title: Simulation, measurement, and design of multi-electrode spinal cord stimulation Leads with a focus on MRI safeness and stimuli induced artefacts

authors: Hartung, Dirk

subjects: MRI safeness

abstract: The electromagnetic (EM) and thermodynamic (TD) models for a Spinal Cord Stimulator (SCS) presented in this work, permit on one hand to analyse the impact of electrical stimuli patterns on artefacts and on the other hand to analyse the impact of RF exposure during MRI scans on tissue heating. Historically, many models have focused on single electrode Leads whereas this work focuses on multi-electrode Leads. Testing of Leads and IPGs for Magnetic Resonance Imaging (MRI) compliance is a tedious, time-consuming and expensive process. A table-top RF test bed has been conceived and calibrated to systematically investigate MRI compliance for various RF exposure scenarios. In addition, a table-top test bed has been conceived to determine the propagation constant of electromagnetic (EM) waves along Leads embedded in electrolytes. A thin low permittivity layer around the multi-lumen conduct (MLC) has been added to the EM model to solve the discrepancy between measured and initially calculated propagation constants. Further, several new measures to minimise tissue heating caused by Leads during MRI scans are presented. The work compares EM and TD simulation results obtained from finite element method (FEM) solvers with measurement results obtained from various test beds that have been conceived or adapted. Despite some discrepancies between simulated and measured results the EM and thermodynamic models permit to investigate the impact of stimuli patterns on artefacts and the impact of RF exposure on tissue heating. The results show that worst case tissue heating does not necessarily occur at the distal end electrode in multi-electrode Leads and that RF exposure scenarios have an impact on locations where RF power is dissipated at the distal end. Simulations and measurements show that Lead wire routing anomalies, i.e. changes in RF coupling between Lead wires along a multi-lumen conduct, have a significant impact on locations where RF power is dissipated at the distal end. The measures proposed to minimise tissue heating have proved their effectiveness in both the table-top RF test bed and in a 3 Tesla MRI scanner. Finally, artefacts captured by recording electrodes in multi-electrode Leads can be significantly reduced if the input impedance of the recording amplifier is low during stimuli on therapeutic electrodes.

text: Simulation, measurement, and design of multi-electrode spinal cord stimulation Leads with a focus on MRI safeness and stimuli induced artefacts The electromagnetic (EM) and thermodynamic (TD) models for a Spinal Cord Stimulator (SCS) presented in this work, permit on one hand to analyse the impact of electrical stimuli patterns on artefacts and on the other hand to analyse the impact of RF exposure during MRI scans on tissue heating. Historically, many models have focused on single electrode Leads whereas this work focuses on multi-electrode Leads. Testing of Leads and IPGs for Magnetic Resonance Imaging (MRI) compliance is a tedious, time-consuming and expensive process. A table-top RF test bed has been conceived and calibrated to systematically investigate MRI compliance for various RF exposure scenarios. In addition, a table-top test bed has been conceived to determine the propagation constant of electromagnetic (EM) waves along Leads embedded in electrolytes. A thin low permittivity layer around the multi-lumen conduct (MLC) has been added to the EM model to solve the discrepancy between measured and initially calculated propagation constants. Further, several new measures to minimise tissue heating caused by Leads during MRI scans are presented. The work compares EM and TD simulation results obtained from finite element method (FEM) solvers with measurement results obtained from various test beds that have been conceived or adapted. Despite some discrepancies between simulated and measured results the EM and thermodynamic models permit to investigate the impact of stimuli patterns on artefacts and the impact of RF exposure on tissue heating. The results show that worst case tissue heating does not necessarily occur at the distal end electrode in multi-electrode Leads and that RF exposure scenarios have an impact on locations where RF power is dissipated at the distal end. Simulations and measurements show that Lead wire routing anomalies, i.e. changes in RF coupling between Lead wires along a multi-lumen conduct, have a significant impact on locations where RF power is dissipated at the distal end. The measures proposed to minimise tissue heating have proved their effectiveness in both the table-top RF test bed and in a 3 Tesla MRI scanner. Finally, artefacts captured by recording electrodes in multi-electrode Leads can be significantly reduced if the input impedance of the recording amplifier is low during stimuli on therapeutic electrodes.

year: 2019

title: Continuous Typist Verification using Machine Learning

authors: Hempstalk, Kathryn

subjects: keystroke verification

abstract: A keyboard is a simple input device.  Its function is to send keystroke information to the computer (or other device) to which it is attached.  Normally this information is employed solely to produce text, but it can also be utilized as part of an authentication system.  Typist verification exploits a typist's patterns to check whether they are who they say they are, even after standard authentication schemes have confirmed their identity.  This thesis investigates whether typists behave in a sufficiently unique yet consistent manner to enable an effective level of verification based on their typing patterns.

