Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Improvement of alzheimer disease diagnosis accuracy using ensemble methods

Nowadays, there is a significant increase in the medical data that we should take advantage of that. The application of the machine learning via the data mining processes, such as data classification depends on using a single classification algorithm or those complained as ensemble models. The objective of this work is to improve the classification accuracy of previous results for Alzheimer disease diagnosing. The Decision Tree algorithm with three types of ensemble methods combined, which are Boosting, Bagging and Stacking. The clinical dataset from the Open Access Series of Imaging Studies (OASIS) was used in the experiments. The experimental results of the proposed approach were better than the previous work results. Where the Random Forest (Bagging) achieved the highest accuracy among all algorithms with 90.69%, while the lowest one was Stacking with 79.07%. All these results generated in this paper are higher in accuracy than that done before

Applications of Deep Learning in Financial Intermediation: A Systematic Literature Review

Abstract In finance, an infinite amount of datais generated daily, which is important for decision-making in the business world. Consequently, there is a need to create models that help to process and interpret this data. Deep learning has demonstrated important advances in the processing of large amounts of data, and for this reason, the objective of this systematic review of literature corresponds to the search for applications, deep learning model and techniques that were used to solve problems in the financial area. For this purpose, out of 346 articles found, 20 were selected that met the inclusion and exclusion criteria corresponding to the research questions. Among the most common applications, models, and techniques were: prediction in market actions, sales forecasting, detection of fraud risks and tax evasion; with respect to the models, convolutional neural networks CNN and recurrent neural networks RNN were among the most executed; the ReLu and Sigmoid techniques turned out to be the most used in these models.   Keywords: deep learning, finance, machine learning, Convolutional Neural Network CNN, Recurrent Neural Network RN

An exploration of epileptic and nonepileptic seizures : an interpretative phenomenological analytic study

Background Differentiating epileptic seizures from non epileptic seizures (NES) has always been difficult. Seizures can look very similar, substantial physical injury and incontinence can occur in both conditions and people can have both conditions simultaneously. Treatment for each condition is very different however, epilepsy needing anti epileptic medication whereas NES is a psychologically rooted condition. Aims To develop previous work To document a number of detailed seizures descriptions and to analyse these using Interpretative Phenomenological Analysis (IPA) To identify linguistic markers to differentiate NES from epilepsy Methodology This project used IPA as a more expansive method of 'history taking' being completely patient led. The approach and its theoretical antecedents have been described in depth in the thesis. Four newly referred patients with uncertain diagnoses were interviewed once, three twice. There was additional, contextual data. Results The interpretation illustrated that subjective seizure experiences using IPA can contribute to previous work: It heralded the potential beginnings of the development of an alternative 'seizure discourse' for lay and professionals. It had the potential to contribute to patient information material and a screening tool. It offered new ideas for clinical practice and research. Discussion As an approach, IPA has the potential to combine its findings with those in the field of neurophenomenology in terms of expanding knowledge of corresponding subjective experiences. Conclusions Given that subjective experiences of people can help locate seizure foci, IPA has the potential for establishing itself as a qualitative scientific research approach in the area of seizure experiences

EEG-fMRI in epilepsy and sleep

This thesis used simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) to investigate both epilepsy and sleep. Initially, EEG-fMRI was used in a cohort of patients with complex epilepsy referred from a tertiary epilepsy clinic for both pre-surgical evaluation and diagnostic reasons. The results suggest a limited utility of EEG-fMRI in the epilepsy clinic with a very complex patient group. Following on, investigation of early blood oxygen level dependent (BOLD) signal changes in a group of patients with focal epilepsy demonstrated potentially meaningful BOLD changes occurring six seconds prior to interictal epileptiform discharges, and modelling less than this six seconds can result in overlap of the haemodynamic response function used to model BOLD changes. The same analysis was used to model endogenously occurring sleep paroxysms; K-complexes (KCs), vertex sharp waves (VSWs) and sleep spindles (SSs), finding early BOLD signal changes with SSs in group data. Finally, KCs and VSWs were investigated in more detail in a group of participants under both sleep deprived and non-deprived conditions, demonstrating an increase in overall activation for both KCs and VSWs following sleep deprivation. Overall, we find early BOLD changes are not restricted to pathological events and sleep deprivation can enhance BOLD responses

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems