Components of Soft Computing for Epileptic Seizure Prediction and Detection

Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. They deal with imprecision and good tolerance of uncertainty. Components of soft computing are needed for developing automated expert systems. These systems reduce human interventions so as to complete a task essentially. Automated expert systems are developed in order to perform difficult jobs. The systems have been trained and tested using soft computing techniques. These systems are required in all kinds of fields and are especially very useful in medical diagnosis. This chapter describes the components of soft computing and review of some analyses regarding EEG signal classification. From those analyses, this chapter concludes that a number of features extracted are very important and relevant features for classifier can give better accuracy of classification. The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. Further, the decomposition of EEG signal at level 4 is sufficient for seizure detection

Enhancing Precision Medicine: A Big Data-Driven Approach for the Management of Genomic Data

[EN] The management of the exponential growth of data that Next Generation Sequencing techniques produce has become a challenge for researchers that are forced to delve into an ocean of complex data in order to extract new insights to unravel the secrets of human diseases. Initially, this can be faced as a Big Data-related problem, but the genomic data have particular and relevant challenges that make them different from other Big Data working domains. Genomic data are much more heterogeneous; they are spread in hundreds of repositories, represented in multiple formats, and have different levels of quality. In addition, getting meaningful conclusions from genomic data requires considering all of the relevant surrounding knowledge that is under continuous evolution. In this scenario, the precise identification of what makes Genome Data Management so different is essential in order to provide effective Big Data-based solutions. Genomic projects require dealing with the technological problems associated with data management, nomenclature standards, and quality issues that only robust Information Systems that use Big Data techniques can provide. The main contribution of this paper is to present a Big Data-driven approach for managing genomic data, that is adapted to the particularities of the domain and to show its applicability to improve genetic diagnoses, which is the core of the development of accurate Precision Medicine.This work was supported by the Spanish State Research Agency (grant number TIN2016-80811-P) and the Generalitat Valenciana (grant number PROMETEO/2018/176), and co-financed with ERDF.León-Palacio, A.; Pastor López, O. (2021). Enhancing Precision Medicine: A Big Data-Driven Approach for the Management of Genomic Data. Big Data Research. 26:1-11. https://doi.org/10.1016/j.bdr.2021.100253S1112

IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering

Imeväisiän epilepsiaa sairastavien imeväisten katsekäyttäytyminen ja sen yhteys myöhempään neurokognitiiviseen kehitykseen

Katsekäyttäytymisen analysointia käytetään nykypäivänä yleisesti auttamaan erilaisten neurologisten sairauksien diagnosoinnissa ja poissulkemisessa sekä auttamaan tutkijoita ymmärtämään paremmin kognitiota aikaisissa elämänvaiheissa. Sen käyttöä imeväisiän epilepsiaa sairastavien lapsien kehityksen arvioinnissa ja seurannassa ei kuitenkaan ole vielä tutkittu perusteellisesti. Siksi tämän tutkimuksen tavoitteena oli tutkia imeväisiän epilepsiaa sairastavien imeväisten katsekäyttäytymisen yhteyttä heidän myöhemmin toteutuneeseen hermoston kehitykseen. Yhteyden ja sen ennustekyvyn tutkimiseksi luotiin kolme mallia. Kuusikymmentäkolme lasta, joiden epileptiset kohtaukset alkoivat ennen 12 kuukauden ikää, osallistuivat tutkimukseen vanhempien vapaaehtoisella suostumuksella. Imeväisten katsekäyttäyminen nauhoitettiin Tobii-Pro-X3-120:lla kahdessa mittauspisteessä. Tulokset osoittivat, että imeväisten alkuperäinen kyky kiinnittää katse, muutokset katseen siirtämisen todennäköisyydessä ensimmäisen 12 elinkuukauden aikana sekä rakenteellinen etiologia olivat merkittävästi yhteydessä imeväisten kehitystulokseen 24 kuukauden iässä. Siinä missä rakenteellinen etiologia liittyi merkitsevästi huonompaan kehitystulokseen, hyvä alkuperäinen katseenkiinnittämiskyky ja katseen siirron todennäköisyyden paraneminen ensimmäisen elinvuoden aikana olivat yhteydessä merkittävästi positiivisempaan tulokseen. Nämä havainnot viittaavat siihen, että katsekäyttäytyminen varhaisessa iässä on olennaista myöhemmän kehityksen kannalta imeväisille, jotka sairastavat imeväisiän epilepsiaa. Näin ollen katseenseuranta voisi tarjota keinoja arvioida imeväisiän epilepsiaa sairastavien imeväisten myöhemmin toteutuvia neurokognitiivisia tuloksia jo varhaisessa iässä.The analysis of gaze behaviour is nowadays commonly employed to help with the diagnosis and exclusion of differential neurological conditions as well as to help researchers better understand cognition in the early stages of life. However, its application in the developmental evaluation and follow-up of children with early-onset epilepsy has not been profoundly studied yet. Therefore, the current study aimed to investigate the association between the gaze behaviour of infants with early-onset epilepsy and their future neurodevelopmental outcome. To study the association and its predictive ability, three models were created. Sixty-three infants with epileptic seizure onset before 12 months of age participated in the study with the voluntary consent of their parents. Infants’ gaze behaviour was recorded with Tobii Pro-X3-120 at two measure points. The results showed infants’ initial ability to fixate their gaze, changes in their gaze shift probability in the first 12 months of life, and structural aetiology to be significantly associated with the infants' developmental outcome at 24 months of age. Where the structural aetiology was significantly associated with poorer developmental outcome, good initial fixation ability and improvements in the infants’ gaze shift probability during their first year of life were significantly associated with more positive outcome. These findings suggest that gaze behaviour at an early age is an essential predictor of later development in infants with early-onset epilepsy. Hence, eye-tracking could provide means to evaluate the later neurocognitive outcome of infants with early-onset epilepsy at an early age

A Review on Machine Learning Techniques for Neurological Disorders Estimation by Analyzing EEG Waves

With the fast improvement of neuroimaging data acquisition strategies, there has been a significant growth in learning neurological disorders among data mining and machine learning communities. Neurological disorders are the ones that impact the central nervous system (including the human brain) and also include over 600 disorders ranging from brain aneurysm to epilepsy. Every year, based on World Health Organization (WHO), neurological disorders affect much more than one billion people worldwide and count for up to seven million deaths. Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders. With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification. Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject