in Emergency Medicine is to first show an ECG in its native state to give you the chance to recognize and interpret salient features. The page can then be flipped to look at the same ECG with abnormal patterns enlarged, highlighted in color, and described in brief text. The ECGs are presented with and without annotations so you can test your diagnostic skills. |
| artificial intelligence ecg analysis: Applications of Artificial Intelligence in Engineering Xiao-Zhi Gao, Rajesh Kumar, Sumit Srivastava, Bhanu Pratap Soni, 2021-05-10 This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8–10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence. |
| artificial intelligence ecg analysis: A Comprehensive Guide to Understanding and Managing Arrhythmias Dr. Spineanu Eugenia, 2024-10-16 Are you ready to unlock the secrets of arrhythmias and their life-altering impact on cardiac health? This comprehensive guide dives deep into the intricacies of arrhythmias, offering readers a detailed understanding of the irregularities in heart rhythm, from their physiological roots to cutting-edge treatment options. MASTER THE COMPLEXITIES OF CARDIAC ELECTROPHYSIOLOGY LEARN ABOUT DIFFERENT TYPES OF ARRHYTHMIAS AND THEIR CAUSES DISCOVER THE IMPACT OF ARRHYTHMIAS ON CARDIAC FUNCTION EXPLORE HISTORICAL PERSPECTIVES AND MODERN INNOVATIONS ACCESS REAL-WORLD CASE STUDIES AND CLINICAL INSIGHTS Whether you're a medical student, healthcare provider, or someone looking to understand arrhythmias, this book provides the knowledge needed to navigate through this critical aspect of heart health. With clear explanations and rich clinical insights, this book is your guide to managing and understanding arrhythmias in a comprehensive and approachable way. |
| artificial intelligence ecg analysis: Artificial Intelligence and Information Technologies Arvind Dagur, Dhirendra Kumar Shukla, Nazarov Fayzullo Makhmadiyarovich, Akhatov Akmal Rustamovich, Jabborov Jamol Sindorovich, 2024-07-31 This book contains the proceedings of a non-profit conference with the objective of providing a platform for academicians, researchers, scholars and students from various institutions, universities and industries in India and abroad, and exchanging their research and innovative ideas in the field of Artificial Intelligence and Information Technologies. It begins with exploring the research and innovation in the field of Artificial Intelligence and Information Technologies including secure transaction, monitoring, real time assistance and security for advanced stage learners, researchers and academicians has been presented. It goes on to cover: Broad knowledge and research trends about artificial intelligence and Information Technologies and their role in today’s digital era. Depiction of system model and architecture for clear picture of AI in real life. Discussion on the role of Artificial Intelligence in various real-life problems such as banking, healthcare, navigation, communication, security, etc. Explanation of the challenges and opportunities in AI based Healthcare, education, banking, and related Industries. Recent Information technologies and challenges in this new epoch. This book will be beneficial to researchers, academicians, undergraduate students, postgraduate students, research scholars, professionals, technologists and entrepreneurs. |
| artificial intelligence ecg analysis: Artificial Intelligence for Drug Product Lifecycle Applications Alberto Pais, Carla Vitorino, Sandra Nunes, Tânia Cova, 2024-09-06 Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments.Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. - Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle - Combines timely literature review with clear artworks to improve understanding - Examines deep learning and machine learning in drug discovery |
| artificial intelligence ecg analysis: Machine learning in clinical decision-making Tyler John Loftus, Amanda Christine Filiberto, Ira L. Leeds, Daniel Donoho, 2023-09-07 |
