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

Journal: Journal of neural engineering

Published Date: Feb 21, 2019

Abstract

OBJECTIVE: Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.

Authors

Prathamesh M Kulkarni

Department of Psychiatry, School of Medicine, New York University, New York, NY 10016, United States of America.
Zhengdong Xiao
Eric J Robinson
Apoorva Sagarwal Jami
Jianping Zhang

Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. zhangjianping@fudan.edu.cn.
Haocheng Zhou
Simon E Henin
Anli A Liu
Ricardo S Osorio
Jing Wang

Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
Zhe Chen

Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China.

Keywords

Adolescent Adult Aged Cohort Studies Computer Systems Databases, Factual Deep Learning Female Humans Male Middle Aged Neural Networks, Computer Sleep Stages Young Adult

External Resources

View on PubMed Access via DOI PubMed (30790769)

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

Abstract

Authors

Keywords

External Resources

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