An EEG spectrogram-based automatic sleep stage scoring method via data augmentation, ensemble convolution neural network, and expert knowledge

Abstract Study Objectives Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming. Methods A sleep staging classifier trained using deep learning methods scored PSG data from the Sleep Heart Health Study (SHHS). The training set was composed of 42 560 hours of PSG data from 5213 patients. To capture higher-order data, spectrograms were generated from electroencephalography, electrooculography, and electromyography data and then passed to the neural network. A holdout set of 580 PSGs not included in the training set was used to assess model accuracy and discrimination via weighted F1-score, per-stage accuracy, and Cohen’s kappa (K). Results The optimal neural network model was composed of spectrograms in the input layer feeding into convolutional neural network layers and a long short-term memory layer to achieve a weighted F1-score of 0.87 and K = 0.82. Conclusions The deep learning sleep stage classifier demonstrates excellent accuracy and agreement with expert sleep stage scoring, outperforming human agreement on sleep staging. It achieves comparable or better F1-scores, accuracy, and Cohen’s kappa compared to literature for automated sleep stage scoring of PSG epochs. Accurate automated scoring of other PSG events may eventually allow for fully automated PSG scoring.

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A Convolution Neural Network for Parts Recognition Using Data Augmentation

2018 13th World Congress on Intelligent Control and Automation (WCICA) ◽

10.1109/wcica.2018.8630451 ◽

2018 ◽

Cited By ~ 2

Author(s):

Qiucheng Dong ◽

Aiguo Wu ◽

Na Dong ◽

Wei Feng ◽

Shaohua Wu

Keyword(s):

Neural Network ◽

Data Augmentation ◽

Convolution Neural Network ◽

Using Data

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Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

SLEEP ◽

10.1093/sleep/zsaa161 ◽

2020 ◽

Author(s):

Alexander Neergaard Olesen ◽

Poul Jørgen Jennum ◽

Emmanuel Mignot ◽

Helge Bjarup Dissing Sorensen

Keyword(s):

Neural Network ◽

Large Scale ◽

Deep Neural Network ◽

Sleep Stage ◽

Scoring Systems ◽

Training Data ◽

Future Research ◽

Small Scale ◽

Sleep Stage Scoring ◽

Single Cohort

Abstract Study Objectives Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts. Methods A deep neural network model was developed using 15,684 polysomnography studies from five different cohorts. We applied four different scenarios: (1) impact of varying timescales in the model; (2) performance of a single cohort on other cohorts of smaller, greater, or equal size relative to the performance of other cohorts on a single cohort; (3) varying the fraction of mixed-cohort training data compared with using single-origin data; and (4) comparing models trained on combinations of data from 2, 3, and 4 cohorts. Results Overall classification accuracy improved with increasing fractions of training data (0.25%: 0.782 ± 0.097, 95% CI [0.777–0.787]; 100%: 0.869 ± 0.064, 95% CI [0.864–0.872]), and with increasing number of data sources (2: 0.788 ± 0.102, 95% CI [0.787–0.790]; 3: 0.808 ± 0.092, 95% CI [0.807–0.810]; 4: 0.821 ± 0.085, 95% CI [0.819–0.823]). Different cohorts show varying levels of generalization to other cohorts. Conclusions Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research.

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