Multimodal, automated detection of nocturnal motor seizures at home: Is a reliable seizure detector feasible?

Autor: Frans S. S. Leijten, Judith van Andel, Johan Arends, Francis Tan, Johannes P. van Dijk, George Petkov, Roland D. Thijs, Al W. de Weerd, Ghislaine J. M. W. van Thiel, Constantin Ungureanu, Stiliyan Kalitzin, Kit C.B. Roes, Ben Vledder, Thea Gutter
Přispěvatelé: Signal Processing Systems, Biomedical Diagnostics Lab
Rok vydání: 2017
Předmět:
Zdroj: Epilepsia Open, 2(4), 424-431. Wiley
Epilepsia Open, 2(4), 424. Wiley-Blackwell Publishing Ltd
Epilepsia Open
ISSN: 2470-9239
DOI: 10.1002/epi4.12076
Popis: SummaryObjective Automated seizure detection and alarming could improve quality of life of and potentially prevent sudden, unexpected death in patients with severe epilepsy. As currently available systems focus on tonic-clonic seizures, we want to detect a broader range of seizure types including tonic, hypermotor and clusters of seizures. Methods In this multi-centre, prospective cohort study, non-EEG signals heart rate and accelerometry were measured during the night in patients undergoing a diagnostic video-EEG examination. Based on clinical video-EEG data seizures were classified and categorized as clinically urgent or not. Seizures included for analysis were tonic, tonic-clonic, hypermotor and clusters of short myoclonic/tonic seizures. Features reflecting physiological changes in heart rate and movement were extracted. Detection algorithms were developed based on stepwise fulfillment of conditions during increases in either feature. A training set was used for development of algorithms and an independent test set for assessing performance. Results 95 patients were included, but due to sensor failures, data from only 43 (out of whom 23 patients had 86 seizures, representing 402 hours of data) could be used for analysis. The algorithms yield acceptable sensitivities, especially for clinically urgent seizures (sensitivity 71%-87%), but come with high false alarm rates (2.3-5.7 per night, positive predictive value 25-43%). There was a large variation in number of false alarms per patient. Significance It seems feasible to develop a detector with a high sensitivity but false-alarm rates are too high for use in clinical practice. For further optimization, personalization of algorithms may be necessary. This article is protected by copyright. All rights reserved.
Databáze: OpenAIRE
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