A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal
Abstract
:1. Introduction
- In signal preprocessing, the signal frequency domain information, the space domain information between the electrodes of the acquisition equipment, and timing characteristics are fully utilized. The extraction of features with this strategy can be implemented automatically without manual acquisition. The model explores the GRU network with CNN layers whereby the CNN layers extract features and the GRU block provides sequence learning.
- A model was proposed with relatively few layers (6 layers), and consequently, a relatively low level of complexity.
2. Materials and Methods
2.1. Subjects
2.2. Proposed Classification Method
2.2.1. EEG Signal Preprocessing
- The EEG signal was processed by Fast ICA to obtain several independent components whereby the independent components include the independent component containing the EEG artifact and the independent component without the EEG artifact;
- Wavelet transforms and the differential evolution algorithm were used to process the independent component containing the artifact to obtain the artifact component;
- Based on wavelet reconstruction and inverse transformation, an EEG signal was obtained to remove the artifacts according to the artifact component.
2.2.2. Extraction Using CNN
2.2.3. Learning Model with GRU
2.2.4. Validation
3. Results
3.1. The Effect of Data Augmentation
3.2. The Influence of The number of CNN Layers
3.3. Comparison with Other Methods
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Properties | MODMA Dataset | Private Dataset |
---|---|---|
No. of participants | 53 | 32 |
No. of depression cases | 24 | 16 |
Depression diagnostics | Diagnosis | Diagnosis, BDI |
Male/female ratio | 33/20 | 16/16 |
No. of channels | 128 | 16 |
Sampling rate, Hz | 250 | 100 |
Method | Data Set | AC (Mean ± Std) | SE (Mean ± Std) | SP (Mean ± Std) | F1 (Mean ± Std) |
---|---|---|---|---|---|
1s slicing | MODMA | 89.63 ± 1.3 | 90.24 ±1.9 | 89.63 ± 1.3 | 90.19 ± 1.3 |
private dataset | 88.56 ± 1.3 | 88.56 ± 1.5 | 88.54 ± 1.8 | 88.68 ± 1.5 | |
2s slicing | MODMA | 90.62 ± 2.1 | 87.81 ± 3.2 | 87.48 ± 2.1 | 88.79 ± 2.1 |
private dataset | 89.84 ± 2.1 | 87.82 ± 3.4 | 87.36 ± 1.7 | 88.79 ± 2.1 | |
3s slicing | MODMA | 87.01 ± 1.5 | 87.01 ± 1.5 | 87.01 ± 1.5 | 88.01 ± 1.5 |
private dataset | 87.72 ± 1.6 | 87.32 ± 1.6 | 86.72 ± 1.6 | 88.72 ± 1.6 |
Layers | Time (s) | Parameters | AC | SE | SP | F1 |
---|---|---|---|---|---|---|
1 | 172 | 896 | 87.98 | 88.38 | 88.98 | 87.79 |
2 | 224 | 10,272 | 86.68 | 85.46 | 85.48 | 85.63 |
3 | 340 | 28,768 | 75.68 | 78.18 | 78.16 | 78.58 |
Methods | Features | Accuracy (%) |
---|---|---|
LR + ReliefF [51] | linear | 66.40 |
LR + ReliefF [51] | nonlinear | 67.17 |
LR + ReliefF [51] | PLI | 82.31 |
LR + ReliefF [51] | Linear + PLI | 80.99 |
LR + ReliefF [51] | Nonlinear + PLI | 81.79 |
TCN [52] | ITD + statistical features | 85.23 |
L-TCN [52] | ITD + statistical features | 86.87 |
BrainMap + CNN + GRU | BrainMap features | 89.63 |
Method | Dataset | AC | SE | SP | F1 |
---|---|---|---|---|---|
SVM | MODMA | 78.12 | 78.12 | 78.12 | 77.31 |
Private dataset | 75.18 | 74.92 | 75.12 | 74.31 | |
GRU | MODMA | 83.12 | 86.67 | 76.57 | 87.55 |
Private dataset | 81.36 | 82.49 | 78.91 | 82.55 | |
CNN | MODMA | 84.32 | 85.76 | 79.86 | 87.96 |
Private dataset | 82.34 | 84.35 | 79.91 | 83.31 | |
TCN | MODMA | 85.23 | 89.67 | 76.57 | 87.55 |
Private dataset | 82.38 | 82.47 | 82.47 | 82.55 | |
L-TCN | MODMA | 86.87 | 90.15 | 83.83 | 90.51 |
Private dataset | 85.64 | 85.87 | 81.23 | 86.55 | |
BrainMap + CNN | MODMA | 87.34 | 89.48 | 88.56 | 87.37 |
Private dataset | 83.65 | 82.59 | 82.31 | 82.55 | |
BrainMap + CNN + GRU | MODMA | 89.63 | 90.24 | 89.63 | 90.19 |
Private dataset | 88.56 | 88.56 | 88.54 | 88.68 |
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Liu, W.; Jia, K.; Wang, Z.; Ma, Z. A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal. Brain Sci. 2022, 12, 630. https://doi.org/10.3390/brainsci12050630
Liu W, Jia K, Wang Z, Ma Z. A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal. Brain Sciences. 2022; 12(5):630. https://doi.org/10.3390/brainsci12050630
Chicago/Turabian StyleLiu, Wei, Kebin Jia, Zhuozheng Wang, and Zhuo Ma. 2022. "A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal" Brain Sciences 12, no. 5: 630. https://doi.org/10.3390/brainsci12050630
APA StyleLiu, W., Jia, K., Wang, Z., & Ma, Z. (2022). A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal. Brain Sciences, 12(5), 630. https://doi.org/10.3390/brainsci12050630