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ChMinMaxPat: Investigations on Violence and Stress Detection Using EEG Signals.
Diagnostics (Basel)
November 2024
Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey.
Background And Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data for violence detection. The primary objective is to assess the classification capability of the proposed XFE model, which uses a next-generation feature extractor, and to obtain interpretable findings for EEG-based violence and stress detection.
View Article and Find Full Text PDFEEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning.
Cogn Neurodyn
October 2024
Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Schizophrenia (SZ) is a serious mental disorder that can mainly be distinguished by symptoms including delusions and hallucinations. This mental disorder makes difficult conditions for the person and her/his relatives. Electroencephalogram (EEG) signal is a sophisticated neuroimaging technique that helps neurologists to diagnose this mental disorder.
View Article and Find Full Text PDFA new trial monitoring plan (TMP) template for clinical trials: output from a Delphi process.
Trials
November 2024
MRC Clinical Trials Unit at UCL, 90 High Holborn, London, WC1V 6LJ, UK.
Background: Monitoring is a crucial part of trial conduct and ensures that participants' data is fairly represented, and future healthcare information is enhanced. This project aims to improve trial monitoring by creating a trial monitoring plan (TMP) template with input from individuals experienced in monitoring clinical trials.
Methods: A review of monitoring plans received from UK Clinical Research Collaboration (UKCRC) registered clinical trials units (CTU)s created the basis for a preliminary TMP template and a Delphi survey.
A Longitudinal Model for a Dynamic Risk Score to Predict Delayed Cerebral Ischemia after Subarachnoid Hemorrhage.
Bioengineering (Basel)
September 2024
Neurocritical Care Unit, Department of Neurosurgery and Institute of Intensive Care Medicine, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland.
Background: Accurate longitudinal risk prediction for DCI (delayed cerebral ischemia) occurrence after subarachnoid hemorrhage (SAH) is essential for clinicians to administer appropriate and timely diagnostics, thereby improving treatment planning and outcome. This study aimed to develop an improved longitudinal DCI prediction model and evaluate its performance in predicting DCI between day 4 and 14 after aneurysm rupture.
Methods: Two DCI classification models were trained: (1) a static model based on routinely collected demographics and SAH grading scores and (2) a dynamic model based on results from laboratory and blood gas analysis anchored at the time of DCI.
Voter Support for Policies Associated With Child Health as National Campaign Priorities.
JAMA Health Forum
September 2024
Nemours Children's Health, Wilmington, Delaware.
Article Synopsis
- Child health policies are often overlooked in political candidate platforms, potentially due to a lack of awareness regarding voter support or the belief that these issues aren't divisive enough for partisan appeal.
- A nationwide survey conducted in early 2024 revealed that many voters are likely to support candidates who back child health initiatives, including Medicaid reform and the refundable child tax credit.
- Overall, significant support was noted among women and Democrats/Independents for child-focused policies, while Republican support was notably lower, with many respondents indicating they'd vote for candidates backing these policies.
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