Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls
Abstract
:1. Introduction
2. Methods
2.1. Participants
2.2. Experimental Procedure
2.3. EEG Recording and Analysis
2.3.1. Time Domain Analysis
2.3.2. Time-Frequency Analysis
2.4. Statistical Analysis
2.4.1. Analysis of Demographic Data
2.4.2. Behavioral Data
2.4.3. Time-Domain and Time-Frequency Analysis
2.5. Classification Methodology
2.5.1. Feature Generation
Time-Domain Features
Time-Frequency Domain Features
2.5.2. Feature Selection
2.5.3. Classifiers
2.5.4. ROC Analysis
3. Results
3.1. Demography Data
3.2. Behavior Performance
3.3. Time-Domain Analysis
3.4. Time-Frequency Analysis
3.4.1. Theta
3.4.2. Slow Alpha
3.4.3. Fast Alpha
3.4.4. Beta
3.5. Machine Learning
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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True Value | |||
---|---|---|---|
Positive | Negative | ||
Predictive value | Positive | TP | FN |
Negative | FP | TN |
High-Altitude (M ± SD) | Low-Altitude (M ± SD) | t | p | |
---|---|---|---|---|
Age (years) | 22.200 ± 1.699 | 21.933 ± 1.438 | 0.691 | 0.492 |
Gender (male/female) | 19/16 | 17/15 | 0.009 | 0.924 |
PSQI | 3.400 ± 2.291 | 3.281 ± 2.098 | 0.221 | 0.826 |
SPM | 44.686 ± 15.976 | 47.250 ± 9.374 | −0.792 | 0.431 |
NEO-FFI | 177.286 ± 56.098 | 194.469 ± 11.086 | −1.775 | 0.084 |
Stimulus | Period | HA Group (n = 35) | LA Group (n = 32) |
---|---|---|---|
Go | a1 | 491.43 ± 10.26 | 540.56 ± 10.69 |
a2 | 503.03 ± 9.48 | 537.69 ± 9.51 | |
a3 | 491.94 ± 8.23 | 542.25 ± 8.44 | |
No-Go | a1 | 571.60 ± 8.90 | 618.69 ± 8.54 |
a2 | 574.11 ± 9.82 | 584.25 ± 10.77 | |
a3 | 564.34 ± 11.13 | 589.69 ± 9.90 |
Stimulus | Period | HA Group (n = 35) | LA Group (n = 32) |
---|---|---|---|
Go | a1 | 5.02 ± 0.58 | 6.89 ± 0.45 |
a2 | 5.60 ± 0.61 | 6.56 ± 0.55 | |
a3 | 5.84 ± 0.69 | 6.69 ± 0.55 | |
No-Go | a1 | 11.39 ± 0.93 | 13.55 ± 0.87 |
a2 | 12.52 ± 0.82 | 14.80 ± 1.04 | |
a3 | 13.62 ± 1.00 | 14.54 ± 0.96 |
Stimulus | Period | HA Group (n = 35) | LA Group (n = 32) |
---|---|---|---|
Go | a1 | 5.02 ± 0.58 | 6.89 ± 0.45 |
a2 | 5.60 ± 0.61 | 6.56 ± 0.55 | |
a3 | 5.84 ± 0.69 | 6.69 ± 0.55 | |
No-Go | a1 | 11.39 ± 0.93 | 13.55 ± 0.87 |
a2 | 12.52 ± 0.82 | 14.80 ± 1.04 | |
a3 | 13.62 ± 1.00 | 14.54 ± 0.96 |
Stimulus | Period | HA Group (n = 35) | LA Group (n = 32) | |
---|---|---|---|---|
theta | Go | a1 | 0.84 ± 0.15 | 0.84 ± 0.16 |
a2 | 1.07 ± 0.16 | 0.74 ± 0.16 | ||
a3 | 1.44 ± 0.24 | 0.88 ± 0.25 | ||
No-Go | a1 | −0.01 ± 0.07 | −0.19 ± 0.08 | |
a2 | −0.03 ± 0.09 | −0.27 ± 0.09 | ||
a3 | 0.13 ± 0.09 | −0.43 ± 0.09 | ||
slow alpha | Go | a1 | −0.10 ± 0.05 | −0.14 ± 0.05 |
a2 | −0.13 ± 0.05 | −0.17 ± 0.05 | ||
a3 | −0.17 ± 0.06 | −0.20 ± 0.05 | ||
No-Go | a1 | −0.01 ± 0.08 | −0.18 ± 0.07 | |
a2 | −0.02 ± 0.11 | −0.27 ± 0.07 | ||
a3 | −0.02 ± 0.10 | −0.42 ± 0.04 | ||
fast alpha | Go | a1 | −0.09 ± 0.05 | −0.17 ± 0.06 |
a2 | −0.12 ± 0.05 | −0.18 ± 0.06 | ||
a3 | −0.15 ± 0.06 | −0.18 ± 0.06 | ||
No-Go | a1 | −0.14 ± 0.07 | −0.28 ± 0.07 | |
a2 | −0.03 ± 0.09 | −0.31 ± 0.09 | ||
a3 | −0.11 ± 0.08 | −0.37 ± 0.08 | ||
beta | Go | a1 | −0.04 ± 0.02 | −0.11 ± 0.02 |
a2 | −0.03 ± 0.02 | −0.10 ± 0.02 | ||
a3 | −0.05 ± 0.02 | −0.08 ± 0.02 | ||
No-Go | a1 | −0.07 ± 0.03 | −0.17 ± 0.03 | |
a2 | −0.05 ± 0.04 | −0.19 ± 0.04 | ||
a3 | −0.09 ± 0.03 | −0.16 ± 0.03 |
Classifiers | Periods | Accuracy (95% Confidence Interval) | F1 Score | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|
SVM | a1 | 86.57% (78.41%, 94.73%) | 0.89 | 85.71% | 87.50% | 0.90 |
a2 | 88.06% (80.3%, 95.82%) | 0.88 | 91.43% | 84.38% | 0.96 | |
a3 | 92.54% (86.25%, 98.83%) | 0.93 | 91.43% | 93.75% | 0.97 | |
LR | a1 | 95.52% (90.57%, 100.00%) | 0.96 | 91.43% | 100.00% | 0.94 |
a2 | 89.55% (82.22%, 96.88%) | 0.90 | 91.43% | 87.50% | 0.93 | |
a3 | 86.57% (78.41%, 94.73%) | 0.87 | 88.57% | 84.38% | 0.92 | |
DT | a1 | 80.60% (71.13%, 90.07%) | 0.80 | 85.71% | 75.00% | 0.69 |
a2 | 70.15% (59.19%, 81.11%) | 0.68 | 80.00% | 59.38% | 0.70 | |
a3 | 70.15% (59.19%, 81.11%) | 0.70 | 65.71% | 75.00% | 0.81 |
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Liu, H.; Shi, R.; Liao, R.; Liu, Y.; Che, J.; Bai, Z.; Cheng, N.; Ma, H. Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls. Brain Sci. 2022, 12, 1677. https://doi.org/10.3390/brainsci12121677
Liu H, Shi R, Liao R, Liu Y, Che J, Bai Z, Cheng N, Ma H. Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls. Brain Sciences. 2022; 12(12):1677. https://doi.org/10.3390/brainsci12121677
Chicago/Turabian StyleLiu, Haining, Ruijuan Shi, Runchao Liao, Yanli Liu, Jiajun Che, Ziyu Bai, Nan Cheng, and Hailin Ma. 2022. "Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls" Brain Sciences 12, no. 12: 1677. https://doi.org/10.3390/brainsci12121677