Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform

Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important physiological information about the activity of the human brain which can be used to diagnose epilepsy. However, visual inspection of a large number of electroencephalogram signals is very time-consuming and can often lead to inconsistencies in physicians' diagnoses. Quantification of abnormalities in brain signals can indicate brain conditions and pathology so the electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. In this article, an attempt has been made to create a single instruction for diagnosing epilepsy, which consists of two steps. In the first step, a low-pass filter was used to preprocess the data and three separate mid-pass filters for different frequency bands and a multilayer neural network were designed. In the second step, the wavelet transform technique was used to process data. In particular, this paper proposes a multilayer perceptron neural network classifier for the diagnosis of epilepsy, that requires normal data and epilepsy data for education, but this classifier can recognize normal disorders, epilepsy, and even other disorders taught in educational examples. Also, the value of using electroencephalogram signal has been evaluated in two ways: using wavelet transform and non-using wavelet transform. Finally, the evaluation results indicate a relatively uniform impact factor on the use or non-use of wavelet transform on the improvement of epilepsy data functions, but in the end, it was shown that the use of perceptron multilayer neural network can provide a higher accuracy coefficient for experts.Comment: 8 pages, 4 tables, 3 figure

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

The object of this study is fuzzy cognitive modeling as a means of studying semistructured socio-economic systems. The features of constructing cognitive maps, providing the ability to choose management decisions in complex semistructured socio-economic systems, are described. It is shown that further improvement of technologies necessary for developing decision support systems and their practical use is still relevant. This work aimed to improve the accuracy of cognitive modeling of semistructured systems based on a fuzzy cognitive map of structuring nonformalized situations (MSNS) with the evaluation of root-mean-square error (RMSE) and mean average squared error (MASE) coefficients. In order to achieve the goal, the following main methods were used: systems analysis methods, fuzzy logic and fuzzy sets theory postulates, theory of integral wavelet transform, correlation and autocorrelation analyses. As a result, a new methodology for constructing MSNS was proposed—a map of structuring nonformalized situations that combines the positive properties of previous fuzzy cognitive maps. The solution of modeling problems based on this methodology should increase the reliability and quality of analysis and modeling of semistructured systems and processes under uncertainty. The analysis using open datasets proved that compared to the classical ARIMA, SVR, MLP, and Fuzzy time series models, our proposed model provides better performance in terms of MASE and RMSE metrics, which confirms its advantage. Thus, it is advisable to use our proposed algorithm in the future as a mathematical basis for developing software tools for the analysis and modeling of problems in semistructured systems and processes. Doi: 10.28991/ESJ-2022-06-02-012 Full Text: PD

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Financiado para publicación en acceso aberto: Universidad de Granada / CBUA.[Abstract]: Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes. This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovacón y Universidades”), P18-RT-1624, UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”. The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”. The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019. Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00. José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC (ED431G 2019/01), GPC ED431B 2022/33, and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). In [247], the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain). In [248], the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4, 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies”BG05M2OP001-1.002-0023. The work reported here has been partially funded by the support of MICIN project PID2020-116346GB-I00. The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III; by MinCiencias project 1222-852-69927, contract 495-2020. The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb. This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058. S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)).Junta de Andalucía; CV20-45250Junta de Andalucía; A-TIC-080-UGR18Junta de Andalucía; B-TIC-586-UGR20Junta de Andalucía; P20-00525Junta de Andalucía; P18-RT-1624Junta de Andalucía; UMA20-FEDERJA-086Portugal. Fundação para a Ciência e a Tecnologia; DSAIPA/AI/0099/2019Xunta de Galicia; ED431G 2019/01Xunta de Galicia; GPC ED431B 2022/33Chile. Agencia Nacional de Investigación y Desarrollo; 1201572Generalitat Valenciana; PROMETEO/2019/119Bulgarian National Science Fund; KP-06-N42/4Bulgaria. Operational Programme Science and Education for Smart Growth; BG05M2OP001-1.002-0023Colombia. Ministerio de Ciencia, Tecnología e Innovación; 1222-852-69927Junta de Andalucía; UMA18-FEDERJA-084Suíza. State Secretariat for Education, Research and Innovation; 22 00058Institute of Information & Communications Technology Planning & Evaluation (Corea del Sur); 2020-0-0136

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

A novel automated autism spectrum disorder detection system

Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines