Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials
Abstract
:1. Introduction
2. Materials and Methods
2.1. Subjects
2.2. EEG Recording and Processing
2.3. Experimental Setup
2.4. Event-Related Potentials
- ‘Plus1’: Condition with an image of an animal as the first stimulus in the trial and arbitrary second stimulus. For channels T5, O1, O2, and T6, the interval from 320 ms to 520 ms after the first stimulus was taken. This interval and localization corresponded to the P3Cue wave of the ERPs (Figure 1) [29,30].
- ‘Plus2’: Condition with an image of an animal as the first stimulus in the trial and arbitrary second stimulus. For channels P3, Pz, and P4, the interval was from 900 ms to 1080 ms. The selected interval corresponded to the contingent negative variation (CNV) wave of the ERPs observed just before the expected stimulus presentation (Figure 1) [31].
- ‘NoGo’: Condition with an image of an animal as the first stimulus and an image of a plant as the second one. Selected channels were C3, Cz, C4, P3, Pz, and P4 with an interval from 300 ms to 500 ms after the second stimuli (Figure 2).
- ‘Go’: Condition with an image of an animal as both stimuli. Channels of interest were C3, Cz, C4, P3, Pz, and P4 with an interval from 250 ms to 450 ms after the second stimuli. Selected intervals for NoGo and Go conditions corresponded to the P300 wave for NoGo stimuli (P300 NOGO) and P300 wave for Go stimuli (P3b) observed within 300–500 ms over the frontal and parietal cortex correspondingly (Figure 2 and Figure 3) [32,33,34].
- ‘P-H’: Condition with an image of a plant as the first stimulus and an image of a human as the second stimulus. Selected channels were C3, Cz, and C4 with an interval from 160 ms to 220 ms after the second stimuli. The interval and localization corresponded to P3a wave elicited by infrequent unpredictable stimuli [32].
2.5. Behavioral Data
2.6. Algorithm Description
2.6.1. Feature Engineering
- The random normally distributed feature was added to the training dataset. After fitting, the model shap-values [35] were calculated for each feature. All features that had a shap-value less than the random feature were eliminated;
- Sequential feature selection where the model was trained on the full set of features and after that, the features were eliminated one by one in greedy fashion so that the performance of the model does not decrease;
- Combination of the above approaches: features with non-random shap-values were selected with successive sequential feature selection;
- Truncated SVD, which performs the linear dimensionality reduction. The number of features to leave was calculated based on their cumulative explained variance.
2.6.2. Considered Models
2.6.3. Pipeline Training
3. Results
3.1. Behavioral Parameters
3.2. Model Performance Metrics
3.3. Interpretation
4. Discussion
5. Conclusions
6. Limitations and Future Directions
- Some of the patients were under medication at the time of the EEG recording. Unfortunately, the interruption of medication in patients with severe symptoms could not be implemented due to ethical issues.
- Another limitation was that the sources of the brain signals were located far from the scalp surface and the respective EEG sensors. Together with volume conductance, it led to overlapping of the brain signals in the EEG recordings. Therefore, we were not able to identify the specific localization of the observed effects based only on the topographies of the EEG potentials. In future studies, we intend to apply the method of extracting latent components of the ERPs [54] to isolate signals from individual brain sources. As shown in the previous papers of the authors [20], the analysis of latent components has the advantage of revealing differences in the parameters of brain responses between groups in intergroup comparisons.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Subject Group | Mean Age | Misses in % Mean ± SE | False Clicks in % Mean ± SE | Reaction Time in % Mean ± SE | Reaction Time Variance Mean ± SE |
---|---|---|---|---|---|
Healthy | 31.8 | 1.6 ± 2.8 | 0.7 ± 1.3 | 379 ± 79 | 8.4 ± 2.6 |
Schizophrenia | 30.6 | 9.4 ± 11.4 | 2.0 ± 6.7 | 416 ± 92 | 12.0 ± 4.8 |
Difference F [1, 198], p< | 1, 0.32 | 56.7, 0.001 | ns | 8.9, 0.01 | 48.5, 0.001 |
Model | Sensitivity | Specificity | F1 | AUC |
---|---|---|---|---|
LR | 0.848 ± 0.166 | 0.855 ± 0.159 | 0.806 ± 0.159 | 0.851 ± 0.125 |
LR (behavior) | 0.862 ± 0.132 | 0.863 ± 0.149 | 0.820 ± 0.127 | 0.862 ± 0.097 |
LR (behavior, SFS) | 0.890 ± 0.128 | 0.885 ± 0.099 | 0.846 ± 0.079 | 0.888 ± 0.060 |
kNN | 0.833 ± 0.186 | 0.856 ± 0.093 | 0.783 ± 0.134 | 0.845 ± 0.101 |
kNN (behavior) | 0.867 ± 0.175 | 0.871 ± 0.098 | 0.815 ± 0.137 | 0.869 ± 0.102 |
kNN (behavior, SFS) | 0.895 ± 0.130 | 0.916 ± 0.054 | 0.867 ± 0.098 | 0.906 ± 0.075 |
Stacking (behavior) | 0.862 ± 0.116 | 0.871 ± 0.118 | 0.822 ± 0.124 | 0.866 ± 0.093 |
SVM | 0.893 ± 0.121 | 0.886 ± 0.103 | 0.849 ± 0.125 | 0.890 ± 0.090 |
SVM (SFS) | 0.912 ± 0.096 | 0.908 ± 0.083 | 0.877 ± 0.098 | 0.910 ± 0.074 |
SVM (shap, SFS) | 0.895 ± 0.094 | 0.901 ± 0.103 | 0.863 ± 0.079 | 0.898 ± 0.055 |
SVM (SFSB) | 0.907 ± 0.124 | 0.910 ± 0.091 | 0.872 ± 0.108 | 0.909 ± 0.080 |
SVM (behavior) | 0.907 ± 0.107 | 0.902 ± 0.082 | 0.866 ± 0.091 | 0.904 ± 0.067 |
SVM (behavior, SFS) | 0.895 ± 0.114 | 0.896 ± 0.097 | 0.854 ± 0.107 | 0.895 ± 0.080 |
SVM (behavior, shap, SFS) | 0.876 ± 0.138 | 0.878 ± 0.116 | 0.833 ± 0.095 | 0.877 ± 0.072 |
SVM (behavior, SFSB) | 0.910 ± 0.074 | 0.908 ± 0.083 | 0.877 ± 0.091 | 0.909 ± 0.067 |
Condition | Window Size in ms | Window Shift in % |
---|---|---|
plus 1 | 49 | 50 |
plus 2 | 5 | 50 |
NO-GO | 49 | 100 |
GO | 20 | 100 |
P-H | 5 | 100 |
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Shanarova, N.; Pronina, M.; Lipkovich, M.; Ponomarev, V.; Müller, A.; Kropotov, J. Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials. Diagnostics 2023, 13, 509. https://doi.org/10.3390/diagnostics13030509
Shanarova N, Pronina M, Lipkovich M, Ponomarev V, Müller A, Kropotov J. Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials. Diagnostics. 2023; 13(3):509. https://doi.org/10.3390/diagnostics13030509
Chicago/Turabian StyleShanarova, Nadezhda, Marina Pronina, Mikhail Lipkovich, Valery Ponomarev, Andreas Müller, and Juri Kropotov. 2023. "Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials" Diagnostics 13, no. 3: 509. https://doi.org/10.3390/diagnostics13030509