Parkinsons Disease Detection by using Isosurfaces with Convolutional Neural Networks

Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson’s disease. The ultimate goal would be detec- tion by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contains a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades be- cause the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to im- plement a classification system which uses two of the most well-known CNN architectures to classify DaTScan images with an average accuracy of 95.1% and AUC=97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computa- tional burden.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Multimodal image data fusion for Alzheimer’s Disease diagnosis by Sparse Representation

Alzheimer's Diasese (AD) diagnosis can be carried out by analysing functional or structural changes in the brain. Functional changes associated to neurological disorders can be figured out by positron emission tomography (PET) as it allows to study the activation of certain areas of the brain during specific task development. On the other hand, neurological disorders can also be discovered by analysing structural changes in the brain which are usually assessed by Magnetic Resonance Imaging (MRI). In fact, computer-aided diagnosis tools (CAD) that have been recently devised for the diagnosis of neurological disorders use functional or structural data. However, functional and structural data can be fused out in order to improve the accuracy and to diminish the false positive rate in CAD tools. In this paper we present a method for the diagnosis of AD which fuses multimodal image (PET and MRI) data by combining Sparse Representation Classifiers (SRC). The method presented in this work shows accuracy values up to 95% and clearly outperforms the classification outcomes obtained using single-modality images.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Secure Distributed System inspired by Ant Colonies for Road Traffic Management in Emergency Situations

We have proposed an algorithm, based on ant colonies, for road traffic management. The implementation of the algorithm does not rely on fixed infrastructures in order to operate in emergency situations. It only uses the VANET V2V communications and location systems that do not require contact with a fixed infrastructure. The algorithm uses signature aggregation and reputation lists to ensure system security. Furthermore, the algorithm has an implicit security that minimizes the risks in case of attacks. A scale prototype has been designed and implemented to validate the algorithm using RFID location system.In this work, we present a distributed system designed for road traffic management. The system is inspired by the behavior of the ant colonies. The distributed design responds to the particular limitations of an emergency situation; mainly, the fixed infrastructures are out of service because no energy supply is available. The implementation is based on the VANET facilities complemented with passive RFID tags or GPS localization. The vehicles can use the information of previous vehicles to dynamically decide the best path. A scale prototype has been developed to validate the system. It consists of several small size robotic vehicles, a test road circuit and a visual monitorization system. The security of the system is provided by a combination of data aggregation and reputation lists.Proyecto TIN 2011-25452 (TUERI: Technologies for secUre and Efficient wiReless networks within the Internet of things with applications in transport and logistic). Y Universidad de Málaga-Campus de Excelencia Internacional Andalucia Tech

PET image classification using HHT-based features through fractal sampling

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain rep- resentative features from the images, play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is spe- cially important in the early diagnosis of dementias. In this work we present a technique that allows extracting discriminative features from Positron Emission Tomography (PET) by means of an Empirical Mode Decomposition-based (EEMD) method. This requires to transform the 3D PET image into a time series which is addressed by sampling the image using a fractal-based method which allows to preserve the spa- tial relationship among voxels. The devised technique has been used to classify images from the Alzheimer's Disease Neuroimaging Initiat- ive (ADNI) achieving up to a 90.5% accuracy in a differential diagnosis task (AD vs. controls), which proves that the information retrieved by our methodology is significantly linked to the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Inter-channel Granger Causality for Estimating EEG Phase Connectivity Patterns in Dyslexia