| artificial intelligence ecg analysis: Intelligence-Based Cardiology and Cardiac Surgery Anthony C Chang, Alfonso Limon, Robert Brisk, Francisco Lopez- Jimenez, Louise Y Sun, 2023-09-06 Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides a comprehensive survey of artificial intelligence concepts and methodologies with real-life applications in cardiovascular medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and data science domains. The book's content consists of basic concepts of artificial intelligence and human cognition applications in cardiology and cardiac surgery. This portfolio ranges from big data, machine and deep learning, cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension and pediatric heart care. The book narrows the knowledge and expertise chasm between the data scientists, cardiologists and cardiac surgeons, inspiring clinicians to embrace artificial intelligence methodologies, educate data scientists about the medical ecosystem, and create a transformational paradigm for healthcare and medicine. - Covers a wide range of relevant topics from real-world data, large language models, and supervised machine learning to deep reinforcement and federated learning - Presents artificial intelligence concepts and their applications in many areas in an easy-to-understand format accessible to clinicians and data scientists - Discusses using artificial intelligence and related technologies with cardiology and cardiac surgery in a myriad of venues and situations - Delineates the necessary elements for successfully implementing artificial intelligence in cardiovascular medicine for improved patient outcomes - Presents the regulatory, ethical, legal, and financial issues embedded in artificial intelligence applications in cardiology |
| artificial intelligence ecg analysis: Big Data Analytics and Artificial Intelligence in the Healthcare Industry Machado, José, Peixoto, Hugo, Sousa, Regina, 2022-04-29 Developing new approaches and reliable enabling technologies in the healthcare industry is needed to enhance our overall quality of life and lead to a healthier, innovative, and secure society. Further study is required to ensure these current technologies, such as big data analytics and artificial intelligence, are utilized to their utmost potential and are appropriately applied to advance society. Big Data Analytics and Artificial Intelligence in the Healthcare Industry discusses technologies and emerging topics regarding reliable and innovative solutions applied to the healthcare industry and considers various applications, challenges, and issues of big data and artificial intelligence for enhancing our quality of life. Covering a range of topics such as electronic health records, machine learning, and e-health, this reference work is ideal for healthcare professionals, computer scientists, data analysts, researchers, practitioners, scholars, academicians, instructors, and students. |
| artificial intelligence ecg analysis: Interfacing Bioelectronics and Biomedical Sensing Hung Cao, Todd Coleman, Tzung K. Hsiai, Ali Khademhosseini, 2020-02-13 This book addresses the fundamental challenges underlying bioelectronics and tissue interface for clinical investigation. Appropriate for biomedical engineers and researchers, the authors cover topics ranging from retinal implants to restore vision, implantable circuits for neural implants, and intravascular electrochemical impedance to detect unstable plaques. In addition to these chapters, the authors also document the approaches and issues of multi-scale physiological assessment and monitoring in both humans and animal models for health monitoring and biological investigations; novel biomaterials such as conductive and biodegradable polymers to be used in biomedical devices; and the optimization of wireless power transfer via inductive coupling for batteryless and wireless implantable medical devices. In addition to engineers and researchers, this book is also an ideal supplementary or reference book for a number of courses in biomedical engineering programs, such as bioinstrumentation, MEMS/BioMEMS, bioelectronics and sensors, and more. Analyzes and discusses the electrode-tissue interfaces for optimization of biomedical devices. Introduces novel biomaterials to be used in next-generation biomedical devices. Discusses high-frequency transducers for biomedical applications. |
Artificial intelligence-enhanced electrocardiography in …
In this Review, we focus on the promise, clinical capa-bilities, research opportunities, gaps and risk of the application of AI to the ECG for the diagnosis and man-agement of cardiovascular...
