Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Signal Preprocessing
2.2.1. Wavelet Packet Transform
2.2.2. Feature Extraction
- (1)
- Time-domain features
- (2)
- Energy-domain features
- (3)
- Frequency-domain features
- (4)
- Nonlinear-dynamics-domain features
2.2.3. Feature Selection
2.3. Cascaded Support Vector Machine Classifier
2.3.1. Data Set for SVM I
2.3.2. Data Set for SVM II
2.3.3. Transform the Input Data
3. Results and Discussion
3.1. The Average Accuracy of Sleep Stage Classification
3.2. The Sleep Stage Classification Performance
3.3. Influence by Number of Input Features on Classification Accuracy
3.4. Effectiveness Analysis of Different Nonlinear Dynamics Features
4. Conclusions
5. Patents
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix A.1. Effective Time-Domain Features of the EEG Signals
Time-Domain Features | Functions |
---|---|
Mean | |
Median | |
Variance | |
Skewness | |
Kurtosis | |
Hjorth mobility | |
Peak-to-peak amplitude | |
Hjorth complexity | |
Average rectified value | |
Root mean square |
Appendix A.2. Nonlinear-Dynamics-Domain Features of the EEG Signals
- (1)
- Spectral entropy (SE)
- (2)
- Sample entropy
- (3)
- Fuzzy entropy
Appendix A.3. The Feature System Considered in this Study
Time-Domain Features | Energy-Domain Features | Frequency-Domain Features | Nonlinear-Dynamics-Domain Features |
---|---|---|---|
7: Mean | 31: Mean frequency | 47: Renyi entropy | |
8: Median | 48: Sample entropy | ||
9: Variance | 49: Fuzzy entropy | ||
10: Skewness | 50: Multi-scale entropy | ||
11: Kurtosis | 51:Lempel–Ziv complexity | ||
12: Hjorth mobility | |||
13:peak-to-peak amplitude | |||
14: Hjorth complexity | |||
15: Average rectified value | |||
16: Root mean square (RMS) |
Appendix A.4. The Permuted Rank of Different Features for SVM Classifiers
Classifier | Ranks of Features’ Number |
---|---|
Support Vector Machine I | 46→49→11→40→10→24→32→50→20→7 12→33→35→47→8→29→13→51→42→15 25→31→43→26→3→48→14→44→9→37 36→41→23 →34→16→27→4→2→38→17 19→28→5→18→1→21→39→45→6→22→30 |
Support Vector Machine II | 20→7→24→2→32→33→17→11→19→39 43→13→49→21→45→50→14→37→40→30 15→31→35→44→4→36→18→25→9→23 28→48→41→34→42→6→38→12→3→26 1→46→5→16→22→27→51→29→47→8→10 |
Appendix A.5. The Frequency Spectra That Were Used for Computing the Frequency-Domain Features
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Sleep Stage | Characteristic Waves |
---|---|
W | Alpha (8–13 Hz) and beta (12–30 Hz) |
N1 | Theta (4–8 Hz) |
N2 | Spindle (12–14 Hz) and K complex (1 Hz) |
N3 | Delta (0.5–2 Hz) |
R | Alpha (8–13 Hz), beta (12–30 Hz), theta (4–8 Hz), and sawtooth wave (2–6 Hz) |
Sleep Stage | Number of Epochs in Stages | Proportion |
---|---|---|
W | 2201 | 26.97% |
N1 | 579 | 7.10% |
N2 | 3221 | 39.47% |
N3 | 900 | 11.03% |
R | 1259 | 15.43% |
Cascaded SVM | SVM | |
---|---|---|
Computing time for each epoch (s) | 1.65 | 1.57 |
Average accuracy | 88.11% ± 0. 67% | 86.45% ± 0.71% |
The average accuracy of N1 | 55.65% ± 3.13% | 41.5% ± 1.72% |
Reference | Classifier | Accuracy | Accuracy of N1 |
---|---|---|---|
[41] | OCRNN | 82.40% | 33.39% |
[18] | LSTM RNN | 86.74% | 61.09% |
[42] | Elman RNN | 87.20% | 36.70% |
[43] | Bagging | 86.53% | 27.48% |
[26] | CNN | 86.79% | 34.92% |
[44] | Multi-class SVM | 83.92% | 17.39% |
This work | Cascaded Support Vector Machine | 88.11% | 55.65% |
Features | Computing Time(s) | Accuracy | Accuracy of N1 | Score |
---|---|---|---|---|
Fuzzy entropy | 2.04 ± 0.012 | 0.8560 ± 0.0133 | 0.4685 ± 0.0638 | 0.6136 |
LZC | 2.03 ± 0.015 | 0.8586 ± 0.0143 | 0.4550 ± 0.0647 | 0.5399 |
Sample entropy | 2.04 ± 0.021 | 0.8589 ± 0.0096 | 0.4750 ± 0.0740 | 0.3808 |
Multi-scale entropy | 2.02 ± 0.014 | 0.8651 ± 0.0152 | 0.4524 ± 0.0391 | 0.5858 |
Performance Parameter | Before Nonlinear Features Selection | After Nonlinear Feature Selection | Total Features |
---|---|---|---|
Computing time for each epoch (s) | 2.08 | 1.65 | 2.65 |
Average accuracy | 74.54% ± 0. 82% | 88.11% ± 0.67% | 74.36% ± 1.93% |
Average accuracy of N1 | 26.58% ± 1.76% | 55.65% ± 3.13% | 26.97% ± 0. 75% |
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Li, D.; Ruan, Y.; Zheng, F.; Su, Y.; Lin, Q. Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals. Sensors 2022, 22, 9914. https://doi.org/10.3390/s22249914
Li D, Ruan Y, Zheng F, Su Y, Lin Q. Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals. Sensors. 2022; 22(24):9914. https://doi.org/10.3390/s22249914
Chicago/Turabian StyleLi, Dezhao, Yangtao Ruan, Fufu Zheng, Yan Su, and Qiang Lin. 2022. "Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals" Sensors 22, no. 24: 9914. https://doi.org/10.3390/s22249914
APA StyleLi, D., Ruan, Y., Zheng, F., Su, Y., & Lin, Q. (2022). Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals. Sensors, 22(24), 9914. https://doi.org/10.3390/s22249914