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Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation. This work has been supported by FEDER and MICINN, TEC2016-77,791-C4-2-R, and UPV/EHU-Basque Research Groups IT11156 and Basque Country EleKin Research Group

Community opposition is one of the key obstacles to the expansion of wind turbine developments. The scholarly literature has focused on public opinion on wind farms (WFs) and the level of community acceptance. The efforts of Original Equipment Manufacturers (OEMs), to gain community acceptance has been under-researched. To fill this gap, the present study analyzes the evolution of interventions by a leading OEM to secure the acceptance of local communities for wind energy projects and the outcomes of those efforts. The fieldwork consisted of a longitudinal case study carried out from 2014 to 2017 by a leading international OEM. In depth personal interviews were also conducted with six experts in the field. The main efforts of the OEM focused on the visual impact of the developments, health and safety issues, community involvement and social investment in the community. In selecting the location for developing a WF, economic criteria usually prevail over social criteria. Although the company makes social investments in the community, different groups point out that those local communities should be taken more seriously, as they can serve as facilitators in the development of projects. Implications for stakeholders such as developers and policy makers are discussed.

Purpose: A videofluoroscopic analysis is the gold standard approach to determine whether a dysphagia problem exists. This procedure is invasive as it involves radiation but also provides th...

This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users. This work is being funded by Grants: TEC201677791-C4 from Plan Nacional de I + D + i, Ministry of Economic Affairs and Competitiveness of Spain and from the DomusVi Foundation Kms para recorder, the Basque Government (ELKARTEK KK-2018/00114, GEJ IT1189-19, the Government of Gipuzkoa (DG18/14 DG17/16), UPV/EHU (GIU19/090), COST ACTION (CA18106, CA15225).

Essential tremor (ET) in the western world is the most common movement disorder, and 50-70% of essential tremor cases are estimated to be genetic in origin 14. This work on selection of nonlinear biomarkers derived from drawings and handwriting is part of a wider cross-study for the diagnosis of essential tremor led by Biodonostia Institute. These biomarkers include not only classic linear features, but also non-linear: fractal dimension and entropy. The presence of integrated features of other diseases such as stress is also analyzed. In future works, these new biomarkers will be integrated with the ones obtained in the wider study of Biodonostia. Note that the use of these methods provide undoubted benefits towards the development of more sustainable, low-cost, high-quality, and non-invasive technologies. These systems are easily adaptable to the user and environment, and can be very useful in real complex environments with regard to a social and economic point of view.

Environmental enrichment (EE) paradigms are designed to enhance laboratory animals surroundings to encourage natural behaviors. Some enrichment paradigms also include a social component, based on the social interactions typical of the genus and species. Novel automatic methodologies based on image are becoming useful tools to improve laboratory works. This paper present a first approach to the automatic image analysis of laboratory rats in EE: behaviour, drug effects and pathology. The new methodology is based on image and Machine Learning paradigms and will become a useful tool for Neuroscience issues.

Among neural disorders related to movement, essential tremor has the highest prevalence, in fact, it is twenty times more common than Parkinson&rsquo, s disease. The drawing of the Archimedes&rsquo, spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users, and taking into account social and economic points of view, it could be very helpful in real complex environments.

Objective: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. Methods: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. Results: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. Conclusion: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.

This paper is an extended version of one paper published in the 4th IEEE International Work Conference on Bioinspired Intelligence, Donostia, Spain, 9–12 June 2015 Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson's disease. Interestingly, about 50%-70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes' spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments. This work has been partially supported by the University of the Basque Country under project ref. UPV/EHU-58/14, SAIOTEK program and others from the Basque Government, the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03, the University of Vic-Central University of Catalonia under the research grant R0904, INNPACTO program from the Spanish Government, and UPV/EHU Summer Courses Foundation.