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CUHK_CCRB00582
2018-02-07
Prospective
CREC Ref No. 2017.554
Protocol No. 2017-HL-001
This is no a funded project
N/A
N/A
Not Applicable
Ka Lai Yip
Room 124010,
10/F, Lui Che Woo Clinical Sciences Building,
Price of Wales Hospital,
30-32 Ngan Shing Street,
Shatin, New Territories
3505 3856
Hannahykl@cuhk.edu.hk
Division of Neurology, The Chinese University of Hong Kong
Hong Kong
Ho Wan Leung
Division of Neurology,
Department of Medicine & Therapeutics,
Price of Wales Hospital, Shatin
3505 3856
Howanleung@cuhk.edu.hk
Division of Neurology, The Chinese University of Hong Kong
Hong Kong
Epileptic seizure detection by neural network architecture based on semiology of video recording
Epileptic seizure detection by neural network architecture based on semiology of video recording
利用(Spatial-Temporal GRU Convolutional Neural Network)深度學習的方法開發新型的智能電腦軟件以分析腦癎發作
Hong Kong
Yes
2017-11-20
Joint CUHK-NTEC Clinical Research Ethics Committee
CREC Ref. No: 2017.554
Epilepsy
Other
A artificial intelligent program
This research is an observational study using video recording to construct an artificial intelligent program for the recognition of epileptic seizures. In the present study, a novel marker-free analytical tool is used as a vision-based monitoring to the epileptic episodes. The video of seizure onsets will be confirmed by specialist or delegated research staffs and processed to the computing analysis.
Not drug trial
Not drug trial
Not drug trial
One epileptic seizure episode per subject
The 40 videos recording for the control will be obtained from the open database in the internet. The control is defined as that without seizure in that video recording.
Not drug trial
Not drug trial
Not drug trial
One normal body movement per subject
Subjects are aged or above 18 years old
Subjects are diagnosed with epilepsy
Subjects who completed a video recording
Subjects are able to give an informed consent
No captured event has been found in the video recording
18
999
Both Male and Female
Observational
Not Applicable
Not Applicable
Not Applicable
Not Applicable
Other
N/A
2018-03-01
40
Not Yet Recruiting
Model validation
The currently used detection model (Spatial-Temporal GRU Convolutional Neural Network) will be validated by the study of video from epilepsy patients.
The efficacy of distinguishing the epilepsy behavior and normal motion will be scored.
Detection latency
A loss function for the calculation of the detection latency is created based on (1) the seizures and normal behavior required separately and (2) the uniformly increasing loss function.
The existing of false positive will be eliminated by Two Stream Segmented ConvNets
2020-12-07
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