Clinical and Experimental Human Sleep-Wake Pharmacogenetics

Sleep-Wake Neurobiology and Pharmacology - Handbook of Experimental Pharmacology ◽

10.1007/164_2018_175 ◽

2018 ◽

pp. 207-241 ◽

Cited By ~ 2

Author(s):

Hans-Peter Landolt ◽

Sebastian C. Holst ◽

Amandine Valomon

Keyword(s):

Human Sleep

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Methods for assessing human sleep

Archives of Internal Medicine ◽

10.1001/archinte.127.3.484a ◽

1971 ◽

Vol 127 (3) ◽

pp. 484a-492

Author(s):

M. W. Johns

Keyword(s):

Human Sleep

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A Study of Human Sleep Staging Behavior Based on Polysomnography Using Machine Learning Techniques

Data Analytics in Bioinformatics ◽

10.1002/9781119785620.ch14 ◽

2021 ◽

pp. 339-358

Author(s):

Santosh Kumar Satapathy ◽

D. Loganathan

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Sleep Staging ◽

Human Sleep ◽

Learning Techniques ◽

Behavior Based

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Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

SN Computer Science ◽

10.1007/s42979-021-00697-3 ◽

2021 ◽

Vol 2 (4) ◽

Author(s):

Sarun Paisarnsrisomsuk ◽

Carolina Ruiz ◽

Sergio A. Alvarez

Keyword(s):

Input Data ◽

Single Channel ◽

Sleep Stage ◽

Quantitative Measure ◽

Classification Performance ◽

Sleep Stages ◽

Layer Depth ◽

Human Sleep ◽

Stage Classification ◽

Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

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