Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly
Abstract
1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
ML | Parameter | Values |
---|---|---|
Logit | C Regularization | 1, 10, 100 L1,l2, elasticnet |
RF | Minimum sample split Minimum samples per leaf Maximum features per tree | 2, 5, 10 2, 5, 10, 15 Square root, log2 |
SVM | C Kernal method | 1, 10, 100 Linear, polynomial, radial, sigmoid |
Distribution | Mood | Energy |
---|---|---|
Statistic (p Value) | Statistic (p Value) | |
Inverse Chi2 (36) | 92.69 (0.000) | 57.19 (0.014) |
Inverse Normal | −2.14 (0.016) | −2.63 (0.004) |
Inverse Logit t (89) | −4.319 (0.000) | −2.70 (0.004) |
Mod. Inv. Chi2 | 6.682 (0.000) | 2.49 (0.006) |
Mood | Immersion | PS | PI | |
---|---|---|---|---|
Male | 12.36 | 1.76 | 0.005 | |
Female | 26.50 | 1.76 | 0.005 |
Variable | Principal Components Analysis (PCA) | ||
---|---|---|---|
PC 1 | PC 2 | PC 3 | |
Immersion | 0.402 | 0.011 | −0.125 |
Immersion (−1) | 0.398 | −0.047 | 0.058 |
Immersion (−2) | 0.385 | −0.003 | −0.207 |
Peak | 0.071 | 0.564 | −0.346 |
Peak (−1) | 0.059 | 0.559 | 0.801 |
Peak (−2) | 0.064 | 0.591 | −0.389 |
Safety | 0.399 | −0.063 | 0.090 |
Safety (−1) | 0.423 | −0.069 | 0.112 |
Safety (−2) | 0.425 | −0.097 | 0.061 |
% Variance Explained | 46.45% | 15.70% | 9.05% |
Variable | OLS | VIF | Logit | Odd Ratio |
---|---|---|---|---|
PC 1 | 0.0599 ** (0.018) | 1.1 | 0.319 (0.079) | 1.375 |
PC 2 | 0.142 (0.037) | 1.03 | 0.674 (0.171) | 1.963 |
PC 3 | −0.095 (0.043) | 1.03 | −0.443 (0.199) | 0.642 |
Sick | −0.917 (0.172) | 1.01 | −5.04 (1.58) | 0.006 |
Male Intercept | 0.297 (0.093) 3.84 *** (0.043) | 1.12 | 2.06 (0.430) 0.285 (0.162) | 7.86 |
F-value | 11.59 | |||
p-value | (0.000) | |||
R-squared | 0.182 |
Energy | Immersion | PS | PI | |
---|---|---|---|---|
Male | 40.17 | 1.76 | 0.005 | |
Female | 35.80 | 1.76 | 0.005 |
Variable | OLS | VIF | Logit | Odd Ratio |
---|---|---|---|---|
PC 1 | 0.143 * (0.028) | 1.1 | 0.389 (0.081) | 1.48 |
PC 2 | 0.266 (0.056) | 1.03 | 0.816 (0.161) | 2.26 |
PC 3 | −0.116 (0.065) | 1.03 | −0.117 (0.167) | 0.889 |
Sick | −0.918 (0.258) | 1.01 | −2.09 (1.15) | 0.123 |
Male Intercept | 0.134 (0.140) 3.078 *** (0.065) | 1.12 | −0.775 (0.457) −0.736 (0.173) | 0.461 |
F-value | 2.420 | |||
p-value | (0.081) | |||
R-squared | 0.316 |
Immersion | AUC | Accuracy | Precision | Recall | |
---|---|---|---|---|---|
Test | Logit | 0.93 | 0.93 | 0.91 | 0.95 |
RF | 1.00 | 1.00 | 1.00 | 1.00 | |
SVM | 0.93 | 0.91 | 1.00 | 0.83 | |
Observed | Logit | 0.71 | 0.91 | 0.99 | 0.91 |
RF | 1.00 | 1.00 | 1.00 | 1.00 | |
SVM | 0.82 | 0.96 | 1.00 | 0.96 | |
CV | Logit | 0.94 | 0.88 | 0.91 | 0.84 |
0.032 | 0.024 | 0.04 | 0.014 | ||
RF | 1.00 | 0.97 | 0.99 | 0.96 | |
