Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy

Abstract

The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 x 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784-789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets.

A review of signals used in sleep analysis

“…Cuppens et al (2010) used optical flow computations (Horn & Schunck 1981) from nocturnal video recordings of paediatric patients with epilepsy to discriminate periods of movement vs. non-movement. Their aim was to find an alternative method of monitoring epileptic seizures to video-EEG which is difficult to use for home monitoring over long periods of time.…”

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

“…Different techniques to detect and to track motion including optical flow and shift clustering analysis, [44][45][46][47][48] spatiotemporal interest point detectors, and histograms-of-flow features 49,50 have been used in the marker-free systems. These handcrafted features have shown limited success.…”

Detektion periodischer Beinbewegungen im Schlaf

“…Manchmal gehen sie mit kortikalen Arousals oder sogar einem kurzen Aufwachen einher. Man nimmt an, dass die PLMs die Ursache von fragmentiertem Schlaf, nicht erholsamem Schlaf und Tagesschläfrig-keit sind [3,12].…”