ObjectiveTo develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement.MethodsIn this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic >30 seconds, hyperkinetic, or others, including clusters (>30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers’ user experience.ResultsTwenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65–0.89). Median sensitivity per participant amounted to 86% (95% CI 77%–93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01–0.05); and the positive predictive value was 49% (95% CI 33%–64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%–80%, p < 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively.ConclusionCombining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.

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People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

SummaryObjectiveAutomated 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.MethodsIn 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.ResultsNinety‐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.SignificanceIt 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.

Objective: To assess the performance of a multimodal seizure detection device, first tested in adults (sensitivity 86%, PPV 49%), in a pediatric cohort living at home or residential care. Methods:In this multicenter, prospective, video-controlled cohort-study, nocturnal seizures were detected by heartrate and movement changes in children with epilepsy and intellectual disability. Participants with a history of >1 monthly major motor seizure wore Nightwatch bracelet at night for 3 months.Major seizures were defined as tonic-clonic, generalized tonic >30 s, hyperkinetic, or clusters (>30 min) of short myoclonic or tonic seizures. The video of all events (alarms and nurse diaries) and about 10% of whole nights were reviewed to classify major seizures, and minor or no seizures.Results: Twenty-three participants with focal or generalized epilepsy and nightly motor seizures were evaluated during 1511 nights, with 1710 major seizures. First 1014 nights, 4189 alarms occurred with average of 1.44/h, showing average sensitivity of 79.9% (median 75.4%) with mean PPV of 26.7% (median 11.1%) and false alarm rate of 0.2/hour. Over 90% of false alarms in children was due to heart rate (HR) part of the detection algorithm. To improve this rate, an adaptation was made such that the alarm was only triggered when the wearer was in horizontal position. For the remaining 497 nights, this was tested prospectively, 384 major seizures occurred. This resulted in mean PPV of 55.5% (median 58.1%) and a false alarm rate 0.08/h while maintaining a comparable mean sensitivity of 79.4% (median 93.2%).Significance: Seizure detection devices that are used in bed which depend on heartrate and movement show similar sensitivity in children and adults.However, children do show general higher false alarm rate, mostly triggered How to cite this article: Lazeron RH, Thijs RD,

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Studies in adults with epilepsy, mainly in specialized epilepsy clinics, have shown that sleep disturbances were twice as prevalent in people with epilepsy as in healthy controls. Our aim was to determine the prevalence of sleep disturbances in people with epilepsy treated in district hospitals, as well as the impact of it on Quality of Life. Method: Adults with epilepsy, attending outpatient clinics in three district hospitals were invited to participate. Those who accepted (N = 122) provided their own controls matched for age and sex. Both groups completed four questionnaires (Groningen Sleep Quality Scale (GSQ), Medical Outcomes Study-Sleep scale (MOSS), Sleep Diagnosis List (SDL) and Epworth Sleepiness Scale) to measure their sleep over different periods and the 36-Item Short Form Health Survey (SF-36) to measure Quality of Life (QoL). The prevalence of sleep disturbances and scores on QoL were compared between both groups. Results: Sleep quality, measured by the SDL, was in the pathological range 50% more often in the epilepsy group than in controls. This was confirmed by the MOSS INDEX and GSQ. People with epilepsy experienced excessive daytime sleepiness more often than controls.The lowest scores on nearly all domains of the SF-36 were seen in people with epilepsy and associated sleep disturbances. Conclusion:We confirmed the higher prevalence of sleep disturbances in people with epilepsy compared to controls as previously reported from specialized settings. The (co-morbid) sleep disturbances result in lower QoL scores, in both people with epilepsy and in controls, but more in people with epilepsy.