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KNN Classifier and K-Means Clustering for Robust Classification of Epilepsy from EEG Signals. A Detailed Analysis

Knn Classifier and K-Means Clustering Fo;

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Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals

Comprehensive Analysis of Extreme Learni is een boek van Harikumar Rajaguru;

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Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals

Comprehensive Analysis of Swarm Based Cl is een boek van Harikumar Rajaguru;

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Development of Two Hybrid Classification Methods for Machine Learning

Maximization Based Classification Method." A hybrid method is formed by using k nearest neighbor (KNN), Bayesian methods and genetic algorithm (GA;

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Performance Analysis of Epilepsy Risk Level From EEG Signals

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EEG-Based Diagnosis of Alzheimer Disease

EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques provides a;

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Brain Seizure Detection and Classification Using EEG Signals

classifiers for the classification of EEG signals in normal and epileptic EEG signals. In addition, the authors also present new methods of feature;

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EEG Signal Analysis and Classification

the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from;

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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final;

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Graph-theoretic Techniques For Web Content Mining

clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization;

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The Importance of Photosensitivity for Epilepsy

. Informative case histories with EEG examples and a helpful glossary are included. In epilepsy, the term photosensitivity is used both for;

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Epilepsy Board Review

diagnosis, classification and treatment are thoroughly covered, along with seizure classification, epidemiology, normal and abnormal EEG, and;

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Biomedical Signal Processing for Healthcare Applications

-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases;

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Understanding Epilepsy

readers in understanding content. A valuable book for medical trainees, neurologists, neurosurgeons, nurses and EEG technologists, and essential;

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Analysis and Classification of EEG Signals for Brain-Computer Interfaces

This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations;

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Analysis and Classification of EEG Signals for Brain Computer Interfaces

This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations;

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Introduction to EEG- and Speech-Based Emotion Recognition

record EEGs. Finally, the authors include information on EEG-based emotion recognition, classification, and a proposed EEG/speech fusion method;

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Brain Signals

of electromagnetic fields by neuronal activity, and discusses the electromagnetic forward problem. The authors then turn to EEG and MEG analysis, offering a;

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EMG Signals Characterization in Three States of Contraction by Fuzzy Network and

brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The;

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Epileptic Seizures and the EEG

epileptic brain. It introduces the EEG as a measured signal and explains its use in the study of epilepsy. Next, it provides an explanation of the;

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Fuzzy Sets & their Application to Clustering & Training

and classification methods, and a general method for obtaining a variety of fuzzy clustering algorithms. The authors - top experts from;

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The Role of EEG in the Diagnosis and Classification of the Epilepsy Syndromes

• Dimensions du produit: 24 x 1,3 x 17 cm;...

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Practical Epilepsy

of clinical epilepsy. It contains core information for professionals who wish gain a breadth and depth of knowledge about epilepsy in a shorter amount;

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Clustering Methodology for Symbolic Data

in order to extract knowledge from such a data. Classification Methodology for Symbolic Data: * Provides new classification methodologies for;

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EEG-Based Brain-Computer Interfaces

EEG-Based Brain-Computer Interface: Cognitive Analysis and Control Applications provides a technical approach to using brain signals for;

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A Practical Approach to Stereo EEG

stereoelectroencephalography written for trainees, physicians, and technologists involved in invasive EEG evaluation and monitoring. This go-to resource provides a;

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Unsupervised Machine Learning for Clustering in Political and Social Research

detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering;

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