Typist verification depends on more than the typist's behaviour.  The quality of the patterns and the algorithms used to compare them also determine how accurately verification is performed.  This thesis sheds light on all technical aspects of the problem, including data collection, feature identification and extraction, and sample classification.  

A dataset has been collected that is comparable in size, timing accuracy and content to others in the field, with one important exception: it is derived from real emails, rather than samples collected in an artificial setting.  This dataset is used to gain insight into what features distinguish typists from one another.  The features and dataset are used to train learning algorithms that make judgements on the origin of previously unseen typing samples.  These algorithms use 'one-class classification'; they make predictions for a particular user having been trained on only that user's patterns.

This thesis examines many one-class classification algorithms, including ones designed specifically for typist verification.  New algorithms and features are proposed to increase speed and accuracy.  The best method proposed performs at the state of the art in terms of classification accuracy, while decreasing the time taken for a prediction from minutes to seconds, and - more importantly - without requiring any negative data from other users.  Also, it is general: it applies not only to typist verification, but to any other one-class classification problem.  

Overall, this thesis concludes that typist verification can be considered a useful biometric technique.

text: Continuous Typist Verification using Machine Learning A keyboard is a simple input device.  Its function is to send keystroke information to the computer (or other device) to which it is attached.  Normally this information is employed solely to produce text, but it can also be utilized as part of an authentication system.  Typist verification exploits a typist's patterns to check whether they are who they say they are, even after standard authentication schemes have confirmed their identity.  This thesis investigates whether typists behave in a sufficiently unique yet consistent manner to enable an effective level of verification based on their typing patterns.

Typist verification depends on more than the typist's behaviour.  The quality of the patterns and the algorithms used to compare them also determine how accurately verification is performed.  This thesis sheds light on all technical aspects of the problem, including data collection, feature identification and extraction, and sample classification.  

A dataset has been collected that is comparable in size, timing accuracy and content to others in the field, with one important exception: it is derived from real emails, rather than samples collected in an artificial setting.  This dataset is used to gain insight into what features distinguish typists from one another.  The features and dataset are used to train learning algorithms that make judgements on the origin of previously unseen typing samples.  These algorithms use 'one-class classification'; they make predictions for a particular user having been trained on only that user's patterns.

This thesis examines many one-class classification algorithms, including ones designed specifically for typist verification.  New algorithms and features are proposed to increase speed and accuracy.  The best method proposed performs at the state of the art in terms of classification accuracy, while decreasing the time taken for a prediction from minutes to seconds, and - more importantly - without requiring any negative data from other users.  Also, it is general: it applies not only to typist verification, but to any other one-class classification problem.  

Overall, this thesis concludes that typist verification can be considered a useful biometric technique.

year: 2009

title: Introduction to special edition.

authors: McGee, Clive

subjects: initial teacher education

abstract: This special issue of the Waikato Journal of Education arose from a symposium held at The University of Waikato in June, 2009. The symposium, Initial Teacher Education and the New Zealand Curriculum–Te Marautanga o Aotearoa Symposium, was attended by delegates from all major initial teacher education (ITE) providers in New Zealand. ITE refers to pre-service teacher education, that is, programmes that prepare student teachers to become beginning teachers. Curriculum includes the school and the ITE curriculum.

text: Introduction to special edition. This special issue of the Waikato Journal of Education arose from a symposium held at The University of Waikato in June, 2009. The symposium, Initial Teacher Education and the New Zealand Curriculum–Te Marautanga o Aotearoa Symposium, was attended by delegates from all major initial teacher education (ITE) providers in New Zealand. ITE refers to pre-service teacher education, that is, programmes that prepare student teachers to become beginning teachers. Curriculum includes the school and the ITE curriculum.

year: 2010