The entropy of chaotic transitions of EEG phase growth in bipolar disorder with lithium carbonate.

The application of chaos measures the association of EEG signals which allows for differentiating pre and post-medicated epochs for bipolar patients. We propose a new approach on chaos necessary for proof of EEG metastability. Shannon entropies of concealed patterns of Schwarzian derivatives from absolute instantaneous frequency transformations of EEG signals after Hilbert transform are compared and found significantly statistically different between pre and post-medication periods when fitted to von Bertalanffy's functions. Schwarzian dynamics measures was compared at first baseline and then at the end of the first hour of one dose 300 mg lithium carbonate intake for the same subject in depressive patients. With an application of Schwarzian derivative on the prediction of von Bertalanffy's models, integration and segregation of phase growth orbits of neural oscillations can be understood as an influence of chaos on the mixing of frequencies. A phase growth constant parameter was performed to determine the bifurcation parameter of von Bertalanffy's model at each given non-overlapped EEG segment. Schwarzian derivative was sometimes very close positive near the origin but stayed negative for most of the number of segments. Lithium carbonate changed the chaotic invariants of the EEG Schwarzian dynamics and removed sharp boundaries in the bipolar spectrum.

Corrigendum to "CFC delta-beta is related with mixed features and response to treatment in bipolar II depression" [Heliyon 5 (6) (June 2019) e01898].

[This corrects the article DOI: 10.1016/j.heliyon.2019.e01898.].

Comparison of the data classification approaches to diagnose spinal cord injury.

In our previous study, we have demonstrated that analyzing the skin impedances measured along the key points of the dermatomes might be a useful supplementary technique to enhance the diagnosis of spinal cord injury (SCI), especially for unconscious and noncooperative patients. Initially, in order to distinguish between the skin impedances of control group and patients, artificial neural networks (ANNs) were used as the main data classification approach. However, in the present study, we have proposed two more data classification approaches, that is, support vector machine (SVM) and hierarchical cluster tree analysis (HCTA), which improved the classification rate and also the overall performance. A comparison of the performance of these three methods in classifying traumatic SCI patients and controls was presented. The classification results indicated that dendrogram analysis based on HCTA algorithm and SVM achieved higher recognition accuracies compared to ANN. HCTA and SVM algorithms improved the classification rate and also the overall performance of SCI diagnosis.