Understanding human response to tactile stimuli: A Machine Learning approach

Whereas understanding human reaction to touch is of great interest in many medical applications, it is still a very unknown field. This research aims to clarify the nature of the relation between endogenous and exogenous attention by analysing electroencephalografic (EEG) data regarding human touch. To this end, data collected from twelve subjects under an experiment based on a variation of the Posner’s cue-target paradigm has been used. After pre-processing, several multi-class classification models based on state-of-the-art machine learning algorithms have been implemented and their accuracy in detecting different experimental conditions have been evaluated. A temporal analysis has also been performed to select the most representative time points. Results showed that although the physical stimuli was identical across conditions, different types of attentional scenarios were classified above chance. Further, the hemisphere contralateral and ipsilateral to the attended side contributed differently, across time, to the accuracy of classification

Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain

To date, the use of synthetic data generation techniques in the health and wellbeing domain has been mainly limited to research activities. Although several open source and commercial packages have been released, they have been oriented to generating synthetic data as a standalone data reparation process and not integrated into a broader analysis or experiment testing workflow. In this context, the VITALISE project is working to harmonize Living Lab research and data capture protocols and to provide controlled processing access to captured data to industrial and scientific communities. In this paper, we present the initial design and implementation of our synthetic data generation approach in the context of VITALISE Living Lab controlled data processing workflow, together with identified challenges and future developments. By uploading data captured from Living Labs, generating synthetic data from them, developing analysis locally with synthetic data, and then executing them remotely with real data, the utility of the proposed workflow has been validated. Results have shown that the presented workflow helps accelerate research on artificial intelligence, ensuring compliance with data protection laws. The presented approach has demonstrated how the adoption of state-of-the-art synthetic data generation techniques can be applied for real-world applications

Influencia de la frecuencia respiratoria inducida en los valores HRV

The study of Place Cells, hippocampal neurons tuned to spatial locations in the environment, is central to elucidate how the brain encodes and retrieves spatial information. Advances in genetic and imaging technologies have allowed keeping track of the dynamics of large ensembles of Place Cells across multiple days in mice. As the brain processes information at the neuronal population level, novel recording techniques such as in-vivo calcium imaging have the potential to unveil the mechanisms underlying the dynamics of place coding. However, with new recording paradigms comes the need to standardize and optimize the processing and first analysis stages of the data. In this work, we present our efforts in building a pipeline to process, extract, filter, track and analyze Place Cells from mice calcium imaging recordings in a linear-track experiment. To validate the pipeline, we show accurate prediction of the animal actions from the processed neural recordings. Finally, building on the previous steps, we present some tentative results on Place Cell turnover and the relation between predictive-accuracy and noise correlations

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data

Background: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. Objective: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decisionmakers. Methods: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. Results: Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. Conclusion: Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases

Aging effects on resting state networks after an emotional memory task

Aging is one of the primary health concerns in nowadays world, being memory decline an important worry that affects daily functioning of healthy adults. This work presents a study of this kind of decline in normal aging, by processing and analysing electroencephalographs (EEGs) of 72 healthy subjects, categorized as either young or older adults. These biosignals are first pre-processed using a customized pre-processing pipeline using the EEGLAB tool in Matlab. Once the signals have been pre-processed, two different analyses are carried out: a frequency analysis, obtaining the powers for each band of the EEG signals, and a time-frequency analysis. The data obtained from these studies is analysed using R, obtaining some important results and conclusions

Monitorización longitudinal de la compliancia pulmonar basada en la TIE en pacientes infectados por COVID-19

El COVID-19 es una infección vírica que causa complicaciones en el sistema respiratorio. Los síntomas más comunes sugieren que las tecnologías de imagen médica pueden ofrecer información relevante sobre el diagnóstico, tratamiento, y posterior recuperación de este tipo de infecciones. Entre estas técnicas de imagen se encuentra la Tomografía de Impedancia Eléctrica (TIE), la cual se trata de una técnica no invasiva, libre de radiación ionizante y de uso continuo seguro que genera imágenes utilizando secuencias de inyecciones de corriente eléctrica y mediciones de voltaje alrededor del cuerpo. Por todo ello, la TIE podría considerarse como una herramienta adecuada para el estudio y monitorización del comportamiento regional de los pulmones infectados. Además, esta técnica podría también ser una herramienta válida para una clasificación preliminar entre los fenotipos del COVID-19. Este estudio se basa en la monitorización longitudinal de dos pacientes infectados por el nuevo coronavirus: los resultados indican que uno de los pacientes podría pertenecer al fenotipo H, mientras que el segundo podría clasificarse como tipo L. Se ha concluido que la TIE es una herramienta muy útil a la hora de recopilar información sobre el COVID-19, así como de sus diferentes fenotipos