Advancements in Artificial Intelligence for ECG Signal Analysis …
Among the most common models for electrocardiogram (ECG) signal classification and arrhythmia detection are deep learning (DL)-based models. One of these methods is artificial …
Artificial Intelligence Analysis of ECG to Determine ... - medRxiv
Aug 27, 2024 · this, we have developed an artificial intelligence model - ECGio – designed to be deployed at the point of care to determine FFR through the analysis of a resting digital 12-lead …
Interactive ECG annotation: An artificial intelligence method …
ECG annotation is challenging and time-consuming, even for specialist physicians. The shortage of labelled ECG data is one of the essential factors that affect ECG intelligent anal-ysis’s long …
Revolutionizing Cardiology: AI in ECG Analysis Paves the Way …
Jul 25, 2024 · He explains how combining artificial intelligence with ECG analysis and care coordination (Viz HCM) can lift the mask, revealing hidden cases and streamlining the patient …
Artificial Intelligence-based Remote Electrocardiogram …
Remote ECG monitoring can provide valuable information for diagnosis in patients with cardiovascular diseases (CVD), but manually studying large amounts of ECG data can be …
ECG SIGNAL ANALYSIS FOR HEART CONDITION DETECTION …
ECGs are crucial in detecting abnormalities like arrhythmias, but manual interpretation is time-consuming and error-prone. By using ML models such as SVM, Random Forests, and CNN, …
Artificial Intelligence Interpretation of the Electrocardiogram: …
Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG …
Artificial Intelligence Based ECG Analysis Framework - Amatis
Firstly R-Peaks are detected with an adaptive algorithm. Then our first deep learning model (CNN + BiLSTM + Attention) classifies ECG signals as beats and no-beats. Then the predictions are …
The emergence and clinical significance of artificial …
AI-ECG is emerging as a functional biological biomarker with a wide range of applications. This article aims to review and discuss the major research, achievements, and future pros-pects of …
Artificial Intelligence in Cardiology: General Perspectives and …
• Artificial intelligence in cardiology focuses on enhanc-ing diagnostics, risk prediction, treatment planning, and real-time decision support. • Artificial intelligence–powered cardiac imaging and …
Harnessing Machine Learning and Artificial Intelligence for ...
computer simulations for ECG analysis, including supervised and unsupervised learning for dataset analysis, with a focus on heartbeat classification and patient diagnosis.
Analysis of Digitalized ECG Signals Based on Artificial …
analysis of ECG segments representing 16 participants with and without the consumption of anhydrous glucose solution is investigated. Our analysis provides a complete methodology for …
An artificial intelligence-enabled ECG algorithm for the …
Methods We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation …
Artificial intelligence for direct-to-physician reporting of
Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an …
Artificial intelligence analysis of ECG signals to predict …
Jan 13, 2025 · The aim of our study is a proof-of-concept analysis of whether artificial intelligence (AI) analysis of electrocardiogram (ECG) signals can predict arrhythmia recurrence after …
Artificial Intelligence ECG to Detect Left Ventricular …
We sought to review the clinical experience with an artificial intelligence electrocardiogram (AI ECG) to screen for ventricular dysfunction in patients with documented COVID-19.
Evaluation of an Ambulatory ECG Analysis Platform Using …
assess whether an artificial intelligence (AI)- based Holter analysis platform using deep neural networks is noninferior to a con - ventional one used in clinical routine in detecting a major …
Artificial intelligence-guided screening for atrial fibrillation …
We did a pragmatic study to evaluate the efectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognised atrial fibrillation.
Artificial intelligence-enhanced electrocardiogram for …
ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help …
Artificial intelligence-enhanced electrocardiogra…
In this Review, we focus on the promise, clinical capa-bilities, research opportunities, gaps and risk of the application of AI to the ECG for the …
Advancements in Artificial Intelligence for ECG Signa…
Among the most common models for electrocardiogram (ECG) signal classification and arrhythmia detection are deep learning (DL)-based …
Artificial Intelligence Analysis of ECG to Determi…
Aug 27, 2024 · this, we have developed an artificial intelligence model - ECGio – designed to be deployed at the point of care to determine FFR through the …
Interactive ECG annotation: An artificial intelligence …
ECG annotation is challenging and time-consuming, even for specialist physicians. The shortage of labelled ECG data is one of the essential …
Revolutionizing Cardiology: AI in ECG Analysis Paves t…
Jul 25, 2024 · He explains how combining artificial intelligence with ECG analysis and care coordination (Viz HCM) can lift the mask, revealing …
