Epileptic Seizure Detection And Prediction From Electroencephalogram Using Neuro-Fuzzy Algorithms

This dissertation presents innovative approaches based on fuzzy logic in epileptic seizure detection and prediction from Electroencephalogram (EEG). The fuzzy rule-based algorithms were developed with the aim to improve quality of life of epilepsy patients by utilizing intelligent methods. An adaptive fuzzy logic system was developed to detect seizure onset in a patient specific way. Fuzzy if-then rules were developed to mimic the human reasoning and taking advantage of the combination in spatial-temporal domain. Fuzzy c-means clustering technique was utilized for optimizing the membership functions for varying patterns in the feature domain. In addition, application of the adaptive neuro-fuzzy inference system (ANFIS) is presented for efficient classification of several commonly arising artifacts from EEG. Finally, we present a neuro-fuzzy approach of seizure prediction by applying the ANFIS. Patient specific ANFIS classifier was constructed to forecast a seizure followed by postprocessing methods. Three nonlinear seizure predictive features were used to characterize changes prior to seizure. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. The ANFIS classifier was constructed based on these features as inputs. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. In this dissertation, the application of the neuro-fuzzy algorithms in epilepsy diagnosis and treatment was demonstrated by applying the methods on different datasets. Several performance measures such as detection delay, sensitivity and specificity were calculated and compared with results reported in literature. The proposed algorithms have potentials to be used in diagnostics and therapeutic applications as they can be implemented in an implantable medical device to detect a seizure, forecast a seizure, and initiate neurostimulation therapy for the purpose of seizure prevention or abortion

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems

Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy

C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö

Decision fusion in healthcare and medicine : a narrative review

Objective: To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. Background: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. Methods: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. Conclusions: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition