A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data
Abstract
:1. Introduction and Related Work
2. Materials & Methods
2.1. Visual Sequential Search Test
2.2. VSST Experimental Dataset Description
- average gaze position along x axis (pixels);
- average gaze position along y axis (pixels);
- fixation ID (integer) ( = saccade);
- pupil size (left);
- pupil size (right);
- timestamp (every 4 ms);
- stimulus (code of the image shown in the screen).
2.3. ETT Image Dataset
2.4. Statistical and Deep Learning Methods for the VSST Data Analysis
2.4.1. Statistical Methods
2.4.2. Deep Learning Modelling: Autoencoders and the U-Net Architecture
2.4.3. K-Means Clustering
3. Results and Discussion
3.1. Statistical Analysis of Pupil and Blinking Data
3.1.1. Outliers Detection and Kruskall–Wallis Test
3.1.2. Bootstrapping Method
3.2. Mapping Latent Space Representations of ETT Images to Phenotypic Groups
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Blinking Rate | Maximum Pupil Size Variation | Blinking Average Duration | ||||
---|---|---|---|---|---|---|
Classes | p-Value | H Statistic | p-Value | H Statistic | p-Value | H Statistic |
H–C | 0.0059 | 7.5880 | 0.0121 | 6.2899 | 0.1079 | 2.5847 |
H–E | 0.2092 | 1.5771 | 0.6534 | 0.2016 | 0.5289 | 0.3966 |
E–C | 0.3258 | 0.9654 | 0.0039 | 8.3394 | 0.0058 | 7.6226 |
Blinking Rate | Maximum Pupil Size Variation | Blinking Average Duration | ||||
---|---|---|---|---|---|---|
Classes | p-Value | H Statistic | p-Value | H Statistic | p-Value | H Statistic |
H–C | 0.0027 | 9.0171 | 0.0075 | 7.1445 | 0.0488 | 3.8814 |
H–E | 0.1877 | 1.7355 | 0.4984 | 0.4584 | 0.5115 | 0.4309 |
E–C | 0.2390 | 1.3867 | 0.0008 | 11.3152 | 0.0016 | 9.999 |
Classes | Blinking Rate | Maximum Pupil Size Variation | Blinking Average Duration |
---|---|---|---|
H–C | 59.11% | 48.06% | 10.99% |
E–C | 2.05% | 67.92% | 58.42% |
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Pancino, N.; Graziani, C.; Lachi, V.; Sampoli, M.L.; Ștefǎnescu, E.; Bianchini, M.; Dimitri, G.M. A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data. Mathematics 2021, 9, 3159. https://doi.org/10.3390/math9243159
Pancino N, Graziani C, Lachi V, Sampoli ML, Ștefǎnescu E, Bianchini M, Dimitri GM. A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data. Mathematics. 2021; 9(24):3159. https://doi.org/10.3390/math9243159
Chicago/Turabian StylePancino, Niccolò, Caterina Graziani, Veronica Lachi, Maria Lucia Sampoli, Emanuel Ștefǎnescu, Monica Bianchini, and Giovanna Maria Dimitri. 2021. "A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data" Mathematics 9, no. 24: 3159. https://doi.org/10.3390/math9243159
APA StylePancino, N., Graziani, C., Lachi, V., Sampoli, M. L., Ștefǎnescu, E., Bianchini, M., & Dimitri, G. M. (2021). A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data. Mathematics, 9(24), 3159. https://doi.org/10.3390/math9243159