A deep learning approach for real-time detection of sleep spindles-Reference-Cited by-同舟云学术

A deep learning approach for real-time detection of sleep spindles

Published:2019-03-19 Issue:3 Volume:16 Page:036004
ISSN:1741-2560
Container-title:Journal of Neural Engineering
language:
Short-container-title:J. Neural Eng.

Author:

Kulkarni Prathamesh M,Xiao Zhengdong,Robinson Eric J,Jami Apoorva Sagarwal,Zhang Jianping,Zhou Haocheng,Henin Simon E,Liu Anli A,Osorio Ricardo S,Wang Jing,Chen Zhe^ORCID

Funder

National Institute of General Medical Sciences

National Institute of Neurological Disorders and Stroke

National Institute on Aging

Division of Information and Intelligent Systems

National Institute of Mental Health

Division of Chemical, Bioengineering, Environmental, and Transport Systems

Publisher

IOP Publishing

Subject

Cellular and Molecular Neuroscience,Biomedical Engineering

Link

http://iopscience.iop.org/article/10.1088/1741-2552/ab0933/pdf

Reference88 articles.

1. Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG

2. Actigraphy: a useful tool to monitor sleep-related hypermotor seizures

3. Manipulating sleep spindles – expanding views on sleep, memory, and disease

4. DiBa: A Data-Driven Bayesian Algorithm for Sleep Spindle Detection

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