Kenneth Revett | Champlain College (original) (raw)

Kenneth Revett

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Papers by Kenneth Revett

Research paper thumbnail of EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Research paper thumbnail of Data Mining an EEG Dataset With an Emphasis on Dimensionality Reduction

Research paper thumbnail of A rule based approach to classification of EEG datasets: A comparison between ANFIS and rough sets

Research paper thumbnail of On the Deployment of Artificial Immune Systems for Biometrics

Research paper thumbnail of A machine learning approach to artefact extraction from independent components derived from EEG datasets

Artefact selection from EEG datasets is still a task remanded to domain experts. In this study, a... more Artefact selection from EEG datasets is still a task remanded to domain experts. In this study, a working memory task dataset is used to examine automated methods for artefact removal. Such artefacts include eye blinks, muscle movements, and blood flow changes that do not reflect actual physiological responses to presented stimuli. In this work, a set of attributes were extracted from a 33 channel EEG recording. The attributes related predominantly to the independent components that were generated from the epoched data. In conjunction with expert analysis of the components, the attributes were used to produce an automated component artefact removal system. The result is an artefact removal system that performs at essentially 94% accuracy.

Research paper thumbnail of Machine learning techniques in electrocardiogram diagnosis

Advances in the area of computer sciences algorithms and artificial intelligence-based machine le... more Advances in the area of computer sciences algorithms and artificial intelligence-based machine learning techniques have greatly enhanced the electrocardiogram (ECG) signal classification and contributed to correct diagnosis. Recently, machine learning techniques have proved to be useful in electrocardiogram (ECG) diagnosis. This paper presents and discusses the current machine learning techniques and approaches used in ECG signal classification.

Research paper thumbnail of A rule-based approach to processing SPECT imaging for the diagnosis of heart disease

Research paper thumbnail of The Future of Behavioral Biometrics

John Wiley & Sons, Ltd eBooks, Nov 24, 2008

Research paper thumbnail of A Rough Sets Based Classifier for Primary Biliary Cirrhosis

Research paper thumbnail of Cognitive Biometrics

IGI Global eBooks, Oct 4, 2011

Research paper thumbnail of User Authentication via Keystroke Dynamics: An Artificial Immune System Based Approach

Research paper thumbnail of On the Use of Rough Sets for Artefact Extraction from EEG Datasets

High-density electroencephalography produces large volumes of data. The analysis of EEG data is c... more High-density electroencephalography produces large volumes of data. The analysis of EEG data is confounded by the existed of a number of different artefacts such as eye blink and, muscle movement which impede the analysis of the data. Typically, artefacts are removed by visual inspection - an arduous task for high-density recordings. In addition, different researchers use Consistency across different laboratories is often difficult, and in addition, the task has to be repeated for each study. An automated method for artefact identification and removal would be a very useful tool for data processing in this domain. In this study, rough sets is employed as a means of automating artefact identification and removal within the context of EEG analysis using the EEGLAB analysis system. The results from this preliminary study indicate that artefacts can be identified and removed with approximately 85% accuracy.

Research paper thumbnail of Classification using adaptive fuzzy inference neural networks

ACTA Press eBooks, 2008

The architecture and learning scheme of a novel fuzzy logic system implemented in the framework o... more The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Both error backpropagation and recursive least squares estimation, are applied to the learning scheme. The convergence of training is faster because the least-squares algorithm is applied to the estimation of consequence parameters of the system and backpropagation is applied only to the estimation of the premise parameters. Using the proposed scheme, high-dimensional fuzzy systems can be realized with fewer rules than a typical Takagi-Sugeno fuzzy system. A number of simulations demonstrate the performance of the proposed system.

Research paper thumbnail of Data Mining a Prostate Cancer Dataset Using Neural Networks

Research paper thumbnail of Evaluation of the Feature Space of an Erythematosquamous Dataset Using Rough Sets

Annals of the University of Craiova - Mathematics and Computer Science Series, Sep 11, 2009

Research paper thumbnail of A Hybrid Approach to MR Imaging Segmentation Using Unsupervised Clustering and Approximate Reducts

Lecture Notes in Computer Science, 2005

We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised cluste... more We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images and 89%

Research paper thumbnail of An Automated Multi-spectral MRI Segmentation Algorithm Using Approximate Reducts

Springer eBooks, 2004

We introduce an automated multi-spectral MRI segmentation technique based on approximate reducts ... more We introduce an automated multi-spectral MRI segmentation technique based on approximate reducts derived from the data mining paradigm of the theory of rough sets. We utilized the T1, T2 and PD MRI images from the Simulated Brain Database as a ”gold standard” to train and test our segmentation algorithm. The results suggest that approximate reducts, used alone or in combination

Research paper thumbnail of Behavioral Biometrics: A Remote Access Approach

Research paper thumbnail of Automatic detection Of EEG abnormalities using wavelet transformations

