Using Spatio-Temporal Interest Points (STIP) for myoclonic jerk detection in nocturnal video.
In this study we introduce a method for detecting myoclonic jerks during the night with video. Using video instead of the traditional method of using EEG-electrodes, permits patients to sleep without any attached sensors. This improves the comfort during sleep and it makes long term home monitoring possible. The algorithm for the detection of the seizures is based on spatio-temporal interest points (STIPs), proposed by Ivan Laptev, which is the state-of-the-art in action recognition.We applied this algorithm on a group of patients suffering from myoclonic jerks. With an optimal parameter setting this resulted in a sensitivity of over 75% and a PPV of over 85%, on the patients' combined data.
MOBILAB of the K.H.Kempen University College, Geel, Belgium. kris.cuppens@khk.be
Kris
K
Cuppens
Chih-Wei
CW
Chen
Kevin Bing-Yung
KB
Wong
Anouk
A
Van de Vel
Lieven
L
Lagae
Berten
B
Ceulemans
Tinne
T
Tuytelaars
Sabine
S
Van Huffel
Bart
B
Vanrumste
Hamid
H
Aghajan
D059925:Anatomic Landmarks / Q000473:pathology*; D002648:Child; D002675:Child, Preschool; D004831:Epilepsies, Myoclonic / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods; D009207:Myoclonus / Q000175:diagnosis* / Q000503:physiopathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*