Inter-hospital moderate and advanced Alzheimer's disease detection through convolutional neural networks.

Electroencephalography (EEG) has been a fundamental technique in the identification of health conditions since its discovery. This analysis specifically centers on machine learning (ML) and deep learning (DL) methodologies designed for the analysis of electroencephalogram (EEG) data to categorize individuals with Alzheimer's Disease (AD) into two groups: Moderate or Advanced Alzheimer's dementia. Our study is based on a comprehensive database comprising 668 volunteers from 5 different hospitals, collected over a decade.

Inter-Hospital Advanced and Mild Alzheimer's Disease Classification Based on Electroencephalogram Measurements via Classical Machine Learning Algorithms.

Background: In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA).

Objective: This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals.

Methods: The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022.

Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This electrical activity can be recorded and processed to aid in the clinical diagnosis of mental disorders. In this study, a novel system for classifying schizophrenia patients from EEG recordings is presented.

Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions.

Purpose: In this paper, a new automated procedure based on deep learning methods for schizophrenia diagnosis is presented.

Methods: To this aim, electroencephalogram signals obtained using a 32-channel helmet are prominently used to analyze high temporal resolution information from the brain. By these means, the data collected is employed to evaluate the class likelihoods using a neuronal network based on radial basis functions and a fuzzy means algorithm.

Predictive factors of functional outcome in patients with bipolar I disorder: a five-year follow-up.

Background: Functional impairment is commonly encountered among patients with bipolar disorder (BD) during periods of remission. The distribution of the impairment of the functional outcome is heterogeneous. The objective of this current investigation was to identify neurocognitive and clinical predictors of psychosocial functioning in a sample of patients with BD.

[The chest x-ray in acute bronchiolitis: Technical quality, findings, and an assessment of its reliability].

Introduction: Despite the recommendations of the current Clinical Practice Guidelines, the chest x-ray continues to be a widely used diagnostic test in the assessment of infants with acute bronchiolitis (AB). However, there have not been many studies that have assessed its reproducibility in these patients. In the present study, an evaluation is made on the radiographs, describing their quality, their radiological findings, and provides new evidence on the agreement between observers.

Early detection of neonatal hearing loss by otoacoustic emissions and auditory brainstem response over 10 years of experience.

Objectives: A number of different screening protocols for detecting neonatal hearing loss currently exist. We present our 10 years of experience with using auditory brainstem response (ABR) complementary to otoacoustic emissions (OAEs) in the three phases hearing screening process in our hospital. Furthermore, we want to demonstrate the usefulness of these screening techniques used in combination, that remain valid to identify cases of neonatal hearing loss and meet the well-established program quality criteria for these screening protocols.

Progression of the functional deficit in a group of patients with bipolar disorder: a cluster analysis based on longitudinal data.

We aimed to examine the trajectory of psychosocial functioning in a sample of euthymic patients with bipolar disorder (BD) throughout a 5-year follow-up. Ninety-nine euthymic bipolar patients and 40 healthy controls (HC) were included. A neurocognitive assessment (17 neurocognitive measures grouped in 6 domains) was carried out at baseline.