Automated Extraction of Temporal Motor Activity Signals From Video Recordings of Neonatal Seizures Based on Adaptive Block Matching

Abstract

Figure 1 illustrates the mechanism that can be used for generating temporal signals tracking the movements of different parts of the infant's body during focal clonic and myoclonic seizures [4]. Figure 1 depicts a single frame containing the sketch of an infant's body with four selected anatomical sites. In this particular configuration, X LL and Y LL represent the projections of the site located at the left leg to the horizontal and vertical axes, respectively. The projections of the sites located at the right leg, left hand, and right hand are denoted by X RL and Y RL , X LH and Y LH , and X RH and Y RH , respectively. As the infant moves its extremities, the locations of the sites in the frame will change, as will the projections of the sites to the horizontal and vertical axes. Recording the values of the projections from frame to frame of the videotaped seizure will generate four pairs of temporal signals, namely the signals X LL (t) and Y LL (t) for the left leg, the signals X RL (t) and Y RL (t) for the right leg, the signals X LH (t) and Y LH (t) for the left hand, and the signals X RH (t) and Y RH (t) for the right hand. For a given set of anatomical sites, each seizure will produce signature signals depending on its type and location.

An improved procedure for the extraction of temporal motion strength signals from video recordings of neonatal seizures

“…This would be sensible only if the performance gain offered by optical flow computation is high enough to compensate for its computational requirements, which exceed by far those of the procedure proposed in this paper. The discrimination between focal clonic seizures and random infant movements may also be reinforced by combining motion strength signals with motion trajectory signals extracted from video recordings by motion trackers based on adaptive block matching [33] or block motion models [34,35]. …”

“…Karayiannis, G. Tao / Image and VisionComputing 24 (2006) [27][28][29][30][31][32][33][34][35][36][37][38][39][40] …”

Quantifying Limb Movements in Epileptic Seizures Through Color-Based Video Analysis

“…This paper focuses on unobtrusive video-based seizure detection. To the best of our knowledge, the only existing system in this category is developed by Karayiannis et al for neonates [15]- [19]. They select anatomic sites on moving limbs by thresholding the motion vector magnitudes and then track the selected sites.…”

“…Furthermore, as a motion-based method, tracking tends to become unreliable over time. Thus, the maximum length of the processed video segments in [15]- [19] is only 20 s and this approach is not suitable for long-term monitoring either. This paper proposes a new unobtrusive color-based video analytic system for quantifying limb movements in epileptic seizure monitoring, as shown in Fig.…”

“…1 (a preliminary version of this work was reported in [20]). We aim to quantify movements in greater details than the motion-based system in [15]- [19] for sequences of longer durations. The proposed system requires a patient to wear pyjamas with limb portion colored, instead of attaching sensors or markers.…”