Research paper thumbnail of Graphical-Based Authentication Methods

John Wiley & Sons, Ltd eBooks, Nov 24, 2008

Research paper thumbnail of EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Research paper thumbnail of Data Mining an EEG Dataset With an Emphasis on Dimensionality Reduction

Research paper thumbnail of A rule based approach to classification of EEG datasets: A comparison between ANFIS and rough sets

Research paper thumbnail of On the Deployment of Artificial Immune Systems for Biometrics

Research paper thumbnail of A machine learning approach to artefact extraction from independent components derived from EEG datasets

Artefact selection from EEG datasets is still a task remanded to domain experts. In this study, a... more Artefact selection from EEG datasets is still a task remanded to domain experts. In this study, a working memory task dataset is used to examine automated methods for artefact removal. Such artefacts include eye blinks, muscle movements, and blood flow changes that do not reflect actual physiological responses to presented stimuli. In this work, a set of attributes were extracted from a 33 channel EEG recording. The attributes related predominantly to the independent components that were generated from the epoched data. In conjunction with expert analysis of the components, the attributes were used to produce an automated component artefact removal system. The result is an artefact removal system that performs at essentially 94% accuracy.

Research paper thumbnail of Machine learning techniques in electrocardiogram diagnosis

Advances in the area of computer sciences algorithms and artificial intelligence-based machine le... more Advances in the area of computer sciences algorithms and artificial intelligence-based machine learning techniques have greatly enhanced the electrocardiogram (ECG) signal classification and contributed to correct diagnosis. Recently, machine learning techniques have proved to be useful in electrocardiogram (ECG) diagnosis. This paper presents and discusses the current machine learning techniques and approaches used in ECG signal classification.

Research paper thumbnail of A rule-based approach to processing SPECT imaging for the diagnosis of heart disease

Research paper thumbnail of The Future of Behavioral Biometrics

John Wiley & Sons, Ltd eBooks, Nov 24, 2008

Research paper thumbnail of A Rough Sets Based Classifier for Primary Biliary Cirrhosis

Research paper thumbnail of Cognitive Biometrics

IGI Global eBooks, Oct 4, 2011

Research paper thumbnail of User Authentication via Keystroke Dynamics: An Artificial Immune System Based Approach

Research paper thumbnail of On the Use of Rough Sets for Artefact Extraction from EEG Datasets

High-density electroencephalography produces large volumes of data. The analysis of EEG data is c... more High-density electroencephalography produces large volumes of data. The analysis of EEG data is confounded by the existed of a number of different artefacts such as eye blink and, muscle movement which impede the analysis of the data. Typically, artefacts are removed by visual inspection - an arduous task for high-density recordings. In addition, different researchers use Consistency across different laboratories is often difficult, and in addition, the task has to be repeated for each study. An automated method for artefact identification and removal would be a very useful tool for data processing in this domain. In this study, rough sets is employed as a means of automating artefact identification and removal within the context of EEG analysis using the EEGLAB analysis system. The results from this preliminary study indicate that artefacts can be identified and removed with approximately 85% accuracy.

Research paper thumbnail of Classification using adaptive fuzzy inference neural networks

ACTA Press eBooks, 2008

The architecture and learning scheme of a novel fuzzy logic system implemented in the framework o... more The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Both error backpropagation and recursive least squares estimation, are applied to the learning scheme. The convergence of training is faster because the least-squares algorithm is applied to the estimation of consequence parameters of the system and backpropagation is applied only to the estimation of the premise parameters. Using the proposed scheme, high-dimensional fuzzy systems can be realized with fewer rules than a typical Takagi-Sugeno fuzzy system. A number of simulations demonstrate the performance of the proposed system.

Research paper thumbnail of Data Mining a Prostate Cancer Dataset Using Neural Networks

Research paper thumbnail of Evaluation of the Feature Space of an Erythematosquamous Dataset Using Rough Sets

Annals of the University of Craiova - Mathematics and Computer Science Series, Sep 11, 2009

Research paper thumbnail of A Hybrid Approach to MR Imaging Segmentation Using Unsupervised Clustering and Approximate Reducts

Lecture Notes in Computer Science, 2005

We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised cluste... more We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images and 89%

Research paper thumbnail of An Automated Multi-spectral MRI Segmentation Algorithm Using Approximate Reducts

Springer eBooks, 2004

We introduce an automated multi-spectral MRI segmentation technique based on approximate reducts ... more We introduce an automated multi-spectral MRI segmentation technique based on approximate reducts derived from the data mining paradigm of the theory of rough sets. We utilized the T1, T2 and PD MRI images from the Simulated Brain Database as a ”gold standard” to train and test our segmentation algorithm. The results suggest that approximate reducts, used alone or in combination

Research paper thumbnail of Behavioral Biometrics: A Remote Access Approach

Research paper thumbnail of Automatic detection Of EEG abnormalities using wavelet transformations

Research paper thumbnail of Graphical-Based Authentication Methods

John Wiley & Sons, Ltd eBooks, Nov 24, 2008

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