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Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance...
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... Generalmente, la CGP junto con la VFC (o bien con la FC) son usados para obtener un referente más confiable de la actividad autonómica asociada a estados emocionales negativos (McNaboe et al., 2022) y su uso se basa en la noción de que a un estado emocional subyace un estado fisiológico susceptible de ser registrado (Candia-Rivera et al., 2022). ...
El entrenamiento en respiración es útil para modificar el estado emocional y autonómico. El objetivo de este estudio fue determinar el efecto de la respiración diafragmática sobre la actividad autonómica y el estado emocional de universitarios irregulares. Mediante un muestreo no probabilístico y sin grupo control, se realizó un entrenamiento en relajación. Se midieron variables emocionales (ansiedad, depresión y estrés) y autonómicas (conductancia galvánica de la piel [CGP] y frecuencia cardiaca [FC]) para comparar los registros antes y después del entrenamiento. La intervención promovió decrementos significativos en los niveles de depresión (t = 5.559, gl = 28, p < 0.001), así como de ansiedad y estrés (t = 6.432, gl = 28, p < 0.001). La CGP también disminuyó (t = 2.327, gl = 28, p = 0.027), pero no la FC (t = 0.405, gl =28, p = 0.689). Es decir, las personas estaban menos estresadas, aunque no más relajadas. La respiración diafragmática es eficaz para el control emocional y el funcionamiento autonómico de universitarios irregulares.
... Consequently, this led to the exploration of alternative electric extracerebral measurements like ECG and EDA [21]. ...
This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue.
It is increasingly common in a wide variety of applied settings to collect data of multiple different types on the same set of samples. Our particular focus in this article is on studying relationships between such multiview features and responses. A motivating application arises in the context of precision medicine where multi-omics data are collected to correlate with clinical outcomes. It is of interest to infer dependence within and across views while combining multimodal information to improve the prediction of outcomes. The signal-to-noise ratio can vary substantially across views, motivating more nuanced statistical tools beyond standard late and early fusion. This challenge comes with the need to preserve interpretability, select features, and obtain accurate uncertainty quantification. We propose a joint additive factor regression model (JAFAR) with a structured additive design, accounting for shared and view-specific components. We ensure identifiability via a novel dependent cumulative shrinkage process (D-CUSP) prior. We provide an efficient implementation via a partially collapsed Gibbs sampler and extend our approach to allow flexible feature and outcome distributions. Prediction of time-to-labor onset from immunome, metabolome, and proteome data illustrates performance gains against state-of-the-art competitors. Our open-source software (R package) is available at https://github.com/niccoloanceschi/jafar.
Electrodermal activity (EDA) is a physiological signal that can be used to infer humans' affective states and stress levels. EDA can nowadays be monitored using unobtrusive wearable devices, such as smartwatches, and leveraged in personal informatics systems. A still largely uncharted issue concerning EDA is the impact on real applications of potential differences observable on signals measured concurrently on the left and right side of the human body. This phenomenon, called lateralization, originates from the distinct functions that the brain's left and right hemispheres exert on EDA. In this work, we address this issue by examining the impact of EDA lateralization in two classification tasks: a cognitive load recognition task executed in the lab and a sleep monitoring task in a real-world setting. We implement a machine learning pipeline to compare the performance obtained on both classification tasks using EDA data collected from the left and right sides of the body. Our results show that using EDA from the side that is not associated with the specific hemisphere activation leads to a significant decline in performance for the considered classification tasks. This finding highlights that researchers and practitioners relying on EDA data should consider possible EDA lateralization effects when deciding on sensor placement.
