Performance metrics for characterization of a seizure detection algorithm for offline and online use

Purpose: To select appropriate previously reported performance metrics to evaluate a new seizure detection algorithm for offline and online analysis, and thus quantify any performance variation between these metrics. Methods: Traditional offline algorithms mark out any EEG section (epoch) of a seizure (event), so that neurologists only analyze the detected and adjacent sections. Thus, offline algorithms could be evaluated using number of correctly detected events, or event-based sensitivity (SEVENT), and epoch-based specificity (percentage of incorrectly detected background epochs). In contrast, online seizure detection (especially, data selection) algorithms select for transmission only the detected EEG sections and hence need to detect the entire duration of a seizure. Thus, online algorithms could be evaluated using percentage of correctly detected seizure duration, or epoch-based sensitivity (SEPOCH), and epoch-based specificity. Here, a new seizure detection algorithm is evaluated using the selected performance metrics for epoch duration ranging from 1s to 60s. Results: For 1s epochs, the area under the event-based sensitivity-specificity curve was 0.95 whilst SEPOCH achieves 0.81. This difference is not surprising, as intuitively, detecting any epoch within a seizure is easier than detecting every epoch - especially as seizures evolve over time. For longer epochs of 30s or 60s, SEVENT falls to 0.84 and 0.82 respectively and SEPOCH reduces to 0.76. Here, decreased SEVENT shows that fewer seizures are detected, possibly due to easy-to-detect short seizure sections being masked by surrounding EEG. However, detecting one long epoch constitutes a larger percentage of a seizure than a shorter one and thus SEPOCH does not decrease proportionately. Conclusions: Traditional offline and online seizure detection algorithms require different metrics to effectively evaluate their performance for their respective applications. Using such metrics, it has been shown that a decrease in performance may be expected when an offline seizure detection algorithm (especially with short epoch duration) is used for online analysis.Accepted versio

Effect of turbulence on electron cyclotron current drive and heating in ITER

Non-linear local electromagnetic gyrokinetic turbulence simulations of the ITER standard scenario H-mode are presented for the q=3/2 and q=2 surfaces. The turbulent transport is examined in regions of velocity space characteristic of electrons heated by electron cyclotron waves. Electromagnetic fluctuations and sub-dominant micro-tearing modes are found to contribute significantly to the transport of the accelerated electrons, even though they have only a small impact on the transport of the bulk species. The particle diffusivity for resonant passing electrons is found to be less than 0.15 m^2/s, and their heat conductivity is found to be less than 2 m^2/s. Implications for the broadening of the current drive and energy deposition in ITER are discussed.Comment: Letter, 5 pages, 5 figures, for submission to Nuclear Fusio

An inverse filter realisation of a single scale Inverse Continuous Wavelet Transform

Published versio

Quantifying the performance of compressive sensing on scalp EEG signals

Compressive sensing is a new data compression paradigm that has shown significant promise in fields such as MRI. However, the practical performance of the theory very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Electroencephalography (EEG) is a fundamental tool for the investigation of many neurological disorders and is increasingly also used in many non-medical applications, such as Brain-Computer Interfaces. This paper characterises in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals for the first time. The results are of particular interest for wearable EEG communication systems requiring low power, real-time compression of the EEG data. ©2010 IEEE.Accepted versio

On data reduction in EEG monitoring: comparison between ambulatory and non-ambulatory recordings

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Utilising noise to improve an interictal spike detector

Accepted versio

Data reduction techniques to facilitate wireless and long term AEEG epilepsy monitoring

Published versio

Standard filter approximations for low power Continuous Wavelet Transforms.

Accepted versio