Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Participants
2.2. EEG Signals and Preprocessing
2.3. The Construction of the Brain’s Static and Dynamic Complex Networks
2.3.1. Constructing the Original EEG Dataset and the Split-Fragment EEG Dataset
2.3.2. Frequency Filtering
2.3.3. The Calculation of the Connectivity and Connectivity Strength between EEG Leads
2.3.4. The Construction of the Brain’s Static Complex Network
2.3.5. The Construction of the Brain’s Dynamic Complex Network
2.3.6. Selection of the Threshold in the Complex Network
2.4. Feature Extraction under the Brain’s Static and Dynamic Complex Network
2.4.1. Feature Extraction under the Brain’s Static Complex Network
2.4.2. Feature Extraction under the Brain’s Dynamic Complex Network
2.5. Feature Selection, Machine Learning Classifier and Evaluating
2.5.1. Feature Selection and Machine Learning Classifier
2.5.2. Evaluating the Classification Performances
3. Results
3.1. Threshold Selection
3.2. Univariate Analysis Results
4. Discussion
4.1. Constructing the Brain’s Dynamic Complex Network and Extracting Its Characteristics Has a Better Performance than the Brain’s Static Complex Network
4.2. The Impact of EEG Frequency Band Splitting on the Discriminant Effect of Machine Learning Models
4.3. Changes in the Topological Characteristics of PE in the Brain Network
4.4. Shortage of This Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Factor | PE | PC |
---|---|---|
Age (years) | 7.75 ± 4.92 | 7.05 ± 3.53 |
Gender n (%) | ||
Female | 6 (37.5) | 7 (35.0) |
Male | 10 (62.5) | 13 (65.0) |
Item | P50_PC | IQR_PC | P50_PE | IQR_PE | W | p |
---|---|---|---|---|---|---|
Small-world index | 1.21 | 0.21 | 1.33 | 0.28 | 106.0 | 0.089 |
Average vertex strength | 0.59 | 0.16 | 1.03 | 0.62 | 53.0 | <0.001 * |
Average path length | 1.63 | 0.04 | 1.64 | 0.05 | 136.0 | 0.453 |
Transitivity | 0.44 | 0.04 | 0.47 | 0.07 | 111.0 | 0.124 |
Diameter | 0.22 | 0.07 | 0.36 | 0.27 | 67.0 | 0.002 * |
Item | P50_PC | IQR_PC | P50_PE | IQR_PE | W | p |
---|---|---|---|---|---|---|
Mean-Small-world index | 1.13 | 0.03 | 1.16 | 0.05 | 84.0 | 0.015 * |
Standard Deviation-Small-world index | 0.18 | 0.02 | 0.18 | 0.01 | 117.0 | 0.178 |
P50-Small-world index | 1.12 | 0.05 | 1.14 | 0.04 | 83.0 | 0.014 * |
IQR-Small-world index | 0.24 | 0.05 | 0.25 | 0.05 | 132.0 | 0.386 |
Mean-Average vertex strength | 2.60 | 0.23 | 2.91 | 0.33 | 65.0 | 0.002 * |
Standard Deviation-Average vertex strength | 0.38 | 0.12 | 0.49 | 0.22 | 53.0 | <0.001 * |
P50-Average vertex strength | 2.59 | 0.26 | 2.82 | 0.17 | 63.0 | 0.002 * |
IQR-Average vertex strength | 0.51 | 0.18 | 0.66 | 0.30 | 43.0 | <0.001 * |
Mean-Average path length | 1.64 | 0.01 | 1.64 | 0.01 | 75.0 | 0.006 * |
Standard Deviation-Average path length | 0.03 | 0.01 | 0.04 | 0.01 | 84.0 | 0.015 * |
P50-Average path length | 1.63 | 0.01 | 1.64 | 0.01 | 44.0 | <0.001 * |
IQR-Average path length | 0.04 | 0.01 | 0.05 | 0.02 | 86.5 | 0.019 * |
