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

Optimal features for online seizure detection

This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, area under the sensitivity-specificity curve (AUC), and relative computational complexity, on 47 seizures (split in 2,698 2 s sections) in over 172 h of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5-25 Hz band. Relative power has a better seizure detection performance (AUC = 0.83; line length AUC = 0.77), but is calculated after the discrete wavelet transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors. © International Federation for Medical and Biological Engineering 2012

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

Utilising noise to improve an interictal spike detector

Accepted versio

Algorithms and circuits for truly wearable physiological monitoring

Truly wearable physiological sensors, monitoring for example breathing or the electroencephalogram (EEG), require accurate and reliable algorithms for the automated analysis of the collected signal. This facilitates real-time signal interpretation and reduces the burden on human interpreters. It is well known that to reduce the total device power in many physiological sensors the automated analysis is best carried out using dedicated circuits in the sensor device itself, rather than transmitting all of the raw data and using an external system for the processing. To allow the physiological sensor to operate from the physically smallest batteries and energy harvesters new algorithms optimized for low power operation are thus required. This results in designers being presented with new trade-offs between the algorithm performance (for example the number of correct detections of an event and the number of false detections) and the power consumption of the circuit implementation. This presentation explores the state-of-the-art algorithms and circuits for use in these situations, drawing on particular examples from algorithms and circuits for use in breathing monitoring and EEG analysis.Accepted versio

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

Published versio

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

Published versio

A 60 pW g(m)C Continuous Wavelet Transform Circuit for Portable EEG Systems

Accepted versio

Toward online data reduction for portable electroencephalography systems in epilepsy.

Portable EEG units are key tools in epilepsy diagnosis. Current systems could be made physically smaller and longer lasting by the inclusion of online data reduction methods to reduce the power required for storage or transmission of the EEG data. This paper presents a real-time data reduction algorithm based upon the discontinuous recording of the EEG: noninteresting background sections of EEG are discarded online, with only potentially diagnostically interesting sections being saved. MATLAB simulations of the algorithm on an EEG dataset containing 982 expert marked events in 4 days of data show that 90% of events can be correctly recorded while achieving a 50% data reduction. The described algorithm is formulated to have a direct, low power, hardware implementation and similar data reduction strategies could be employed in a range of body-area-network-type applications. © 2009 IEEE