Assessment of a quasi-piezoelectric mattress monitor as a detection system for generalized convulsions

Narechania, Aditi; Garic, Irena; Sen‐Gupta, Indranil; Macken, Mícheál P.; Gerard, Elizabeth E.; Schuele, Stephan

doi:10.1016/j.yebeh.2013.04.017

Cited by 73 publications

(47 citation statements)

References 24 publications

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“…In another study, colored pyjamas were used to facilitate movement quantification for CS detection . Compared to other algorithms targeted on periodicity, the detection delay we used (2 seconds) is shorter than generally applied (10 seconds), while maintaining a low false detection rate. This can be attributed to the application of spectral contrast (opposed to power) and the output‐smoothening effect of the 4‐second calculation window.…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, unobtrusive wireless sensors could be used, but these require regular charging and a reliable wireless connection to an alerting unit. Movement sensors that can be attached to the bed are widely used in nocturnal seizure monitoring and show fair detection performance for CS . Such detectors, however, are only effective if the person is in bed.…”

Section: Introductionmentioning

confidence: 99%

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Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

et al. 2018

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“…Seizure alarms studied include a wireless wrist accelerometer sensor that detects GTCS with high sensitivity and specificity 113. Another seizure alarm that has been studied is an under-mattress device,114 which is triggered by rhythmic motor activity of a specifiable duration, frequency and intensity using a quasi-piezoelectric material sensitive to changes in mattress pressure.…”

Section: Preventionmentioning

confidence: 99%

Sudden unexpected death in epilepsy: basic mechanisms and clinical implications for prevention

Dlouhy

Gehlbach

Richerson

2015

J Neurol Neurosurg Psychiatry

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Sudden unexpected death in epilepsy (SUDEP) is the most common cause of death in patients with intractable epilepsy. The substantial lifetime risk of SUDEP and the lack of a clear pathophysiological connection between epilepsy itself and sudden death have fuelled increased attention to this phenomenon. Understanding the mechanisms underlying SUDEP is paramount to developing preventative strategies. In this review, we discuss SUDEP population studies, case-control studies, witnessed and monitored cases, as well as human seizure cardiorespiratory findings related to SUDEP, and SUDEP animal models. We integrate these data to suggest the most probable mechanisms underlying SUDEP. Understanding the modifiable risk factors and pathophysiology allows us to discuss potential preventative strategies.

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“…Several methods for automated seizure detection based on online analysis of EEG, ECG, or body movements [19][20][21][22] have been developed in the last decades. The performance of several methods based on body movement detectors was recently assessed, showing a sensitivity of 88% to 94% to detect convulsive seizures with various rates of false alarms [23][24][25][26]. Such methods could, thus, prove useful in EMUs with suboptimal or intermittent patients' observation.…”

Section: Discussionmentioning

confidence: 99%

A European survey on current practices in epilepsy monitoring units and implications for patients' safety

Rubboli

Beniczky

Claus

et al. 2015

Epilepsy & Behavior

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Assessment of a quasi-piezoelectric mattress monitor as a detection system for generalized convulsions

Cited by 73 publications

References 24 publications

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

Sudden unexpected death in epilepsy: basic mechanisms and clinical implications for prevention

A European survey on current practices in epilepsy monitoring units and implications for patients' safety

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