Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals
Abstract
:1. Introduction
2. Methods
2.1. Subjects
2.2. Experimental Protocols and Paradigm
2.3. Data Acquisition
2.4. EEG Pre-Processing
2.5. DSTCLN
3. Results
3.1. Classification Performances for Drowsiness Levels
3.2. Comparison Classification Performances with Conventional Methods
3.3. Neurophysiological Analysis from EEG Signals
4. Discussion
5. Conclusions and Future Works
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
BCI | Brain-Computer Interface |
EEG | Electroencephalogram |
DSTCLN | Deep Spatio-Temporal Convolutional Bidirectional Long Short-Term Memory Network |
KSS | Karolinska Sleepiness Scale |
AI | Artificial Intelligence |
ECG | ElectroCardioGram |
EOG | ElectroOculoGram |
NIRS | Near-Infrared Spectroscopy |
HRV | Heart Rate Variability |
PPG | PhotoPlethysmoGraphy |
AdaBoost | Adaptive Boosting |
SVM | Support Vector Machine |
CNN | Convolutional Neural Network |
AUC | Area Under the Curve |
PPMs | Peripheral Physiological Measures |
VA | Very Alert |
FA | Fairly Alert |
NAS | Neither Alert nor Sleepy |
SNEA | Sleepy but No Effort to keep Awake |
VS | Very Sleepy |
ICA | Independent Component Analysis |
IC | Independent Component |
Bi-LSTM | Bidirectional Long Short-Term Memory |
ELUs | Exponential Linear Units |
LSTM | Long Short-Term Memory |
Std | Standard Deviation |
PSD | Power Spectral Density |
CCA | Canonical Correlation Analysis |
CCNN | Channel-Wise Convolutional Neural Network |
ESTCNN | EEG-based Spatio-Temporal Convolutional Neural Network |
LSTM-D | Deep Long Short-Term Memory |
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Input Size | Block | Layer | Parameter | Output Size |
---|---|---|---|---|
- | - | Input | - | 30 × 100 |
30 × 100 | Convolutional block I | Convolution | Layer number: 2 | 30 × 32 × 92 |
Filter size: 1 × 5 | ||||
Feature map: 32 | ||||
Stride size: 1 × 1 | ||||
BatchNorm | - | |||
30 × 32 × 92 | Convolutional block II | Convolution | Layer number: 2 | 30 × 64 × 84 |
Filter size: 1 × 5 | ||||
Feature map: 64 | ||||
Stride size: 1 × 1 | ||||
BatchNorm | - | |||
30 × 64 × 84 | Convolutional block III | Convolution | Layer number: 2 | 30 × 128 × 76 |
Filter size: 1 × 5 | ||||
Feature map: 128 | ||||
Stride size: 1 × 1 | ||||
BatchNorm | - | |||
30 × 128 × 76 | Convolutional block IV | Convolution | Layer number: 3 | 18 × 128 × 76 |
Filter size: 5 × 1 | ||||
Feature map: 128 | ||||
Stride size: 1 × 1 | ||||
18 × 128 × 76 | Maxpool | Filter size: 2 × 1 | 9 × 128 × 76 | |
Stride size: 2 × 1 | ||||
BatchNorm | - | |||
9 × 128 × 76 | Convolutional block V | Convolution | Layer number: 3 | 3 × 256 × 76 |
Filter size: 1 × 3 | ||||
Feature map: 256 | ||||
Stride size: 1 × 1 | ||||
3 × 256 × 76 | Avgpool | Filter size: 3 × 1 | 1 × 256 × 76 | |
BatchNorm | - | |||
Acvivation (ELU) | - | |||
Dropout (0.5) | - | |||
1 × 256 × 76 | Bi-LSTM block | Bi-LSTM | Hidden units: 256 | 512 × 76 |
512 × 76 | Bi-LSTM | Hidden units: 256 | 512 × 76 | |
512 × 76 | Bi-LSTM | Hidden units: 128 | 256 × 76 | |
256 × 76 | Bi-LSTM | Hidden units: 128 | 256 × 1 | |
Dropout (0.5) | - | |||
256 × 1 | Classification | Fully connected | Hidden units: 128 | 128 × 1 |
128 × 1 | Fully connected | Hidden units: 64 | 64 × 1 | |
64 × 1 | Fully connected | Hidden units: 5 | 5 × 1 | |
Softmax | - |
1-Fold | 2-Fold | 3-Fold | 4-Fold | Classification Accuracy | Std | |
---|---|---|---|---|---|---|
2-class (Alert state / Drowsy state) | 0.86 | 0.87 | 0.88 | 0.87 | 0.87 | ±0.01 |
5-class (VA / FA / NAS / SNEA / VS) | 0.67 | 0.69 | 0.70 | 0.71 | 0.69 | ±0.02 |
Methods | Alert State and Drowsy State (2-Class) | Drowsiness Levels (5-Class) | ||||
---|---|---|---|---|---|---|
Accuracy | Std | Sensitivity | Specificity | Accuracy | Std | |
PSD-SVM [57] | 0.64 | 0.03 | 0.77 | 0.50 | 0.31 | 0.04 |
CCA-SVM [46] | 0.78 | 0.02 | 0.73 | 0.84 | 0.47 | 0.05 |
CCNN [58] | 0.52 | 0.01 | 0.68 | 0.34 | 0.33 | 0.03 |
ESTCNN [23] | 0.78 | 0.01 | 0.73 | 0.85 | 0.56 | 0.01 |
LSTM-D [55] | 0.74 | 0.01 | 0.55 | 0.92 | 0.45 | 0.02 |
ProposedDSTCLN | 0.87 | 0.01 | 0.86 | 0.88 | 0.69 | 0.02 |
State | Delta (1–4 Hz) | Theta (4–8 Hz) | Alpha (8–13 Hz) | Beta (13–30 Hz) | Gamma (30–50 Hz) | |
---|---|---|---|---|---|---|
2-class | Alert | 1.99 Hz | 5.98 Hz | 9.86 Hz | 20.74 Hz | 36.96 Hz |
Drowsy | 1.98 Hz | 6.22 Hz | 9.68 Hz | 19.87 Hz | 36.77 Hz | |
Difference | 0.01 | −0.24 | 0.18 | 0.87 | 0.19 | |
5-class | VA | 2.19 Hz | 5.82 Hz | 9.92 Hz | 20.68 Hz | 36.59 Hz |
FA | 2.22 Hz | 5.92 Hz | 10.01 Hz | 20.82 Hz | 37.12 Hz | |
NAS | 2.15 Hz | 6.05 Hz | 9.84 Hz | 20.80 Hz | 37.13 Hz | |
SNEA | 2.04 Hz | 6.17 Hz | 9.69 Hz | 20.21 Hz | 36.92 Hz | |
VS | 2.24 Hz | 6.29 Hz | 9.70 Hz | 19.38 Hz | 36.00 Hz |
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Share and Cite
Jeong, J.-H.; Yu, B.-W.; Lee, D.-H.; Lee, S.-W. Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals. Brain Sci. 2019, 9, 348. https://doi.org/10.3390/brainsci9120348
Jeong J-H, Yu B-W, Lee D-H, Lee S-W. Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals. Brain Sciences. 2019; 9(12):348. https://doi.org/10.3390/brainsci9120348
Chicago/Turabian StyleJeong, Ji-Hoon, Baek-Woon Yu, Dae-Hyeok Lee, and Seong-Whan Lee. 2019. "Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals" Brain Sciences 9, no. 12: 348. https://doi.org/10.3390/brainsci9120348