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

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

Published versio

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

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

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Standard filter approximations for low power Continuous Wavelet Transforms.

Accepted versio

Utilising noise to improve an interictal spike detector

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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

Generic vs custom; analogue vs digital: on the implementation of an online EEG signal processing algorithm

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

Accepted versio