Belief in omens and superstitions among patients with chronic neurological disorders.

Introduction: Chronic neurological disorders may affect various cognitive processes, including religiosity or superstitious belief. We investigated whether superstitious beliefs are equally prevalent in patients with Parkinson's disease (PD), people with epilepsy (PWE), patients with multiple sclerosis (MS) and healthy controls (HCs). Methods: From late 2014 to early 2023 we conducted a cross-sectional in-person anonymous paper-based survey at the tertiary clinic of Vilnius University Hospital Santaros Klinikos among outpatients and HCs by asking them to ascribe meaning or report belief for 27 culturally adapted statements (9 omens and 18 superstitions). The sum of items that a respondent believes in was labeled the superstition index (SI). The SI was compared between groups by means of the Kruskal-Wallis (H) test and negative binomial regression modeling. A two-step cluster analysis was performed to discern different subgroups based on answers to the items of the SI. Results: There were 553 respondents who completed the questionnaire (183 PWE, 124 patients with PD, 133 with MS and 113 HCs). Complete SI scores were collected for 479 (86.6%) participants and they were lower in patients with PD (n = 96, Md = 1, IQR = 0-5.75) in comparison to those with epilepsy (n = 155, Md = 6, IQR = 1-14), MS (n = 120, Md = 4, IQR = 0-12) or HCs (n = 108, Md = 4.5, IQR = 1-10), H (3) = 26.780, p < 0.001. In a negative binomial regression model (n = 394, likelihood ratio χ2 = 35.178, p < 0.001), adjusted for sex, place of residence, income and education, female sex was the only characteristic associated with the SI (ß = 0.423, OR = 1.526, 95% CI = 1.148 to 2.028). Both female sex (ß = 0.422, OR = 1.525, 95% CI = 1.148 to 2.026) and Parkinson's disease (ß = -0.428, OR = 0.652, 95% CI = 0.432 to 0.984) were significant predictors of the SI when age was removed from the model. Two-step cluster analysis resulted in individuals with PD being grouped into "extreme non-believer," "non-believer" and "believer" rather than "non-believer" and "believer" clusters characteristic for PWE, patients with MS and HCs. Conclusion: Our study suggests that individuals with PD believe in less superstitions than patients with MS, PWE or HCs. The results of this investigation should be independently confirmed after adjusting for PD-specific variables.

Parkinson's disease classification with CWNN: Using wavelet transformations and IMU data fusion for improved accuracy.

BACKGROUND: Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by motor impairments and various other symptoms. Early and accurate classification of PD patients is crucial for timely intervention and personalized treatment. Inertial measurement units (IMUs) have emerged as a promising tool for gathering movement data and aiding in PD classification. OBJECTIVE: This paper proposes a Convolutional Wavelet Neural Network (CWNN) approach for PD classification using IMU data. CWNNs have emerged as effective models for sensor data classification. The objective is to determine the optimal combination of wavelet transform and IMU data type that yields the highest classification accuracy for PD. METHODS: The proposed CWNN architecture integrates convolutional neural networks and wavelet neural networks to capture spatial and temporal dependencies in IMU data. Different wavelet functions, such as Morlet, Mexican Hat, and Gaussian, are employed in the continuous wavelet transform (CWT) step. The CWNN is trained and evaluated using various combinations of accelerometer data, gyroscope data, and fusion data. RESULTS: Extensive experiments are conducted using a comprehensive dataset of IMU data collected from individuals with and without PD. The performance of the proposed CWNN is evaluated in terms of classification accuracy, precision, recall, and F1-score. The results demonstrate the impact of different wavelet functions and IMU data types on PD classification performance, revealing that the combination of Morlet wavelet function and IMU data fusion achieves the highest accuracy. CONCLUSION: The findings highlight the significance of combining CWT with IMU data fusion for PD classification using CWNNs. The integration of CWT-based feature extraction and the fusion of IMU data from multiple sensors enhance the representation of PD-related patterns, leading to improved classification accuracy. This research provides valuable insights into the potential of CWT and IMU data fusion for advancing PD classification models, enabling more accurate and reliable diagnosis.