0.004 | 0.018 | 0.024 | 0.031 | ||
SVM | 1.00 | 0.96 | 0.99 | 0.92 | |
0.006 | 0.023 | 0.012 | 0.051 | ||
PS | |||||
Test | Logit | 0.76 | 0.76 | 0.8 | 0.68 |
RF | 0.9 | 0.89 | 0.94 | 0.83 | |
SVM | 0.94 | 0.94 | 0.97 | 0.9 | |
Observed | Logit | 0.59 | 0.75 | 0.98 | 0.75 |
RF | 0.78 | 0.94 | 1.00 | 0.94 | |
SVM | 0.81 | 0.95 | 1.00 | 0.95 | |
CV | Logit | 0.85 | 0.79 | 0.81 | 0.75 |
0.064 | 0.065 | 0.072 | 0.099 | ||
RF | 0.96 | 0.91 | 0.96 | 0.86 | |
0.033 | 0.037 | 0.042 | 0.095 | ||
SVM | 0.95 | 0.92 | 0.99 | 0.85 | |
0.018 | 0.03 | 0.013 | 0.065 |
Immersion | AUC | Accuracy | Precision | Recall | |
---|---|---|---|---|---|
Test | Logit | 0.73 | 0.74 | 0.67 | 0.74 |
RF | 0.80 | 0.80 | 0.73 | 0.81 | |
SVM | 0.78 | 0.78 | 0.78 | 0.67 | |
Observed | Logit | 0.78 | 0.82 | 0.9 | 0.85 |
RF | 0.93 | 0.95 | 0.97 | 0.96 | |
SVM | 0.86 | 0.90 | 0.97 | 0.88 | |
CV | Logit | 0.81 | 0.78 | 0.83 | 0.7 |
0.081 | 0.084 | 0.095 | 0.089 | ||
RF | 0.88 | 0.8 | 0.84 | 0.74 | |
0.088 | 0.083 | 0.118 | 0.101 | ||
SVM | 0.89 | 0.8 | 0.88 | 0.71 | |
0.076 | 0.074 | 0.1 | 0.083 | ||
PS | |||||
Test | Logit | 0.7 | 0.71 | 0.65 | 0.63 |
RF | 0.83 | 0.83 | 0.75 | 0.89 | |
SVM | 0.83 | 0.82 | 0.86 | 0.67 | |
Observed | Logit | 0.63 | 0.65 | 0.85 | 0.63 |
RF | 0.93 | 0.94 | 0.96 | 0.96 | |
SVM | 0.84 | 0.88 | 0.97 | 0.86 | |
CV | Logit | 0.75 | 0.65 | 0.68 | 0.58 |
0.044 | 0.049 | 0.052 | 0.073 | ||
RF | 0.87 | 0.79 | 0.81 | 0.81 | |
0.05 | 0.033 | 0.095 | 0.106 | ||
SVM | 0.87 | 0.81 | 0.87 | 0.75 | |
0.05 | 0.028 | 0.06 | 0.079 |
Immersion | AUC | Accuracy | Precision | Recall | |
---|---|---|---|---|---|
Test | Logit | 0.94 | 0.94 | 0.97 | 0.89 |
RF | 1.00 | 1.001 | 1.00 | 1.00 | |
SVM | 0.99 | 0.99 | 1.00 | 0.97 | |
Observed | Logit | 0.74 | 0.92 | 0.99 | 0.93 |
RF | 0.96 | 0.99 | 1.00 | 0.99 | |
SVM | 0.96 | 0.99 | 1.00 | 0.99 | |
CV | Logit | 0.96 | 0.93 | 0.96 | 0.89 |
0.037 | 0.034 | 0.028 | 0.081 | ||
RF | 0.99 | 0.98 | 1.00 | 0.95 | |
0.002 | 0.029 | 0 | 0.058 | ||
SVM | 0.99 | 0.98 | 1.00 | 0.95 | |
0.002 | 0.021 | 0 | 0.042 | ||
PS | |||||
Test | Logit | 0.67 | 0.63 | 0.55 | 0.83 |
RF | 0.91 | 0.91 | 0.89 | 0.91 | |
SVM | 0.96 | 0.95 | 1.00 | 0.89 | |
Observed | Logit | 0.57 | 0.76 | 0.97 | 0.76 |
RF | 0.79 | 0.95 | 0.98 | 0.96 | |
SVM | 0.88 | 0.98 | 1.00 | 0.98 | |
CV | Logit | 0.78 | 0.69 | 0.68 | 0.68 |
0.055 | 0.044 | 0.03 | 0.102 | ||
RF | 0.99 | 0.95 | 0.97 | 0.93 | |
0.016 | 0.031 | 0.056 | 0.023 | ||
SVM | 0.97 | 0.96 | 0.99 | 0.93 | |
0.02 | 0.008 | 0.012 | 0.016 |
Immersion | AUC | Accuracy | Precision | Recall | |
---|---|---|---|---|---|
Test | Logit | 0.84 | 0.84 | 0.87 | 0.82 |
RF | 0.85 | 0.85 | 0.88 | 0.85 | |
SVM | 0.89 | 0.89 | 0.93 | 0.85 | |