Methods like Electroencephalography (EEG) and magnetoencephalogram (MEG) record brain oscillations and provide an invaluable insight into healthy and pathological brain function. These signals are helpful to study and achieve an objective and early diagnosis of neural disorders as Developmental Dyslexia (DD). An atypical oscillatory sampling could cause the characteristic phonological difficulties of dyslexia at one or more temporal rates; in this sense, measuring the EEG signal can help to make an early diagnosis of DD. The LEEDUCA study conducted a series of EEG experiments on children listening to amplitude modulated (AM) noise with slow-rhythmic prosodic (0.5–1 Hz) to detect differences in perception of oscillatory ampling that could be associated with dyslexia. The evolution of each EEG channel has been studied in the frequency domain, obtaining the analytical phase using the Hilbert transform. Subsequently, the cause-effect relationships between channels in ach subject have been reflected thanks to Granger causality, obtaining matrices that reflect the interaction between the different parts of the brain. Hence, each subject was classified as belonging or not to the control group or the experimental group. For this purpose, two ensemble classification algorithms were compared, showing that both can reach acceptable classifying erformance in delta band with an accuracy up to 0.77, recall of 0.91 and AUC of 0.97 using Gradient Boosting classifier.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

EEG Interchannel Causality to Identify Source/Sink Phase Connectivity Patterns in Developmental Dyslexia

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and bandlimited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels’ activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the stablished right-lateralized Theta sampling network anomaly, in line with the assumption of the temporal sampling framework of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively

Desafío tecnológico: herramienta para trabajar y evaluar las competencias básicas y generales en los estudios de grado de la E.T.S.I. de Telecomunicación

This work presents the evolution of the five editions of the educational activity named “Technological Challenge” specially focused on the students at “Escuela Técnica Superior de Ingeniería de Telecomunicación” (ETSIT), but open to all students of the “Universidad de Málaga” (UMA). This initiative has been developed in the context of the educational innovation project PIE17-021 funded by UMA. The “Technological Challenge” consists on the formulation of specific real problems, which students must face in a competitive regime. This activity allows the reinforcement and evaluation of basic and general competences reached by the graduate students in the ETSIT. After nearly five years, this paper describes the evaluation of the results, regarding interest and participation of the students in the “Technology Challenge” along with the basic and general competences reached by the students.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. PIE17-02

Complex network modelling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis

Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain’s structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the informa- tion. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from electroencephalography (EEG) data, while different auditory stim- uli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between control and dyslexic sub- jects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for control and dyslexic subjects. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small- world topology. Finally, these graph-based features are used to classify between control and dyslexic subjects by means of a Support Vector Machine (SVM).This work was supported by projects PGC2018-098813-B-C32 (Spanish “Ministerio de Cien- cia, Innovación y Universidades”), UMA20-FEDERJA-086 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). We gratefully ac- knowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Incorpo- ración” Fellowship. We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support. Funding for open access charge: Universidad de Málaga / CBU

Chlorophyll fluorescence and its relationship with physiological stress in Chenopodium quinoa Willd.

Photosynthetic activity is a fundamental process in the physiology of plants, and its regulation plays an important role in determining the effect of abiotic factors. Quinoa is a plant species of agronomic and nutritional interest that has been recognized for its adaptability to extreme environmental conditions, however, climate change may result in unfavorable conditions capable of affecting the natural development of this species, which is of great interest culture and research in South America. To evaluate the response of quinoa to stress, techniques could be used that quantify the loss of light energy through its dissipation in the form of heat. However, the measurement of chlorophyll fluorescence is the most widely used and accessible technique for field research, which allows to recognize the relationships between the plant and agroclimatic factors. This review summarizes the physiological effects of heat, radiation, salinity, and nutrient and water availability, as well as their possible interactions on quinoa

Temporal phase synchrony disruption in dyslexia: anomaly patterns in auditory processing

The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proportionate a correct analysis and avoid these types of problems. It is in this task, biomarkers like electroencephalograms can help to obtain an objective measurement of the brain behavior that can be used to perform several analyses and ultimately making a diagnosis, keeping the human interaction at minimum. In this work, we used recorded electroencephalograms of children with and without dyslexia while a sound stimulus is played. We aim to detect whether there are significant differences in adaptation when the same stimulus is applied at different times. Our results show that following this process, a machine learning pipeline can be built with AUC values up to 0.73.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech