A DEEP LEARNING ARCHITECTURE TO DETECT EVENTS IN EEG SIGNALS DURING SLEEP

被引:0
作者
Chambon, Stanislas [1 ,2 ,3 ]
Thorey, Valentin [2 ]
Arnal, Pierrick J. [2 ]
Mignot, Emmanuel [1 ]
Gramfort, Alexandre [3 ,4 ,5 ]
机构
[1] Stanford Univ, Ctr Sleep Sci & Med, Stanford, CA 94305 USA
[2] Dreem, Res & Algorithms Team, Paris, France
[3] Univ Paris Saclay, LTCI Telecom ParisTech, Paris, France
[4] Univ Paris Saclay, INRIA, Paris, France
[5] Univ Paris Saclay, CEA Neurospin, Paris, France
来源
2018 IEEE 28TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2018年
关键词
Deep learning; EEG; event detection; sleep; EEG-patterns; time series; SPINDLES;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (>= 10 s) such as sleep stages, and micro-events (<= 2 s) such as spindles and K-complexes. Annotations of such events require a trained sleep expert, a time consuming and tedious process with a large inter-scorer variability. Automatic algorithms have been developed to detect various types of events but these are event-specific. We propose a deep learning method that jointly predicts locations, durations and types of events in EEG time series. It relies on a convolutional neural network that builds a feature representation from raw EEG signals. Numerical experiments demonstrate efficiency of this new approach on various event detection tasks compared to current state-of-the-art, event specific, algorithms.
引用
收藏
页数:6
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