Observed | Logit | 0.75 | 0.79 | 0.90 | 0.80 |
RF | 0.93 | 0.95 | 0.97 | 0.96 | |
SVM | 0.94 | 0.96 | 0.98 | 0.96 | |
CV | Logit | 0.83 | 0.78 | 0.82 | 0.71 |
0.07 | 0.07 | 0.08 | 0.08 | ||
RF | 0.89 | 0.81 | 0.87 | 0.74 | |
0.09 | 0.09 | 0.13 | 0.09 | ||
SVM | 0.89 | 0.85 | 0.91 | 0.78 | |
0.09 | 0.081 | 0.12 | 0.073 | ||
Safety | |||||
Test | Logit | 0.78 | 0.71 | 0.94 | 0.48 |
RF | 0.74 | 0.74 | 0.76 | 0.76 | |
SVM | 0.80 | 0.74 | 0.95 | 0.55 | |
Observed | Logit | 0.62 | 0.57 | 0.86 | 0.48 |
RF | 0.83 | 0.87 | 0.95 | 0.86 | |
SVM | 0.70 | 0.68 | 0.94 | 0.59 | |
CV | Logit | 0.71 | 0.65 | 0.72 | 0.49 |
0.07 | 0.06 | 0.10 | 0.12 | ||
RF | 0.75 | 0.71 | 0.71 | 0.71 | |
0.07 | 0.07 | 0.08 | 0.06 | ||
SVM | 0.71 | 0.68 | 0.78 | 0.50 | |
0.06 | 0.07 | 0.08 | 0.12 |
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Variable | OLS | VIF | Logit | Odds Ratio |
---|---|---|---|---|
Immersion | 0.403 * (0.126) | 1.60 | 1.19 (0.484) | 3.296 |
PS | −0.091 (0.081) | 1.58 | 0.127 (0.301) | 1.136 |
PI | 19.63 * (6.52) | 1.03 | 0.428 * (0.192) | 1.153 |
Sick | −0.922 *** (0.152) | 1.02 | −3.889 (1.14) | 0.020 |
Male Intercept | 0.285 (0.087) 3.068 * (0.900) | 1.09 | 1.687 (0.370) −0.535 (3.139) | 5.403 |
F-value | 12.54 | Likelihood ratio χ2 | 55.42 | |
p-value | 0.000 | p-value | 0.000 | |
R-squared | 0.174 | Pseudo R-squared | 0.134 |
Variable | OLS | VIF | Logit | Odds Ratio |
---|---|---|---|---|
Immersion | 0.499 * (0.192) | 1.60 | 1.134 * (0.488) | 3.11 * |
PS | 0.122 (0.123) | 1.58 | 0.507 (0.302) | 1.66 |
PI | 33.95 ** (9.95) | 1.03 | 0.504 ** (0.199) | 1.66 |
Sick | −0.594 ** (0.232) | 1.02 | −1.733 (1.06) | 0.177 |
Male | 0.159 (0.132) | 1.09 | −0.504 (0.394) | 0.604 |
Intercept | 0.764 * (0.614) | −5.888 ** (1.593) | ||
F-value | 7.04 | Likelihood ratio χ2 | 37.88 | |
p-value | (0.000) | p-value | 0.000 | |
R-squared | 0.106 | Pseudo R-squared | 0.107 |
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Merritt, S.H.; Krouse, M.; Alogaily, R.S.; Zak, P.J. Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly. Brain Sci. 2022, 12, 1240. https://doi.org/10.3390/brainsci12091240
Merritt SH, Krouse M, Alogaily RS, Zak PJ. Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly. Brain Sciences. 2022; 12(9):1240. https://doi.org/10.3390/brainsci12091240
Chicago/Turabian StyleMerritt, Sean H., Michael Krouse, Rana S. Alogaily, and Paul J. Zak. 2022. "Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly" Brain Sciences 12, no. 9: 1240. https://doi.org/10.3390/brainsci12091240
APA StyleMerritt, S. H., Krouse, M., Alogaily, R. S., & Zak, P. J. (2022). Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly. Brain Sciences, 12(9), 1240. https://doi.org/10.3390/brainsci12091240