EEG Data Mining Using PCA

EEG Data Mining Using PCA

Lenka Lhotská (Czech Technical University in Prague, Czech Republic), Vladimír Krajca (Faculty Hospital Na Bulovce, Czech Republic), Jitka Mohylová (Technical University Ostrava, Czech Republic), Svojmil Petránek (Faculty Hospital Na Bulovce, Czech Republic) and Václav Gerla (Czech Technical University in Prague, Czech Republic)
DOI: 10.4018/978-1-60566-218-3.ch008
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Abstract

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.
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Electroencephalogram

An electroencephalogram (EEG) is a recording of spontaneous brain electrical activity by means of electrodes located on the scalp. The placing of the electrodes is constrained by natural physical limits, namely by the size of the electrodes, which limits the maximum number of electrodes that can be used. Another limitation is the mutual influence of electrodes located close to each other. Standardized placement of the basic number of electrodes is done in accordance with the scheme designed by Dr. Jasper (Jasper, 1958). This is nowadays known as the International 10-20 system.

In the frequency domain we can distinguish four basic frequency bands on an EEG signal, namely delta, theta, alpha, and beta activities.

The delta band corresponds to the slowest waves in the range of 0-4 Hz. Its appearance is always pathological in an adult in the waking state. The pathological significance increases with increasing amplitude and localization. The existence of a delta wave is normal for children up to three years of age, in deep sleep and hypnosis. During sleep the waves can be higher than 100 µV in amplitude.

The theta band corresponds to waves in the range of 4-8 Hz. Their existence is considered as pathological if their amplitude is at least twice as high as the alpha activity or higher than 30 µV if alpha activity is absent. The presence of a theta wave is normal if its amplitude is up to 15 µV and if the waves appear symmetrically. In healthy persons they appear in central, temporal and parietal parts. This activity is characteristic for certain periods of sleep.

The alpha band corresponds to waves in the range of 8-13 Hz. In the waking state in mental and physical rest the maximum appears in the occipital part of the brain. Its presence is highly influenced by open or closed eyes. The amplitude is in the range of 20-100 µV, most frequently around 50 µV.

The beta band corresponds to the fastest waves in the range of 13-20 Hz. The maximum of the activity is mostly localized in the frontal part, and it decreases in the backward direction. The rhythm is mostly symmetrical or nearly symmetrical in the central part. The amplitude is up to 30 µV. The activity is characteristic for concentration, logical reasoning and feelings of anger and anxiety.

An EEG contains a great deal of information about the state of a patient’s health. It has the advantage of being non-invasive and applicable over a comparatively long time span (up to 24 hours, if necessary). This is an important feature in cases where we want to follow disorders that are not permanently present but appear incidentally (e.g., epileptic seizures) or under certain conditions (various sleep disorders) (Daube, 2002).

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Table of Contents
Foreword
Riccardo Bellazzi
Preface
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Acknowledgment
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Chapter 1
Jana Zvárová, Arnošt Veselý
This chapter introduces the basic concepts of medical informatics: data, information, and knowledge. Data are classified into various types and... Sample PDF
Data, Information and Knowledge
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Chapter 2
Michel Simonet, Radja Messai, Gayo Diallo
Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status... Sample PDF
Ontologies in the Health Field
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Chapter 3
Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to... Sample PDF
Cost-Sensitive Learning in Medicine
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Chapter 4
Arnošt Veselý
This chapter deals with applications of artificial neural networks in classification and regression problems. Based on theoretical analysis it... Sample PDF
Classification and Prediction with Neural Networks
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Chapter 5
Patrik Eklund, Lena Kallin Westin
Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data... Sample PDF
Preprocessing Perceptrons and Multivariate Decision Limits
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Chapter 6
Xiu Ying Wang, Dagan Feng
The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new... Sample PDF
Image Registration for Biomedical Information Integration
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Chapter 7
ECG Processing  (pages 137-160)
Lenka Lhotská, Václav Chudácek, Michal Huptych
This chapter describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG)... Sample PDF
ECG Processing
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Chapter 8
EEG Data Mining Using PCA  (pages 161-180)
Lenka Lhotská, Vladimír Krajca, Jitka Mohylová, Svojmil Petránek, Václav Gerla
This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing.... Sample PDF
EEG Data Mining Using PCA
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Chapter 9
Darryl N. Davis, Thuy T.T. Nguyen
Risk prediction models are of great interest to clinicians. They offer an explicit and repeatable means to aide the selection, from a general... Sample PDF
Generating and Verifying Risk Prediction Models using Data Mining
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Chapter 10
Vangelis Karkaletsis, Konstantinos Stamatakis, Karampiperis, Karampiperis, Pythagoras Karampiperis, Pythagoras Karampiperis
The World Wide Web is an important channel of information exchange in many domains, including the medical one. The ever increasing amount of freely... Sample PDF
Management of Medical Website Quality Labels via Web Mining
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Chapter 11
Rainer Schmidt
In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal... Sample PDF
Two Case-Based Systems for Explaining Exceptions in Medicine
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Chapter 12
Bruno Crémilleux, Arnaud Soulet, Jiri Kléma, Céline Hébert, Olivier Gandrillon
The discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on... Sample PDF
Discovering Knowledge from Local Patterns in SAGE Data
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Chapter 13
Jirí Kléma, Filip Železný, Igor Trajkovski, Filip Karel, Bruno Crémilleux
This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several... Sample PDF
Gene Expression Mining Guided by Background Knowledge
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Chapter 14
Pamela L. Thompson, Xin Zhang, Wenxin Jiang, Zbigniew W. Ras, Pawel Jastreboff
This chapter describes the process used to mine a database containing data, related to patient visits during Tinnitus Retraining Therapy. The... Sample PDF
Mining Tinnitus Database for Knowledge
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Chapter 15
Dinora A. Morales, Endika Bengoetxea, Pedro Larrañaga
Infertility is currently considered an important social problem that has been subject to special interest by medical doctors and biologists. Due to... Sample PDF
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Chapter 16
Mining Tuberculosis Data  (pages 332-349)
Marisa A. Sánchez, Sonia Uremovich, Pablo Acrogliano
This chapter reviews the current policies of tuberculosis control programs for the diagnosis of tuberculosis. The international standard for... Sample PDF
Mining Tuberculosis Data
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Chapter 17
Mila Kwiatkowska, M. Stella Atkins, Les Matthews, Najib T. Ayas, C. Frank Ryan
This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules.... Sample PDF
Knowledge-Based Induction of Clinical Prediction Rules
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Chapter 18
Petr Berka, Jan Rauch, Marie Tomecková
The aim of this chapter is to describe goals, current results, and further plans of long-time activity concerning application of data mining and... Sample PDF
Data Mining in Atherosclerosis Risk Factor Data
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About the Contributors