Spatial characterization of the effect of age and sex on macular layer thicknesses and foveal pit morphology

Characterizing the effect of age and sex on macular retinal layer thicknesses and foveal pit morphology is crucial to differentiating between natural and disease-related changes. We applied advanced image analysis techniques to optical coherence tomography (OCT) to: 1) enhance the spatial description of age and sex effects, and 2) create a detailed open database of normative retinal layer thickness maps and foveal pit shapes. The maculae of 444 healthy subjects (age range 21–88) were imaged with OCT. Using computational spatial data analysis, thickness maps were obtained for retinal layers and averaged into 400 (20 x 20) sectors. Additionally, the geometry of the foveal pit was radially analyzed by computing the central foveal thickness, rim height, rim radius, and mean slope. The effect of age and sex on these parameters was analyzed with multiple regression mixed-effects models. We observed that the overall age-related decrease of the total retinal thickness (TRT) (-1.1% per 10 years) was mainly driven by the ganglion cell-inner plexiform layer (GCIPL) (-2.4% per 10 years). Both TRT and GCIPL thinning patterns were homogeneous across the macula when using percentual measurements. Although the male retina was 4.1 μm thicker on average, the greatest differences were mainly present for the inner retinal layers in the inner macular ring (up to 4% higher TRT than in the central macula). There was an age-related decrease in the rim height (1.0% per 10 years) and males had a higher rim height, shorter rim radius, and steeper mean slope. Importantly, the radial analysis revealed that these changes are present and relatively uniform across angular directions. These findings demonstrate the capacity of advanced analysis of OCT images to enhance the description of the macula. This, together with the created dataset, could aid the development of more accurate diagnosis models for macular pathologies

Spatial characterization of the effect of age and sex on macular layer thicknesses and foveal pit morphology

Characterizing the effect of age and sex on macular retinal layer thicknesses and foveal pit morphology is crucial to differentiating between natural and disease-related changes. We applied advanced image analysis techniques to optical coherence tomography (OCT) to: 1) enhance the spatial description of age and sex effects, and 2) create a detailed open database of normative retinal layer thickness maps and foveal pit shapes. The maculae of 444 healthy subjects (age range 21–88) were imaged with OCT. Using computational spatial data analysis, thickness maps were obtained for retinal layers and averaged into 400 (20 x 20) sectors. Additionally, the geometry of the foveal pit was radially analyzed by computing the central foveal thickness, rim height, rim radius, and mean slope. The effect of age and sex on these parameters was analyzed with multiple regression mixed-effects models. We observed that the overall age-related decrease of the total retinal thickness (TRT) (-1.1% per 10 years) was mainly driven by the ganglion cell-inner plexiform layer (GCIPL) (-2.4% per 10 years). Both TRT and GCIPL thinning patterns were homogeneous across the macula when using percentual measurements. Although the male retina was 4.1 μm thicker on average, the greatest differences were mainly present for the inner retinal layers in the inner macular ring (up to 4% higher TRT than in the central macula). There was an age-related decrease in the rim height (1.0% per 10 years) and males had a higher rim height, shorter rim radius, and steeper mean slope. Importantly, the radial analysis revealed that these changes are present and relatively uniform across angular directions. These findings demonstrate the capacity of advanced analysis of OCT images to enhance the description of the macula. This, together with the created dataset, could aid the development of more accurate diagnosis models for macular pathologies.This study was partially co-funded by the Instituto de Salud Carlos III (https://www.isciii.es) through the projects PI14/00679 (IG) and PI16/00005 (IG), by the Basque Foundation for Health Innovation and Research (https://www.bioef.org) through the project BIO17/ND/010 (IG), and by the Department of Health of the Basque Government (https://www.euskadi.eus/gobierno-vasco/departamento-salud) through the projects 2019111100 (IG), 2020333033(IG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Automatic assessment of functional health decline in older adults based on smart home data.

In the context of an aging population, tools to help elderly to live independently must be developed. The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavioral data to automatically detect one of the most common consequences of aging: functional health decline. After gathering the longitudinal smart home data of 29 older adults for an average of &gt;2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing 10 behavioral features. Using this data, we created regression models to predict absolute and standardized functional health scores, as well as classification models to detect reliable absolute change and positive and negative fluctuations in everyday functioning. Functional health was assessed every six months by means of the Instrumental Activities of Daily Living-Compensation (IADL-C) scale. Results show that total IADL-C score and subscores can be predicted by means of activity-aware smart home data, as well as a reliable change in these scores. Positive and negative fluctuations in everyday functioning are harder to detect using in-home behavioral data, yet changes in social skills have shown to be predictable. Future work must focus on improving the sensitivity of the presented models and performing an in-depth feature selection to improve overall accuracy