Non-EEG seizure detection systems and potential SUDEP prevention: State of the art

Vel, Anouk Van de; Cuppens, Kris; Bonroy, Bert; Milosevic, Milica; Jansen, Katrien; Huffel, Sabine Van; Vanrumste, Bart; Cras, Patrick; Lagae, Lieven; Ceulemans, Berten

doi:10.1016/j.seizure.2016.07.012

Cited by 127 publications

(108 citation statements)

References 86 publications

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“…Wearable automated seizure detectors may improve existing practice by providing continuous ambulatory monitoring, potentially more accurate seizure counts, and alerts for early intervention 10,11,12 . Existing automated seizure detectors 11 measure motion to detect seizures with a motor manifestation.…”

Section: Introductionmentioning

confidence: 99%

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Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Onorati¹,

Regalia²,

Caborni³

et al. 2017

Epilepsia

176

169

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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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Section: Introductionmentioning

confidence: 99%

“…combining ACM with EMG 20,21 or with electrodermal activity, EDA 22 ) have shown increased sensitivity with reduced false alarms 10,12 . Moreover, physiological parameters may be useful to assess SUDEP risk; for example, the amplitude of EDA accompanying GTC seizures has been shown to correlate to the duration of PGES 23 .…”

Section: Introductionmentioning

confidence: 99%

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Onorati¹,

Regalia²,

Caborni³

et al. 2017

Epilepsia

176

169

View full text Add to dashboard Cite

show abstract

“…As such, there is a significant number of people living with the risks of epilepsy, the most feared of which may be sudden unexpected death in epilepsy (SUDEP) [6]. There exists a need for seizure detection and monitoring devices to alert caregivers to the occurrence of seizures in patients whose disease is not controlled [7].…”

Section: Introductionmentioning

confidence: 99%

Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features

Jacobs

Hilton

Campo

et al. 2018

IEEE Trans. Biomed. Eng.

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“…We used the algorithm proposed by [23] due to the high accuracy (99.382%) in a timely manner that performs in contrary to other more complex algorithms. In the second-phase, motivated by the results presented in [24], we correlated bio-signals from the medical sensors (ECG, blood pressure, pulsioximeter, body thermometer, airflow) using J48 classification algorithm in order to detect seizure and/or stroke [25]. The J48 classification algorithm generates decision trees, the nodes of which evaluate the existence or significance of a seizure or stroke event.…”

Section: ) Outlier Detectionmentioning

confidence: 99%

IoTA: IoT automated SIP-based emergency call triggering system for general eHealth purposes

Andriopoulou

Orphanoudakis

Dagiuklas

2017

2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Abstract-The expansion of Internet of Things (IoT) and the evolution in communication technologies have enabled homes, cars even whole cities to be network connected. However, during an emergency incident, IoT devices have not been used to trigger emergency calls directly to healthcare providers mainly due to their constrained capabilities and lack of support session-oriented communications. Moreover, emergency services are currently offered by public safety stakeholders that do not support call triggering by IoT devices. This paper proposes IoTA framework which enables IoT devices to generate automatically emergency calls and support bi-directional communication sessions between healthcare providers and end users. The IoTA framework incorporates intelligent algorithms for processing and evaluating emergency events from various devices and performs emergency calls immediately after the occurrence of an event. The healthcare providers can interact with the IoTA framework requesting continuous real-time sensor data. A prototype implementation and initial evaluation results are presented as a proof of concept for people suffering from diverse chronic diseases. Experimental results have shown that the proposed framework can be considered as a promising solution for detecting, reporting emergency events, eliminating the hoax calls and responding swiftly saving lives.

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Non-EEG seizure detection systems and potential SUDEP prevention: State of the art

Cited by 127 publications

References 86 publications

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features

IoTA: IoT automated SIP-based emergency call triggering system for general eHealth purposes

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