Multi-Biosignal Analysis for Epileptic Seizure Monitoring

Cogan, Diana; Birjandtalab, Javad; Nourani, Mehrdad; Harvey, Jay; Nagaraddi, Venkatesh

doi:10.1142/s0129065716500313

Cited by 107 publications

(80 citation statements)

References 43 publications

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“…Several other seizure-detection devices have appeared within the last decade, some employing EEG 2,10,17,21,22 and some employing other technologies, such as EMG/accelerometry, 23,24 heart rate variability, 25 electrodermal activity, or a combination of these. 26,27 Describing the influence of an ultra-long-term EEG monitoring device on the mobility of the wearer is a key aspect of its usability. In 2017, Bateson et al 28 developed a mobility score for use in wearable EEG research going from 0 (least mobile) to 5 (most mobile).…”

Section: Discussionmentioning

confidence: 99%

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

et al. 2019

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ObjectiveTo explore the feasibility of home monitoring of epilepsy patients with a novel subcutaneous electroencephalography (EEG) device, including clinical implications, safety, and compliance via the first real‐life test.MethodsWe implanted a beta‐version of the 24/7 EEG SubQ (UNEEG Medical A/S, Denmark) subcutaneously in nine participants with temporal lobe epilepsy. Data on seizures, adverse events, compliance in using the device, and use of antiepileptic drugs (AEDs) were collected. EEG was recorded for up to 3 months, and all EEG data were reviewed visually to identify electrographic seizures. These were descriptively compared to seizure counts and AED changes reported in diaries from the same period.ResultsFour hundred ninety days of EEG and 338 electrographic seizures were collected. Eight participants completed at least 9 weeks of home monitoring, while one cancelled participation after 4 weeks due to postimplantation soreness. In total, 13 cases of device‐related adverse events were registered, none of them serious. Recordings obtained from the device covered 73% of the time, on average (range 45%‐91%). Descriptively, electrographic seizure counts were substantially different from diary seizure counts. We uncovered several cases of underreporting and revealed important information on AED response. Electrographic seizure counts revealed circadian distributions of seizures not visible from seizure diaries.SignificanceThe study shows that home monitoring for up to 3 months with a subcutaneous EEG device is feasible and well tolerated. No serious adverse device‐related events were reported. An objective seizure count can be derived, which often differs substantially from self‐reported seizure counts. Larger clinical trials quantifying the benefits of objective seizure counting should be a priority for future research as well as development of algorithms for automated review of data.

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Section: Discussionmentioning

confidence: 99%

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

et al. 2019

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“…A study with an Empatica E3 wristband reported that 97% of 34 predominantly non-motor seizures could be detected with a hierarchical classifier based on EDA 36 . However, using mainly EDA dramatically decreased specificity; thus, information from other physiological signals is necessary to detect non-convulsive seizures, such as heart rate and arterial oxygenation 41 .…”

Section: Limits and Future Workmentioning

confidence: 99%

“…36 However, using mainly EDA dramatically decreased specificity; thus, information from other physiological signals is necessary to detect nonconvulsive seizures, such as heart rate and arterial oxygenation. 41 Finally, a seizure detector incorporating EDA could be suitable for other important applications, such as identifying triggering factors reflected in autonomic activations (e.g., stress or deep sleep) or using EDA biofeedback for training patients to prevent epileptic seizures. 42…”

Section: Limits and Future Workmentioning

confidence: 99%

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Onorati¹,

Regalia²,

Caborni³

et al. 2017

Epilepsia

159

163

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Running title: Wearable multimodal motor seizure detectors Onorati et al. 2 Summary Objective:New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive, automated and provide false alarm rates bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Methods:Hand-annotated video-electroencephalography seizure events were collected from 69 patients at 6 clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 hours of data, including 55 convulsive epileptic seizures (6 focal tonic-clonic seizures and 49 focal-to-bilateral-tonicclonic seizures) from 22 patients. Recordings were analyzed off-line to train and test two new machine learning classifiers and a published EDA and ACM-based classifier. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. Results:The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and false alarm rate (FAR) of 0.2 events/day.No nocturnal seizures were missed. Most patients had less than 1 false alarm every 4 days with FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures) the FAR is up to 13 times lower than the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median: 29.3 s, range: 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of post-ictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. Onorati et al. 3 Significance:The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behaviour and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.

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“…Epileptic seizures are potentially dangerous as they can lead to complications, including injury, status epilepticus, and sudden unexpected death in epilepsy (SUDEP) [ 1 ]. Adequate seizure detection may have the potential to minimize these complications and to ameliorate treatment evaluation, as seizures—particularly those at night—are often underreported [ 2 – 5 ]. Detection devices may also help to improve the independence and quality of life of people with epilepsy and their caregivers [ 3 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Ictal autonomic changes as a tool for seizure detection: a systematic review

et al. 2018

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Purpose Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters. Methods The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. Results Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability ( n = 10), heart rate ( n = 4), or QRS morphology ( n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h). Conclusions The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection. Electronic supplementary material The online version of this article (10.1007/s10286-018-0568-1) contains supplementary material, which is available to authorized users.

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Multi-Biosignal Analysis for Epileptic Seizure Monitoring

Cited by 107 publications

References 43 publications

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Ictal autonomic changes as a tool for seizure detection: a systematic review

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