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.

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment.

The assessment of physiological signals such as the electroencephalography (EEG) has become a key point in the research area of emotion detection. This study compares the performance of two EEG devices, a low-cost brain-computer interface (BCI) (Emotiv EPOC+) and a high-end EEG (BrainVision), for the detection of four emotional conditions over 20 participants. For that purpose, signals were acquired with both devices under the same experimental procedure, and a comparison was made under three different scenarios, according to the number of channels selected and the sampling frequency of the signals analyzed. A total of 16 statistical, spectral and entropy features were extracted from the EEG recordings. A statistical analysis revealed a major number of statistically significant features for the high-end EEG than the BCI device under the three comparative scenarios. In addition, different machine learning algorithms were used for evaluating the classification performance of the features extracted from high-end EEG and low-cost BCI in each scenario. Artificial neural networks reported the best performance for both devices with an F[Formula: see text]-score of 75.08% for BCI and 98.78% for EEG. Although the professional EEG outcomes were higher than the low-cost BCI ones, both devices demonstrated a notable performance for the classification of the four emotional conditions.

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli.

The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants' responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

Effectiveness of the level of personal relevance of visual autobiographical stimuli in the induction of positive emotions in young and older adults: pilot study protocol for a randomized controlled trial.

BACKGROUND: The ability to retrieve specific memories is a cognitive and emotional protective factor. Among the most effective techniques to generate autobiographical memories is the use of audio-visual stimuli, particularly images. Developing and improving techniques that facilitate the generation of such memories could be highly effective in the prevention of depressive symptoms, especially in the elderly population. The aim of the present study is to examine how the level of personal relevance of pictures as autobiographical memory cues to induce positive emotions may affect an individual's emotion regulation. METHODS: The participants, 120 older adults aged 65 and over and 120 young adults aged between 18 and 35, of both sexes and without depressive symptoms, will be induced to a negative mood state by means of viewing a film clip. Following the negative mood induction, the participants will be shown positive images according to experimental group to which they were randomly assigned (high personal relevance: personal autobiographical photographs; medium personal relevance: pictures of favourite locations associated with specific positive autobiographical memories; and low personal relevance: positive images from the International Affective Picture System). We will analyse the differences in subjective (responses to questionnaires) and objectives measures (EEG signal, heart rate variability and electrodermal activity) between the groups before and after the induction of negative affect and following the recall of positive memories. DISCUSSION: The use of images associated with specific positive autobiographical memories may be an effective input for inducing positive mood states, which has potentially important implications for their use as a cognitive behavioural technique to treat emotional disorders, such as depression, which are highly prevalent among older adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT04251104 . Registered on 30 January 2020.

Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity.

Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate identification of stress/calm condition from electrodermal activity (EDA) signals. The acquisition of EDA signals from a commercial wearable as well as their storage and processing are presented. Several time-domain, frequency-domain and morphological features are extracted over the skin conductance response of the EDA signals. Afterwards, a classification is undergone by using several classical support vector machines (SVMs) and deep support vector machines (D-SVMs). In addition, several binary classifiers are also compared with SVMs in the stress/calm identification task. Moreover, a series of video clips evoking calm and stress conditions have been viewed by 147 volunteers in order to validate the classification results. The highest F1-score obtained for SVMs and D-SVMs are 83% and 92%, respectively. These results demonstrate that not only classical SVMs are appropriate for classification of biomarker signals, but D-SVMs are very competitive in comparison to other classification techniques. In addition, the results have enabled drawing useful considerations for the future use of SVMs and D-SVMs in the specific case of stress/calm identification.