Harkaitz Eguiraun

On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore, it is one of the most active research areas today. Alzheimer’s disease is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a postmortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early AD detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of AD by noninvasive methods. The purpose is to examine, in a pilot study, the potential of applying machine learning algorithms to speech features obtained from suspected Alzheimer’s disease sufferers in order to help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: spontaneous speech and emotional response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of AD patients.

Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture

Abstract: The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni’s FD algorithms respectively. The methodology was tested on three case groups of European sea bass ( Dicentrarchus labrax ), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in

Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach

Abstract Alzheimers disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Western countries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by using automatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selected is based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work is feature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. The feature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters in the feature vector in order to enhance the performance of the original system while controlling the computational cost.

The role of environmental biotechnology in exploring, exploiting, monitoring, preserving, protecting and decontaminating the marine environment

In light of the Marine Strategy Framework Directive (MSFD) and the EU Thematic Strategy on the Sustainable Use of Natural Resources, environmental biotechnology could make significant contributions in the exploitation of marine resources and addressing key marine environmental problems. In this paper 14 propositions are presented focusing on (i) the contamination of the marine environment, and more particularly how to optimize the use of biotechnology-related tools and strategies for predicting and monitoring contamination and developing mitigation measures; (ii) the exploitation of the marine biological and genetic resources to progress with the sustainable, eco-compatible use of the maritime space (issues are very diversified and include, for example, waste treatment and recycling, anti-biofouling agents; bio-plastics); (iii) environmental/marine biotechnology as a driver for a sustainable economic growth.

Shannon Entropy in a European Seabass (Dicentrarchus labrax) System during the Initial Recovery Period after a Short-Term Exposure to Methylmercury

Methylmercury (MeHg) is an environmental contaminant of increasing relevance as a seafood safety hazard that affects the health and welfare of fish. Non-invasive, on-line methodologies to monitor and evaluate the behavior of a fish system in aquaculture may make the identification of altered systems feasible—for example, due to the presence of agents that compromise their welfare and wholesomeness—and find a place in the implementation of Hazard Analysis and Critical Control Points and Fish Welfare Assurance Systems. The Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) system has been shown to differentiate MeHg-treated from non-treated fish, the former displaying a lower SE value than the latter. However, little is known about the initial evolution of the system after removal of the toxicant. To help to cover this gap, the present work aims at providing information about the evolution of the SE of a European seabass system during a recuperation period of 11 days following a two-week treatment with 4 µg·MeHg/L. The results indicate that the SE of the system did not show a recovery trend during the examined period, displaying erratic responses with daily fluctuations and lacking a tendency to reach the initial SE values.

The Shannon Entropy Trend of a Fish System Estimated by a Machine Vision Approach Seems to Reflect the Molar Se:Hg Ratio of Its Feed

The present study investigates the suitability of a machine vision-based method to detect deviations in the Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) biological system fed with different selenium:mercury (Se:Hg) molar ratios. Four groups of fish were fed during 14 days with commercial feed (control) and with the same feed spiked with 0.5, 5 and 10 mg of MeHg per kg, giving Se:Hg molar ratios of 29.5 (control-C1); 6.6, 0.8 and 0.4 (C2, C3 and C4). The basal SE of C1 and C2 (Se:Hg > 1) tended to increase during the experimental period, while that of C3 and C4 (Se:Hg < 1) tended to decrease. In addition, the differences in the SE of the four systems in response to a stochastic event minus that of the respective basal states were less pronounced in the systems fed with Se:Hg molar ratios lower than one (C3 and C4). These results indicate that the SE may be a suitable indicator for the prediction of seafood safety and fish health (i.e., the Se:Hg molar ratio and not the Hg concentration alone) prior to the displaying of pathological symptoms. We hope that this work can serve as a first step for further investigations to confirm and validate the present results prior to their potential implementation in practical settings.

Reducing the number of individuals to monitor shoaling fish systems - Application of the Shannon entropy to construct a biological warning system model

The present study aims at identifying the lowest number of fish (European seabass) that could be used for monitoring and/or experimental purposes in small-scale fish facilities by quantifying the effect that the number of individuals has on the Shannon entropy (SE) of the trajectory followed by the shoal’s centroid. Two different experiments were performed: (i) one starting with 50 fish and decreasing to 25, 13, and 1 fish, and (ii) a second experiment starting with one fish, adding one new fish per day during 5 days, ending up with five fish in the tank. The fish were recorded for 1h daily, during which time a stochastic event (a hit in the tank) was introduced. The SE values were calculated from the images corresponding to three arbitrary basal (shoaling) periods of 3.5 min prior to the event, and to the 3.5 min period immediately after the event (schooling response). Taking both experiments together, the coefficient of variation (CV) of the SE among measurements was largest for one fish systems (CV 37.12 and 17.94% for the daily average basal and response SE, respectively) and decreased concomitantly with the number of fish (CV 8.6–10% for the basal SE of 2 to 5 fish systems and 5.86, 2.69, and 2.31% for the basal SE of 13, 25, and 50 fish, respectively). The SE of the systems kept a power relationship with the number of fish (basal: R2= 0.93 and response: R2= 0.92). Thus, 5–13 individuals should be the lowest number for a compromise between acceptable variability (<10%) in the data and reduction in the number of fish. We believe this to be the first scientific work made to estimate the minimum number of individuals to be used in subsequent experimental (including behavioral) studies using shoaling fish species that reaches a compromise between the reduction in number demanded by animal welfare guidelines and a low variability in the fish system’s response.

Evolution of Shannon entropy in a fish system (European seabass, Dicentrarchus labrax) during the recuperation period after exposure to methylmercury

The development of non-invasive methods for fish welfare and contaminant monitoring is of high relevance for the production of high quality and safe farmed fish. We have recently shown the suitability of the Shannon entropy (SE) in a commercially relevant fish system (European seabass, Dicentrarchus labrax) to differentiate methylmercury (MeHg) treated fish from non-treated fish. The present work examines the evolution of the SE of a European seabass system during an 11 days recuperation period immediately after a 2 weeks treatment with 4 μg MeHg/L (Case C2) and compares it to that of a control group not treated with MeHg (Case 1). While the SE of the C1 increased during the recuperation period, that of C2 showed erratic responses with a very modest decreasing trend.

Discrimination of contaminated fish responses by fractal dimension and entropy algorithms

The aim of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the center of the fish group as a response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm. The Fractal Dimension (FD) and the Entropy of the trajectory followed by the centroids of the groups of fish were calculated using Katz, Higuchi and Katz-Castiglionís FD and the Shannon Entropy algorithms respectively. The methodology was tested on three single cases European sea bass, two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish in methylmercury contaminated water). Katz-Castiglioni and Shannon entropy were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses.