Using Electroencephalogram-Extracted Nonlinear Complexity and Wavelet-Extracted Power Rhythm Features during the Performance of Demanding Cognitive Tasks (Aristotle's Syllogisms) in Optimally Classifying Patients with Anorexia Nervosa

Anna Karavia; Anastasia Papaioannou; Ioannis Michopoulos; Panos C Papageorgiou; George Papaioannou; Fragiskos Gonidakis; Charalabos C Papageorgiou

doi:10.3390/brainsci14030251

Using Electroencephalogram-Extracted Nonlinear Complexity and Wavelet-Extracted Power Rhythm Features during the Performance of Demanding Cognitive Tasks (Aristotle's Syllogisms) in Optimally Classifying Patients with Anorexia Nervosa

Brain Sci. 2024 Mar 4;14(3):251. doi: 10.3390/brainsci14030251.

Authors

Anna Karavia¹, Anastasia Papaioannou^{2

3}, Ioannis Michopoulos¹, Panos C Papageorgiou⁴, George Papaioannou⁵, Fragiskos Gonidakis², Charalabos C Papageorgiou^{2

3}

Affiliations

¹ Eating Disorder Unit, 2nd Department of Psychiatry, Medical School, National & Kapodistrian University of Athens, 'Attikon' University Hospital, 1 Rimini St., 12462 Athens, Greece.
² 1st Department of Psychiatry, Medical School, National & Kapodistrian University of Athens, Eginition Hospital, 74 Vas. Sofias Ave., 11528 Athens, Greece.
³ Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS" (UMHRI), University Mental Health, Soranou tou Efesiou 2, Papagou, 11527 Athens, Greece.
⁴ Department of Electrical and Computer Engineering, University of Patras, 26504 Rion-Patras, Greece.
⁵ Center for Research of Nonlinear Systems (CRANS), Department of Mathematics, University of Patras, 26500 Rion-Patras, Greece.

Abstract

Anorexia nervosa is associated with impaired cognitive flexibility and central coherence, i.e., the ability to provide an overview of complex information. Therefore, the aim of the present study was to evaluate EEG features elicited from patients with anorexia nervosa and healthy controls during mental tasks (valid and invalid Aristotelian syllogisms and paradoxes). Particularly, we examined the combination of the most significant syllogisms with selected features (relative power of the time-frequency domain and wavelet-estimated EEG-specific waves, Higuchi fractal dimension (HFD), and information-oriented approximate entropy (AppEn)). We found that alpha, beta, gamma, theta waves, and AppEn are the most suitable measures, which, when combined with specific syllogisms, form a powerful tool for efficiently classifying healthy subjects and patients with AN. We assessed the performance of triadic combinations of "feature-classifier-syllogism" via machine learning techniques in correctly classifying new subjects in these two groups. The following triads attain the best classifications: (a) "AppEn-invalid-ensemble BT classifier" (accuracy 83.3%), (b) "Higuchi FD-valid-linear discriminant" (accuracy 75%), (c) "alpha amplitude-valid-SVM" (accuracy 83.3%), (d) "alpha RP-paradox-ensemble BT" (accuracy 85%), (e) "beta RP-valid-ensemble" (accuracy 85%), (f) "gamma RP-valid-SVM" (accuracy 85%), and (g) "theta RP-valid-KNN" (accuracy 80%). Our findings suggest that anorexia nervosa has a specific information-processing style across reasoning tasks in the brain as measured via EEG activity. Our findings also contribute to further supporting the view that entropy-oriented, i.e., information-based features (the AppEn measure used in this study) are promising diagnostic tools (biomarkers) in clinical applications related to medical classification problems. Furthermore, the main EEG-specific frequency waves are extremely enhanced and become powerful classification tools when combined with Aristotle's syllogisms.

Keywords: Aristotelian syllogisms; EEG features; anorexia nervosa; approximate entropy; information processing; machine learning classifiers; reasoning tasks.

Grants and funding

The study was funded by the Regional Governor of Attica (funding number: 16092) and co-funded by the Athanasios & Marina Martinou Foundation (AMMF)-nonprofit civil company AEGEAS (funding number: 34430/2020; Athens, 11 November 2020).