Mean-Transitivity | 0.44 | 0.01 | 0.45 | 0.02 | 60.0 | 0.001 * |
Standard Deviation-Transitivity | 0.04 | 0.01 | 0.05 | 0.00 | 55.0 | 0.001 * |
P50-Transitivity | 0.44 | 0.01 | 0.45 | 0.02 | 62.5 | 0.002 * |
IQR-Transitivity | 0.06 | 0.01 | 0.07 | 0.01 | 71.0 | 0.004 * |
Mean-Diameter | 0.96 | 0.09 | 1.09 | 0.14 | 57.0 | 0.001 * |
Standard Deviation-Diameter | 0.19 | 0.05 | 0.26 | 0.10 | 60.0 | 0.001 * |
P50-Diameter | 0.94 | 0.08 | 1.04 | 0.12 | 55.5 | 0.001 * |
IQR-Diameter | 0.23 | 0.08 | 0.32 | 0.11 | 44.0 | <0.001 * |
Item | P50_PC | IQR_PC | P50_PE | IQR_PE | W | p |
---|---|---|---|---|---|---|
Small-world index | 1.22 | 0.23 | 1.29 | 0.29 | 1977.0 | 0.324 |
Average vertex strength | 0.64 | 0.24 | 0.97 | 0.72 | 912.0 | <0.001 * |
Average path length | 1.64 | 0.04 | 1.64 | 0.05 | 2487.5 | 0.188 |
Transitivity | 0.44 | 0.06 | 0.46 | 0.08 | 1864.0 | 0.134 |
Diameter | 0.24 | 0.10 | 0.36 | 0.26 | 1031.5 | <0.001 * |
Item | P50_PC | IQR_PC | P50_PE | IQR_PE | W | p |
---|---|---|---|---|---|---|
Mean-Small-world index | 1.14 | 0.03 | 1.15 | 0.05 | 1658.0 | 0.015 * |
Standard Deviation-Small-world index | 0.17 | 0.02 | 0.18 | 0.03 | 1421.0 | <0.001 * |
P50-Small-world index | 1.13 | 0.05 | 1.14 | 0.05 | 1642.0 | 0.012 * |
IQR-Small-world index | 0.23 | 0.04 | 0.24 | 0.05 | 1638.0 | 0.012 * |
Mean-Average vertex strength | 2.60 | 0.22 | 2.84 | 0.39 | 1008.0 | <0.001 * |
Standard Deviation-Average vertex strength | 0.39 | 0.11 | 0.49 | 0.18 | 897.0 | <0.001 * |
P50-Average vertex strength | 2.55 | 0.23 | 2.80 | 0.29 | 1030.5 | <0.001 * |
IQR-Average vertex strength | 0.52 | 0.11 | 0.65 | 0.30 | 843.5 | <0.001 * |
Mean-Average path length | 1.64 | 0.01 | 1.64 | 0.01 | 1751.0 | 0.045 * |
Standard Deviation-Average path length | 0.04 | 0.01 | 0.04 | 0.01 | 1143.0 | <0.001 * |
P50-Average path length | 1.63 | 0.01 | 1.64 | 0.01 | 1503.5 | 0.001 * |
IQR-Average path length | 0.04 | 0.01 | 0.05 | 0.01 | 1540.0 | 0.003 * |
Mean-Transitivity | 0.44 | 0.01 | 0.45 | 0.01 | 1072.0 | <0.001 * |
Standard Deviation-Transitivity | 0.05 | 0.01 | 0.05 | 0.01 | 1354.0 | <0.001 * |
P50-Transitivity | 0.44 | 0.01 | 0.45 | 0.01 | 1100.0 | <0.001 * |
IQR-Transitivity | 0.06 | 0.01 | 0.06 | 0.01 | 1576.0 | 0.005 * |
Mean-Diameter | 0.96 | 0.09 | 1.06 | 0.17 | 947.0 | <0.001 * |
Standard Deviation-Diameter | 0.20 | 0.05 | 0.24 | 0.09 | 934.0 | <0.001 * |
P50-Diameter | 0.93 | 0.09 | 1.03 | 0.14 | 903.5 | <0.001 * |
IQR-Diameter | 0.25 | 0.07 | 0.31 | 0.11 | 898.0 | <0.001 * |
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Liang, Z.; Chen, S.; Zhang, J. Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy. Sensors 2022, 22, 2553. https://doi.org/10.3390/s22072553
Liang Z, Chen S, Zhang J. Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy. Sensors. 2022; 22(7):2553. https://doi.org/10.3390/s22072553
Chicago/Turabian StyleLiang, Zichao, Siyang Chen, and Jinxin Zhang. 2022. "Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy" Sensors 22, no. 7: 2553. https://doi.org/10.3390/s22072553