Literature DB >> 35907045

Machine learning and clinical neurophysiology.

Julian Ray1, Lokesh Wijesekera2, Silvia Cirstea2.   

Abstract

Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.
© 2022. Crown.

Entities:  

Keywords:  Artificial neural networks; Clinical neurophysiology; Electroencephalogram; Electromyography; Evoked potential; Machine learning; Nerve conduction

Year:  2022        PMID: 35907045     DOI: 10.1007/s00415-022-11283-9

Source DB:  PubMed          Journal:  J Neurol        ISSN: 0340-5354            Impact factor:   6.682


  44 in total

1.  Deep learning-based electroencephalography analysis: a systematic review.

Authors:  Yannick Roy; Hubert Banville; Isabela Albuquerque; Alexandre Gramfort; Tiago H Falk; Jocelyn Faubert
Journal:  J Neural Eng       Date:  2019-08-14       Impact factor: 5.379

Review 2.  eDoctor: machine learning and the future of medicine.

Authors:  G S Handelman; H K Kok; R V Chandra; A H Razavi; M J Lee; H Asadi
Journal:  J Intern Med       Date:  2018-09-03       Impact factor: 8.989

Review 3.  Detecting Neonatal Seizures With Computer Algorithms.

Authors:  Andriy Temko; Gordon Lightbody
Journal:  J Clin Neurophysiol       Date:  2016-10       Impact factor: 2.177

4.  Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data.

Authors:  Benjamin H Brinkmann; Mark R Bower; Keith A Stengel; Gregory A Worrell; Matt Stead
Journal:  J Neurosci Methods       Date:  2009-04-01       Impact factor: 2.390

5.  Similar image search for histopathology: SMILY.

Authors:  Narayan Hegde; Jason D Hipp; Yun Liu; Michael Emmert-Buck; Emily Reif; Daniel Smilkov; Michael Terry; Carrie J Cai; Mahul B Amin; Craig H Mermel; Phil Q Nelson; Lily H Peng; Greg S Corrado; Martin C Stumpe
Journal:  NPJ Digit Med       Date:  2019-06-21

6.  The implication of the air quality pattern in South Korea after the COVID-19 outbreak.

Authors:  Ja-Ho Koo; Jhoon Kim; Yun Gon Lee; Sang Seo Park; Seoyoung Lee; Heesung Chong; Yeseul Cho; Jaemin Kim; Kyungbae Choi; Taegyung Lee
Journal:  Sci Rep       Date:  2020-12-31       Impact factor: 4.379

7.  Charles Bonnet syndrome: evidence for a generative model in the cortex?

Authors:  David P Reichert; Peggy Seriès; Amos J Storkey
Journal:  PLoS Comput Biol       Date:  2013-07-18       Impact factor: 4.475

Review 8.  Review on solving the inverse problem in EEG source analysis.

Authors:  Roberta Grech; Tracey Cassar; Joseph Muscat; Kenneth P Camilleri; Simon G Fabri; Michalis Zervakis; Petros Xanthopoulos; Vangelis Sakkalis; Bart Vanrumste
Journal:  J Neuroeng Rehabil       Date:  2008-11-07       Impact factor: 4.262

9.  Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis.

Authors:  Silvia Tommasin; Sirio Cocozza; Alessandro Taloni; Costanza Giannì; Nikolaos Petsas; Giuseppe Pontillo; Maria Petracca; Serena Ruggieri; Laura De Giglio; Carlo Pozzilli; Arturo Brunetti; Patrizia Pantano
Journal:  J Neurol       Date:  2021-05-10       Impact factor: 4.849

10.  Seizure detection using wearable sensors and machine learning: Setting a benchmark.

Authors:  Jianbin Tang; Rima El Atrache; Shuang Yu; Umar Asif; Michele Jackson; Subhrajit Roy; Mahtab Mirmomeni; Sarah Cantley; Theodore Sheehan; Sarah Schubach; Claire Ufongene; Solveig Vieluf; Christian Meisel; Stefan Harrer; Tobias Loddenkemper
Journal:  Epilepsia       Date:  2021-07-15       Impact factor: 5.864

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