Cardiac-based detection of seizures in children with epilepsy

Hegarty-Craver, Meghan; Kröner, Barbara; Bumbut, Adrian; DeFilipp, Samuel J.; Gaillard, William D.; Gilchrist, Kristin H.

doi:10.1016/j.yebeh.2021.108129

Cited by 7 publications

(14 citation statements)

References 24 publications

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“…A wide range of non‐invasive wearable devices were used to collect the ECG or HR data. Four studies used Empatica E4, 29,43,45,46 three studies used the ePatch device, 37–39 and three studies used Bittium Faros 180° 34,42,45 . Other wearable devices that were noted include Nightwatch, 28,40 Nonin WristOx2, 31,32 SmartCardia INYU wearable sensor, 33 Zephyr Biopatch, 34 cosinuss° In‐Ear sensor, 35 EcgMove, 36 and Shimmer sensor 44 .…”

Section: Resultsmentioning

confidence: 99%

“…33 Six studies evaluated seizure detection based on both HR and HRV parameters. 34,[36][37][38][39]45 Three of these studies included both adult and child participants, two involved adults only, while the remaining study included children only. Among the studies with adult participants, one unimodal algorithm study analyzed and compared ECG and PPG wearable devices for seizure detection in patients with temporal lobe epilepsy (TLE).…”

Section: Wearable Devicesmentioning

confidence: 99%

“…36 In the study with children, a multimodal seizure detection algorithm based on ECG and ACC signals was evaluated. 34 The authors also developed a custom prototype unit to employ the algorithm and allow for real-time seizure detection. The cardiac algorithm, where both HR and HRV were analyzed, was able to detect tonic-clonic and focal seizures without focal to bilateral tonic-clonic features with an overall sensitivity of 72% and FAR or 0.04/h.…”

Section: Wearable Devicesmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Wearable Devicesmentioning

confidence: 99%

Section: Wearable Devicesmentioning

confidence: 99%

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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

View full text Add to dashboard Cite

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“…There were four studies identified that detected both focal seizures and TCS and one separate study that focused on reporting only focal seizures, all using heart rate and heart rate variability. Although the first four studies demonstrated FARs of <2/24 h, the sensitivities ranged from 39% to 93% 20–23 19 …”

Section: Discussionmentioning

confidence: 98%

“…The variability in the algorithms used to analyze the data collected by the devices may underlie study heterogeneity. To avoid increasing heterogeneity further, we did not include studies that utilized invasive EEG measurements in the training dataset 22 . As intracranial recordings may detect seizures that occur in deeper structures but are missed on scalp EEG, these studies may be incorporated in future systematic reviews and meta‐analyses 28 …”

Section: Discussionmentioning

confidence: 99%

Automated seizure detection with noninvasive wearable devices: A systematic review and meta‐analysis

et al. 2022

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Objective: This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES). Methods:We conducted a systematic review and meta-analysis of studies reported up to November 15, 2021. We included studies that used video-electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection. Results:Twenty-eight studies met the criteria for the systematic review, of which 23 were eligible for meta-analysis. These studies (1269 patients in total, median

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Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives

Meritam Larsen,

Beniczky

2023

Epilepsy & Behavior

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Cardiac-based detection of seizures in children with epilepsy

Cited by 7 publications

References 24 publications

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

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

Automated seizure detection with noninvasive wearable devices: A systematic review and meta‐analysis

Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives

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