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. This diverse dataset enables better training and validation of our results. Among the methods evaluated, the CNN (deep learning) approach outperformed others, achieving a remarkable classification accuracy of 97.45% for patients with Moderate Alzheimer's Dementia (ADM) and 97.03% for patients with Advanced Alzheimer's Dementia (ADA). Importantly, all the compared methods were rigorously assessed under identical conditions. The proposed DL model, specifically CNN, effectively extracts time domain features from EEG data in time, resulting in a significant reduction in learnable parameters and data redundancy.

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. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. RESULTS: The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. CONCLUSIONS: By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification.

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. The developed algorithm decomposes the EEG signals into a system of radial basis functions using the method of fuzzy means. This decomposition helps to obtain the information from the various electrodes of the EEG and allows separating between healthy controls and patients with schizophrenia. The proposed method has been compared with classical machine learning algorithms, such as, K-Nearest Neighbor, Adaboost, Support Vector Machine, and Bayesian Linear Discriminant Analysis. The results show that the proposed method obtains the highest values in terms of balanced accuracy, recall, precision and F1 score, close to 93 % in all cases. The model developed in this study can be implemented in brain activity analysis systems that help in the prediction of patients with schizophrenia.

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. Results: The results obtained with real datasets validate the high accuracy of the proposed classification method. Thus, effectively characterizing the changes in EEG signals acquired from schizophrenia patients and healthy volunteers. More specifically, values of accuracy better than 93% has been obtained in the present research. Additionally, a comparative study with other approaches based on well-knows machine learning methods shows that the proposed method provides better results than recently proposed algorithms in schizophrenia detection. Conclusion: The proposed method can be used as a diagnostic tool in the detection of the schizophrenia, helping for early diagnosis and treatment.

[The chest x-ray in acute bronchiolitis: Technical quality, findings, and an assessment of its reliability]. / La radiografía de tórax en la bronquiolitis aguda: calidad técnica, hallazgos y evaluación de su fiabilidad.

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. METHOD: Out of a total of 281 infants admitted due to acute bronchiolitis, 140 chest x-rays were performed. Twelve doctors from different specialities evaluated the presence or absence of 10 radiological signs previously agreed by consensus. The level of agreement between 2 observers, and in groups of 3 or more, were analysed using the Cohen and Fleiss kappa index, respectively. RESULTS: Only 8.5% of the radiographs showed evidence of a complicated AB. The between-observer agreement in groups of 3 or more was medium, and with little variability (kappa: 0.20-0.40). However, between 2 observers, each observer against radiologist, the variability was wider, (kappa: -0.20-0.60). This level of agreement was associated with factors including, the sign to evaluate, the medical specialty, and level of professional experience. CONCLUSION: The low levels of agreement between observers and the wide variability, makes the chest x-ray an unreliable diagnostic tool, and is not recommended for the assessment of infants with AB.

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. METHODS: Seventy-six patients (59.2% females) and 40 healthy controls (50% females), aged 18 to 55 years, were assessed using a comprehensive neurocognitive battery (six neurocognitive domains), and the Functioning Assessment Short Test (FAST), at baseline and after a 5-year follow-up. Stepwise regression models were used to identify predictor variables related to psychosocial functioning. RESULTS: The number of hospitalizations during the follow-up, the change occurred in the neurocognitive composite index (NCI change), and NCI at baseline explained 30.8% of the variance of functioning. The number of hospitalizations during the follow-up was the variable that explained a greater percentage of the variance (16.9%). Verbal memory at baseline and the change in sustained attention during the follow-up explained 10% and 5.9% of the variance of the psychosocial functioning, respectively. LIMITATIONS: The interval of 5 years between the two assessments could be too short to detect a possible progression in functional outcome for the overall sample. CONCLUSIONS: The clinical course during the follow-up is the factor that has a greater impact on psychosocial functioning in patients with BD. Thus, the interventions aimed to promote prevention of relapses should be considered as essential for avoiding functional impairment in these patients.

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. The split version of the Global Assessment of Functioning scale (GAF-F) and the Functioning Assessment Short Test (FAST) were used to examine psychosocial functioning at baseline (T1), and after a 5-year follow-up (T2). The statistical analysis was performed through repeated measures ANOVA and hierarchical cluster analysis based on the GAF-F and the FAST scores at T1 and T2. Eighty-seven patients (87.9%) were evaluated at T2. The cluster analysis identified two groups of patients. The first group included 44 patients (50.6%) who did not show a progression of the functional impairment (BD-NPI). The second cluster, which included 43 patients (49.4%), was characterized by a progression of the functional impairment (BD-PI). The BD-PI had a higher number of relapses and a higher number of hospitalizations during the follow-up period, as well as worse neurocognitive functioning than the BD-NPI. The repeated measures ANOVA confirmed that the psychosocial performance of BD-NPI is stable while there was a progression of the functional deterioration in BD-PI. The trajectory of the psychosocial functioning of patients with BD is not homogeneous. Our results suggest that in at least one subset of patients with BD, which might account for half of the patients, the disease has a progressive course.