Jan Willem Elting

Dr. Jan Willem Elting is a researcher at the University of Groningen's Faculty of Medical Sciences, specializing in movement disorders and neurological conditions. His work focuses on understanding the mechanisms and developing diagnostic tools for conditions such as myoclonus, tremor, dystonia, and cerebral autoregulation disorders, contributing to several UN Sustainable Development Goals related to health and well-being.

His primary research interests include:

• Myoclonus and other movement disorders
• Tremor analysis and classification using advanced technologies
• Cerebral autoregulation and brain blood flow dynamics
• Application of machine learning in neurological diagnosis
• Dystonia and deep brain stimulation efficacy
• Neurophysiological testing methodologies

Dr. Elting's recent publications demonstrate a strong trend toward integrating machine learning techniques with traditional neurological assessment methods. His work bridges computational approaches and clinical neurology, particularly in the classification and diagnosis of movement disorders. He has made significant contributions to understanding benign idiopathic myoclonus as a distinct clinical entity and exploring viral associations with movement disorders in children. His research employs various methodologies including accelerometry, near-infrared spectroscopy, and neurophysiological testing to quantify movement abnormalities.

His scientific achievements have been recognized with:

• Publication Price 2018 for established researchers (awarded March 2019)
• Top 5 best publications in 2018 by the Journal of the American Society of Nephrology

Dr. Elting actively contributes to academic mentoring, currently serving as co-supervisor for PhD student Celina Brunsch. His research has received significant attention, being picked up by 9 news outlets, blogged about by 2 sources, and referenced by multiple Bluesky users and Mendeley readers. He has presented his work at numerous academic conferences, focusing on tremor quantification using accelerometry in Parkinson's disease patients.

His work is conducted within research groups focused on movement disorders and neurological diagnostics, collaborating with experts in neurology, machine learning, and clinical research. His fingerprint analysis shows strong expertise in myoclonus (100%), tremor (80%), cerebral autoregulation (64%), autoregulation (47%), brain blood flow (47%), subarachnoid hemorrhage (46%), dystonia (46%), and near-infrared spectroscopy (44%).