Dynamic Monitoring of Probiotics Effect in Parkinson's Disease Patients via Swarm Decomposition and Bispectral Analysis of Electrogastrograms.

People with Parkinson's Disease (PwP) experience a significant deterioration of their daily life quality due to non-motor symptoms, with gastrointestinal dysfunctions manifesting as a vanguard of the latter. Electrogastrography (EGG) is a noninvasive diagnostic tool that can potentially provide biomarkers for the monitoring of dynamic gastric alterations that are related to daily lifestyle and treatment regimens. In this work, a robust analysis of EGG dynamics is introduced to evaluate the effect of probiotic treatment on PwP. The proposed framework, namely biSEGG, introduces a Swarm Decomposition-based enhancement of the EGG, combined with Bispectral feature engineering to model the underlying Quadratic Phase Coupling interactions between the gastric activity oscillatory components of EGG. The biSEGG features are benchmarked against the conventional Power Spectrum-based ones and evaluated through machine learning classifiers. The experimental results, when biSEGG was applied on data epochs from 11 PwP (probiotic vs placebo, AUROC: 0.67, Sensitivity/Specificity: 75/58%), indicate the superiority of biSEGG over Power Spectrum-based approaches and justify the efficiency of biSEGG in capturing and explaining intervention- and meal consumption-related alterations of the gastric activity in PwP.Clinical relevance- biSEGG holds potential for dynamic monitoring of gastrointestinal dysfunction and health status of PwP across diverse daily life scenarios.

Classification of children with ADHD through task-related EEG recordings via Swarm-Decomposition-based Phase Locking Value.

Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder mainly affecting children. ADHD children brain activity is reported to present alterations from neurotypically developed children, yet establishment of an EEG biomarker, which is of high importance in clinical practice and research, has not been achieved. In this work, task-related EEG recordings from 61 ADHD and 60 age-matched non-ADHD children are analyzed to examine the underlying Cross-Frequency Coupling phenomena. The proposed framework introduces personalized brain rhythm extraction in the form of oscillatory modes via Swarm Decomposition, allowing for the transition from sensor-level connectivity to source-level connectivity. Oscillatory modes are then subjected to a phase locking value-based feature extraction and the efficiency of the extracted features in separating ADHD from non-ADHD individuals is evaluated by means of a nested 5-fold cross validation scheme. The experimental results of the proposed framework (Area Under the Receiver Operating Characteristics Curve-AUROC: 0.9166) when benchmarked against the commonly used filter-based brain rhythm extraction (AUROC: 0.8361) underscore its efficiency and demonstrate its overall superiority over other state-of-the-art functional connectivity approaches in this classification task for this dataset.Clinical relevance-This framework provides novel insights about brain regions of interest that are involved in ADHD task-related function and holds promise in providing objective ADHD biomarkers by extending classic sensor-level connectivity to source-level.