Vision-based motion detection, analysis and recognition of epileptic seizures—A systematic review

Pediaditis, Matthew; Tsiknakis, Manolis; Leitgeb, Norbert

doi:10.1016/j.cmpb.2012.08.005

Cited by 62 publications

(51 citation statements)

References 32 publications

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“…This was done with frontal and temporal lobe seizures to evaluate for the lateralizing value of ictal head turning [99,100]. The overall sensitivity varies from 75-100%, positive predictive value over 85%, and specificity of 53-93% [93][94][95][96]. Reported latencies range from 4.6 to 21.4 seconds [94].…”

Section: Video Detection Systemsmentioning

confidence: 99%

“…Seizure types that can be captured by video include focal, hypermotor, myoclonic, and clonic [93][94][95]98]. Myoclonic seizures are detected with good sensitivity and specificity with a marker-based system based on spatio-temporal interest points [95].…”

Section: Video Detection Systemsmentioning

confidence: 99%

“…Automatic video detection systems use velocity, area, duration, rotation, oscillation, angular speed, and/or displacement (motion trajectory) to detect seizures [93][94][95][96][97]. The underlying concept is to detect complex motor patterns by automatic interpretation of video data [93].…”

Section: Video Detection Systemsmentioning

confidence: 99%

“…The underlying concept is to detect complex motor patterns by automatic interpretation of video data [93]. The systems have been classified as marker-based or marker-free, depending on whether the cameras track detectable markers placed in relevant places [93].…”

Section: Video Detection Systemsmentioning

confidence: 99%

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Automated seizure detection systems and their effectiveness for each type of seizure

Ulate‐Campos

Coughlin

Gaínza‐Lein

et al. 2016

Seizure

195

166

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Epilepsy affects almost 1% of the population and most of the approximately 20-30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies.

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Section: Video Detection Systemsmentioning

confidence: 99%

Section: Video Detection Systemsmentioning

confidence: 99%

Section: Video Detection Systemsmentioning

confidence: 99%

Section: Video Detection Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Automated seizure detection systems and their effectiveness for each type of seizure

Ulate‐Campos

Coughlin

Gaínza‐Lein

et al. 2016

Seizure

195

166

View full text Add to dashboard Cite

show abstract

“…al. [22] reviewed vision based human motion analysis from Epileptic seizure detection point of view. An image processing techniques have been applied using quantitative video analysis of movement patterns of adult patients with epilepsy during seizures [23].The suitability of wearable sensors (accelerometer) is used to detect seizure events in patients exhibiting different types of seizures has been evaluated by Nijsen et.…”

Section: A Seizure Detection Algorithm -mentioning

confidence: 99%

An Image Processing Based Novel Approach to Real-Time Analysis and Diagnosis of Epileptic Seizures using Wearable Sensors

2017

IJIET

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Characteristics of motion signal profiles of tonic–clonic, tonic, hyperkinetic, and motor seizures extracted from nocturnal video recordings

Ojanen,

Kertész,

Peltola

2024

Epileptic Disorders

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ObjectiveIn this study, characteristics of signal profiles formed by motion, oscillation, and sound signals were analyzed to evaluate generalizability and variability in a single patient setting (intra‐patient variability) and between patients (inter‐patient variability). As a secondary objective, the effect of brivaracetam intervention on signal profiles was explored.MethodsPatient data included 13 hyperkinetic seizures, 65 tonic seizures, 13 tonic–clonic seizures, and 138 motor seizures from 11 patients. All patients underwent an 8‐week monitoring, and after a 3‐week baseline, brivaracetam was initiated. Motion, oscillation, and sound features extracted from the video were used to form signal profiles. Variance of signals was calculated, and combined median and quartile visualizations were used to visualize the results. Similarly, the effect of intervention was visualized.ResultsHyperkinetic motion signals showed a rapid increase in motion and sound signals without oscillations and achieved low intra‐patient variance. Tonic component created a recognizable peak in motion signal typical for tonic and tonic–clonic seizures. For tonic seizures, inter‐patient variance was low. Motor signal profiles were varying, and they did not form a generalizable signal profile. Visually recognizable changes were observed in the signal profiles of two patients.SignificanceVideo‐based motion signal analysis enabled the extraction of motion features characteristic for different motor seizure types which might be useful in further development of this system. Tonic component formed a recognizable seizure signature in the motion signal. Hyperkinetic and motor seizures may have not only significantly different motion signal amplitude but also overlapping signal profile characteristics which might hamper their automatic differentiation. Motion signals might be useful in the assessment of movement intensity changes to evaluate the treatment effect. Further research is needed to test generalizability and to increase reliability of the results.

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Vision-based motion detection, analysis and recognition of epileptic seizures—A systematic review

Cited by 62 publications

References 32 publications

Automated seizure detection systems and their effectiveness for each type of seizure

Automated seizure detection systems and their effectiveness for each type of seizure

An Image Processing Based Novel Approach to Real-Time Analysis and Diagnosis of Epileptic Seizures using Wearable Sensors

Characteristics of motion signal profiles of tonic–clonic, tonic, hyperkinetic, and motor seizures extracted from nocturnal video recordings

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