Multimodal nocturnal seizure detection: Do we need to adapt algorithms for children?

Lazeron, R.H.C.; Thijs, Roland D.; Arends, Johan; Gutter, T.; Cluitmans, P.J.M.; Dijk, Johannes Van; Tan, In Y.; Hofstra, Wytske A.; Donjacour, Claire E H M; Leijten, Frans S.S.

doi:10.1002/epi4.12618

Cited by 11 publications

(26 citation statements)

References 30 publications

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“…In a prospective phase 4 study, we demonstrated the good performance of a wearable multimodal device (NightWatch) for the detection of nocturnal major motor seizures (median sensitivity of 86% per person and median false alarm rate [FAR] of .25 per night) 14 . Subsequent validation of NightWatch in a pediatric cohort revealed higher FARs, with rates amounting to .2 per hour 15 . To improve performance, we adapted the algorithm and found that it could reduce FAR to levels close to that of adults while maintaining high sensitivity 15 …”

Section: Introductionmentioning

confidence: 95%

“…The multimodal algorithm of NightWatch, based on photoplethysmography and accelerometry (ACC) data, is described in more detail in previous publications. 14,15 Heart rate (HR) values are determined and updated every second based on a 5-min average of past individual peak-to-peak intervals. The accelerometry sensor measures motion and position, where position represents the angle of the sensor with respect to the gravity vector.…”

Section: Seizure Detection Algorithmmentioning

confidence: 99%

“…We estimated a sample size of 384 major motor seizures to obtain acceptable confidence limits (precision = 4%) assuming a conservative sensitivity of 80%. 15 We aimed to include 60 participants with ≥1 major nocturnal motor seizure per week. We expected a 2-month intervention period (9 weeks) with a dropout rate < 25% to yield at least 405 significant seizures.…”

Section: Sample Sizementioning

confidence: 99%

“…14 Subsequent validation of NightWatch in a pediatric cohort revealed higher FARs, with rates amounting to .2 per hour. 15 To improve performance, we adapted the algorithm and found that it could reduce FAR to levels close to that of adults while maintaining high sensitivity. 15 We, therefore, set up a long-term, home-based phase 4 study to prospectively validate the performance of the adjusted NightWatch algorithm in children with severe epilepsy while monitoring the effect on caregiver's stress, sleep, and QoL.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequent validation of NightWatch in a pediatric cohort revealed higher FARs, with rates amounting to .2 per hour 15 . To improve performance, we adapted the algorithm and found that it could reduce FAR to levels close to that of adults while maintaining high sensitivity 15 …”

Section: Introductionmentioning

confidence: 99%

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Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long‐term, in‐home trial

et al. 2023

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ObjectiveThere is a pressing need for reliable automated seizure detection in epilepsy care. Performance evidence on ambulatory non‐electroencephalography‐based seizure detection devices is low, and evidence on their effect on caregiver's stress, sleep, and quality of life (QoL) is still lacking. We aimed to determine the performance of NightWatch, a wearable nocturnal seizure detection device, in children with epilepsy in the family home setting and to assess its impact on caregiver burden.MethodsWe conducted a phase 4, multicenter, prospective, video‐controlled, in‐home NightWatch implementation study (NCT03909984). We included children aged 4–16 years, with ≥1 weekly nocturnal major motor seizure, living at home. We compared a 2‐month baseline period with a 2‐month NightWatch intervention. The primary outcome was the detection performance of NightWatch for major motor seizures (focal to bilateral or generalized tonic–clonic [TC] seizures, focal to bilateral or generalized tonic seizures lasting >30 s, hyperkinetic seizures, and a remainder category of focal to bilateral or generalized clonic seizures and "TC‐like" seizures). Secondary outcomes included caregivers' stress (Caregiver Strain Index [CSI]), sleep (Pittsburgh Quality of Sleep Index), and QoL (EuroQol five‐dimension five‐level scale).ResultsWe included 53 children (55% male, mean age = 9.7 ± 3.6 years, 68% learning disability) and analyzed 2310 nights (28 173 h), including 552 major motor seizures. Nineteen participants did not experience any episode of interest during the trial. The median detection sensitivity per participant was 100% (range = 46%–100%), and the median individual false alarm rate was .04 per hour (range = 0–.53). Caregiver's stress decreased significantly (mean total CSI score = 8.0 vs. 7.1, p = .032), whereas caregiver's sleep and QoL did not change significantly during the trial.SignificanceThe NightWatch system demonstrated high sensitivity for detecting nocturnal major motor seizures in children in a family home setting and reduced caregiver stress.

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Section: Introductionmentioning

confidence: 95%

Section: Seizure Detection Algorithmmentioning

confidence: 99%

Section: Sample Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long‐term, in‐home trial

et al. 2023

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Multimodal nocturnal seizure detection: Do we need to adapt algorithms for children?

et al. 2022

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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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Feasibility of cardiac‐based seizure detection and prediction: A systematic review of non‐invasive wearable sensor‐based studies

Seth,

Watterson,

Xie

et al. 2023

Epilepsia Open

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ObjectiveA reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non‐invasive wearable devices.MethodsThe Embase, PubMed and Scopus databases were used to systematically search according to the PRISMA guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS‐2 tool and proposed standards for validation for seizure detection devices were used for quality assessment.ResultsTwenty‐four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms and four studies focused on seizure prediction. Most studies used either a wrist‐worn or chest‐worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilised for the algorithms mainly included heart rate (HR) (n=11) or a combination of HR and heart rate variability (HRV) (n=6). HR‐based seizure detection studies collectively reported a sensitivity range of 56%‐100% and a false alarm rate (FAR) of 0.02‐8 per hour, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm, using HRV extracted from ECG data collected from a custom wearable device.SignificanceThese studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithm performance. Many studies are in the proof‐of‐principle stage and evidence for real‐time detection or prediction is currently limited. Future studies should prioritise further refinement of the algorithm performance with prospective validation using large‐scale longitudinal data.

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Multimodal nocturnal seizure detection: Do we need to adapt algorithms for children?

Cited by 11 publications

References 30 publications

Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long‐term, in‐home trial

Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long‐term, in‐home trial

Multimodal nocturnal seizure detection: Do we need to adapt algorithms for children?

Feasibility of cardiac‐based seizure detection and prediction: A systematic review of non‐invasive wearable sensor‐based studies

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