Early Detection of Neurodegenerative Diseases from Bio-signals PDF Download
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Author: Shamaila Iram
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Shamaila Iram
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Erika Rovini
Publisher: Frontiers Media SA
ISBN: 2832519210
Category : Science
Languages : en
Pages : 120
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Book Description
Author: Paul, Sudip
Publisher: IGI Global
ISBN: 1522585680
Category : Medical
Languages : en
Pages : 376
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Book Description
While doctors and physicians are more than capable of detecting diseases of the brain, the most agile human mind cannot compete with the processing power of modern technology. Utilizing algorithmic systems in healthcare in this way may provide a way to treat neurological diseases before they happen. Early Detection of Neurological Disorders Using Machine Learning Systems provides innovative insights into implementing smart systems to detect neurological diseases at a faster rate than by normal means. The topics included in this book are artificial intelligence, data analysis, and biomedical informatics. It is designed for clinicians, doctors, neurologists, physiotherapists, neurorehabilitation specialists, scholars, academics, and students interested in topics centered on biomedical engineering, bio-electronics, medical electronics, physiology, neurosciences, life sciences, and physics.
Author: Saeid Sanei
Publisher: John Wiley & Sons
ISBN: 1118691237
Category : Science
Languages : en
Pages : 312
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Book Description
Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods. Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.
Author: Philip V. Peplow
Publisher:
ISBN: 9781071617120
Category : Biochemical markers
Languages : en
Pages : 565
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Book Description
This volume presents recent data on the latest achievements in new and emerging technologies for biomarkers and for innovations in their assessment. The chapters cover topics such as activation of microglia and macrophages in neurodegenerative diseases; oxidative stress and cellular dysfunction in neurodegenerative diseases; TSPO PET imaging as a biomarker of neuroinflammation in neurodegenerative disorders; and imaging biomarkers in Huntington's disease and amyotrophic lateral sclerosis. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and comprehensive, Neurodegenerative Diseases Biomarkers: Towards Translating Research to Clinical Practice is a valuable resource for both experimental and clinical experts in the field of neurodegenerative diseases who are looking to expand their knowledge of novel biomarkers in different types of neurodegenerative diseases.
Author: Jesus Avila
Publisher: Frontiers E-books
ISBN: 288919261X
Category : Medicine (General)
Languages : en
Pages : 114
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Book Description
Neurofibrillary tangles (NFTs) composed of intracellular aggregates of tau protein are a key neuropathological feature of Alzheimer’s Disease (AD) and other neurodegenerative diseases, collectively termed tauopathies. The abundance of NFTs has been reported to correlate positively with the severity of cognitive impairment in AD. However, accumulating evidences derived from studies of experimental models have identified that NFTs themselves may not be neurotoxic. Now, many of tau researchers are seeking a “toxic” form of tau protein. Moreover, it was suggested that a “toxic” tau was capable to seed aggregation of native tau protein and to propagate in a prion-like manner. However, the exact neurotoxic tau species remain unclear. Because mature tangles seem to be non-toxic component, “tau oligomers” as the candidate of “toxic” tau have been investigated for more than one decade. In this topic, we will discuss our consensus of “tau oligomers” because the term of “tau oligomers” [e.g. dimer (disulfide bond-dependent or independent), multimer (more than dimer), granular (definition by EM or AFM) and maybe small filamentous aggregates] has been used by each researchers definition. From a biochemical point of view, tau protein has several unique characteristics such as natively unfolded conformation, thermo-stability, acid-stability, and capability of post-translational modifications. Although tau protein research has been continued for a long time, we are still missing the mechanisms of NFT formation. It is unclear how the conversion is occurred from natively unfolded protein to abnormally mis-folded protein. It remains unknown how tau protein can be formed filaments [e.g. paired helical filament (PHF), straight filament and twisted filament] in cells albeit in vitro studies confirmed tau self-assembly by several inducing factors. Researchers are still debating whether tau oligomerization is primary event rather than tau phosphorylation in the tau pathogenesis. Inhibition of either tau phosphorylation or aggregation has been investigated for the prevention of tauopathies, however, it will make an irrelevant result if we don’t know an exact target of neurotoxicity. It is a time to have a consensus of definition, terminology and methodology for the identification of “tau oligomers”.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799834425
Category : Medical
Languages : en
Pages : 671
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Book Description
Cognitive impairment, through Alzheimer’s disease or other related forms of dementia, is a serious concern for afflicted individuals and their caregivers. Understanding patients’ mental states and combatting social stigmas are important considerations in caring for cognitively impaired individuals. Technology is playing an increasing role in the lives of the elderly. One of the most prevalent developments for the aging population is the use of technological innovations for intervention and treatment of individuals with mental impairments. Research Anthology on Diagnosing and Treating Neurocognitive Disorders examines the treatment, diagnosis, prevention, and therapeutic and technological interventions of neurodegenerative disorders. It also describes programs and strategies that professional and family caregivers can implement to engage and improve the quality of life of persons suffering from cognitive impairment. Highlighting a range of topics such as dementia, subjective wellbeing, and cognitive decline, this publication is an ideal reference source for speech pathologists, social workers, occupational therapists, psychologists, psychiatrists, neurologists, pediatricians, researchers, clinicians, and academicians seeking coverage on neurocognitive disorder identification and strategies for clinician support and therapies.
Author: Shamim I. Ahmad
Publisher: Springer Science & Business Media
ISBN: 1461406536
Category : Medical
Languages : en
Pages : 421
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Book Description
The editor of this volume, having research interests in the field of ROS production and the damage to cellular systems, has identified a number of enzymes showing ·OH scavenging activities details of which are anticipated to be published in the near future as confirmatory experiments are awaited. It is hoped that the information presented in this book on NDs will stimulate both expert and novice researchers in the field with excellent overviews of the current status of research and pointers to future research goals. Clinicians, nurses as well as families and caregivers should also benefit from the material presented in handling and treating their specialised cases. Also the insights gained should be valuable for further understanding of the diseases at molecular levels and should lead to development of new biomarkers, novel diagnostic tools and more effective therapeutic drugs to treat the clinical problems raised by these devastating diseases.
Author: Rani, Geeta
Publisher: IGI Global
ISBN: 1799827437
Category : Medical
Languages : en
Pages : 586
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Book Description
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.
Author: Amit Kumar Tyagi
Publisher: John Wiley & Sons
ISBN: 1119785723
Category : Computers
Languages : en
Pages : 532
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Book Description
The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.
