Dataset Information

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

ABSTRACT: Objective:Automated seizure detection and alarming could improve quality of life 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 multicenter, prospective cohort study, the nonelectroencephalographic (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 was used for assessing performance. Results:Ninety-five patients were included, but due to sensor failures, data from only 43 (of whom 23 patients had 86 seizures, representing 402 h of data) could be used for analysis. The algorithms yield acceptable sensitivities, especially for clinically urgent seizures (sensitivity = 71-87%), but produce high false alarm rates (2.3-5.7 per night, positive predictive value = 25-43%). There was a large variation in the number of false alarms per patient. Significance:It seems feasible to develop a detector with high sensitivity, but false alarm rates are too high for use in clinical practice. For further optimization, personalization of algorithms may be necessary.

SUBMITTER: van Andel J

PROVIDER: S-EPMC5862103 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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

van Andel Judith J Ungureanu Constantin C Arends Johan J Tan Francis F Van Dijk Johannes J Petkov George G Kalitzin Stiliyan S Gutter Thea T de Weerd Al A Vledder Ben B Thijs Roland R van Thiel Ghislaine G Roes Kit K Leijten Frans F

Epilepsia open 20170906 4

<h4>Objective</h4>Automated seizure detection and alarming could improve quality of life 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.<h4>Methods</h4>In this multicenter, prospective cohort study, the nonelectroencephalographic (non-EEG) signals heart rate and accelerometry were measured duri ...[more]

PMID: 29588973

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Project description:OBJECTIVE:Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind-the-ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind-the-ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels. METHODS:Fifty-four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video-EEG at University Hospital Leuven. In addition, extra behind-the-ear EEG channels were recorded. First, a neurologist was asked to annotate behind-the-ear EEG segments containing selected seizure and nonseizure fragments. Second, a data-driven algorithm was developed using only behind-the-ear EEG. This algorithm was trained using data from other patients (patient-independent model) or from the same patient (patient-specific model). RESULTS:The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false-positive detections (FPs)/24 hours with the patient-independent model. The patient-specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. SIGNIFICANCE:Visual recognition of ictal EEG patterns using only behind-the-ear EEG is possible in a significant number of patients with TLE. A patient-specific seizure detection algorithm using only behind-the-ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.

Project description:C57BL/6J mice exposed to eight flurothyl-induced generalized clonic seizures exhibit a change in seizure phenotype following a 28-day incubation period and subsequent flurothyl rechallenge. Mice now develop a complex seizure semiology originating in the forebrain and propagating into the brainstem seizure network (a forebrain→brainstem seizure). In contrast, this phenotype change does not occur in seizure-sensitive DBA/2J mice. The underlying mechanism(s) was the focus of these studies.DBA2/J mice were exposed to eight flurothyl-induced seizures (1/day) followed by 24-hour video-electroencephalographic recordings for 28-days. Forebrain and brainstem seizure thresholds were determined in C57BL/6J and DBA/2J mice following one or eight flurothyl-induced seizures, or after eight flurothyl-induced seizures, a 28-day incubation period, and final flurothyl rechallenge.Similar to C57BL/6J mice, DBA2/J mice expressed spontaneous seizures. However, unlike C57BL/6J mice, DBA2/J mice continued to have spontaneous seizures without remission. Because DBA2/J mice do not express forebrain→brainstem seizures following flurothyl rechallenge after a 28-day incubation period, this indicated that spontaneous seizures were not sufficient for the evolution of forebrain→brainstem seizures. Therefore, we determined whether brainstem seizure thresholds were changing during this repeated-flurothyl model and whether this could account for the expression of forebrain→brainstem seizures. Brainstem seizure thresholds were not different between C57BL/6J and DBA/2J mice on day one or on the last induction seizure trial (day eight). However, brainstem seizure thresholds did differ significantly on flurothyl rechallenge (day 28) with DBA/2J mice showing no lowering of their brainstem seizure thresholds.These results demonstrated that DBA/2J mice exposed to the repeated-flurothyl model develop spontaneous seizures without evidence of seizure remission and provide a new model of epileptogenesis. Moreover, these findings indicated that the transition of forebrain ictal discharge into the brainstem seizure network occurs due to changes in brainstem seizure thresholds that are independent of spontaneous seizure expression.

Dataset Information

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

Publications

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

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