The Experts below are selected from a list of 93 Experts worldwide ranked by ideXlab platform
William J. Nowack - One of the best experts on this subject based on the ideXlab platform.
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An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs
Journal of Medical Systems, 2001Co-Authors: Steven Walczak, William J. NowackAbstract:Determining the cause of Seizures is a significant medical problem, as misdiagnosis can result in increased morbidity and even mortality of patients. The reported research evaluates the efficacy of using an artificial neural network (ANN) for determining epileptic Seizure occurrences for patients with lateralized bursts of theta (LBT) EEGs. Training and test cases are acquired from examining records of 1,500 consecutive adult Seizure patients. The small resulting pool of 92 patients with LBT EEGs requires using a jack-knife procedure for developing the ANN categorization models. The ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic Seizure or Non-Epileptic Seizure. The original ANN model using eight variables produces a categorization accuracy of 62%. Following a modified factor analysis, an ANN model utilizing just four of the original variables achieves a categorization accuracy of 68%.
Steven Walczak - One of the best experts on this subject based on the ideXlab platform.
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An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs
Journal of Medical Systems, 2001Co-Authors: Steven Walczak, William J. NowackAbstract:Determining the cause of Seizures is a significant medical problem, as misdiagnosis can result in increased morbidity and even mortality of patients. The reported research evaluates the efficacy of using an artificial neural network (ANN) for determining epileptic Seizure occurrences for patients with lateralized bursts of theta (LBT) EEGs. Training and test cases are acquired from examining records of 1,500 consecutive adult Seizure patients. The small resulting pool of 92 patients with LBT EEGs requires using a jack-knife procedure for developing the ANN categorization models. The ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic Seizure or Non-Epileptic Seizure. The original ANN model using eight variables produces a categorization accuracy of 62%. Following a modified factor analysis, an ANN model utilizing just four of the original variables achieves a categorization accuracy of 68%.
Mark J. Edwards - One of the best experts on this subject based on the ideXlab platform.
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Spectral power changes prior to psychogenic Non-Epileptic Seizures: a pilot study.
Journal of neurology neurosurgery and psychiatry, 2016Co-Authors: Anne Marthe Meppelink, Isabel Pareés, Martijn Beudel, Simon Little, Mahinda Yogarajah, Sanjay M. Sisodiya, Mark J. EdwardsAbstract:Psychogenic Non-Epileptic Seizures (PNES) are the most common manifestation of functional (psychogenic) neurological symptoms. Clinically, they consist of intermittent episodes that resemble epileptic Seizures and can involve changes in behaviour, movement, sensation, autonomic function or consciousness. To date, there are no positive EEG features that have been identified that are diagnostic of PNES and therefore, the diagnosis is primarily based on clinical assessment and the absence of epileptic activity during the Seizure. Our goal was to identify a positive marker of PNES, by assessing EEG spectral power changes prior to Non-Epileptic attacks. We hypothesised that decreases in β power (desynchronisation in the 13–30 Hz band) might occur prior to a Non-Epileptic Seizure. β-Desynchronisation is known to occur prior to cued movement (event-related desynchronisation, ERD) or self-paced movement and was recently shown to occur prior to functional myoclonic jerks.1 ### Participants We recruited three patients previously diagnosed with PNES from the Movement Disorder outpatient clinics at the National Hospital for Neurology and Neurosurgery. Three EEG recordings of patients with convulsive epileptic attacks were used as control. The study was approved by the local Ethics Committee and informed consent was obtained. ### Procedure Patients were seated in a comfortable chair while a 32-channel EEG was recorded and a video was made. We asked each participant to sit in a relaxed position with their eyes open and to let attacks happen in their usual way …
Vittoria Cianci - One of the best experts on this subject based on the ideXlab platform.
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Management of psychogenic Non-Epileptic Seizures: a multidisciplinary approach.
European journal of neurology, 2018Co-Authors: Sara Gasparini, Ettore Beghi, Edoardo Ferlazzo, Massimiliano Beghi, Vincenzo Belcastro, Klaus Peter Biermann, Gabriella Bottini, Giuseppe Capovilla, R. A. Cervellione, Vittoria CianciAbstract:The International League against Epilepsy (ILAE) proposed a diagnostic scheme for psychogenic Non-Epileptic Seizure (PNES). The debate on ethical aspects of the diagnostic procedures is ongoing, the treatment is not standardized and management might differ according to age group. The objective was to reach an expert and stakeholder consensus on PNES management. A board comprising adult and child neurologists, neuropsychologists, psychiatrists, pharmacologists, experts in forensic medicine and bioethics as well as patients' representatives was formed. The board chose five main topics regarding PNES: diagnosis; ethical issues; psychiatric comorbidities; psychological treatment; and pharmacological treatment. After a systematic review of the literature, the board met in a consensus conference in Catanzaro (Italy). Further consultations using a model of Delphi panel were held. The global level of evidence for all topics was low. Even though most questions were formulated separately for children/adolescents and adults, no major age-related differences emerged. The board established that the approach to PNES diagnosis should comply with ILAE recommendations. Seizure induction was considered ethical, preferring the least invasive techniques. The board recommended looking carefully for mood disturbances, personality disorders and psychic trauma in persons with PNES and considering cognitive-behavioural therapy as a first-line psychological approach and pharmacological treatment to manage comorbid conditions, namely anxiety and depression. Psychogenic Non-Epileptic Seizure management should be multidisciplinary. High-quality long-term studies are needed to standardize PNES management.
Kogulavadanan Arumaithurai - One of the best experts on this subject based on the ideXlab platform.
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Artificial intelligence as an emerging technology in the current care of neurological disorders
Journal of Neurology, 2019Co-Authors: Urvish K. Patel, Arsalan Anwar, Preeti Malik, Bakhtiar Rasul, Robert Yao, Ashok Seshadri, Mohammed Yousufuddin, Sidra Saleem, Karan Patel, Kogulavadanan ArumaithuraiAbstract:BackgroundArtificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind.DiscussionThe smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological Non-Epileptic Seizure, predict outcome of Seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community.ConclusionAI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner.