Summary: Purpose:The main objective of this research is the development of automated video processing and analysis procedures aimed at the recognition and characterization of the types of neonatal seizures. The long-term goal of this research is the integration of these computational procedures into the development of a stand-alone automated system that could be used as a supplement in the neonatal intensive care unit (NICU) to provide 24-h per day noninvasive monitoring of infants at risk for seizures.Methods: We developed and evaluated a variety of computational tools and procedures that may be used to carry out the three essential tasks involved in the development of a seizure recognition and characterization system: the extraction of quantitative motion information from video recordings of neonatal seizures in the form of motion-strength and motor-activity signals, the selection of quantitative features that convey some unique behavioral characteristics of neonatal seizures, and the training of artificial neural networks to distinguish neonatal seizures from random infant behaviors and to differentiate between myoclonic and focal clonic seizures.Results: The methods were tested on a set of 240 video recordings of 43 patients exhibiting myoclonic seizures (80 cases), focal clonic seizures (80 cases), and random infant movements (80 cases). The outcome of the experiments verified that opticalflow methods are promising computational tools for quantifying neonatal seizures from video recordings in the form of motionstrength signals. The experimental results also verified that the robust motion trackers developed in this study outperformed considerably the motion trackers based on predictive block matching in terms of both reliability and accuracy. The quantitative features selected from motion-strength and motor-activity signals constitute a satisfactory representation of neonatal seizures and random infant movements and seem to be complementary. Such features lead to trained neural networks that exhibit performance levels exceeding the initial goals of this study, the sensitivity goal being ≥80% and the specificity goal being ≥90%.Conclusions: The outcome of this experimental study provides strong evidence that it is feasible to develop an automated system for the recognition and characterization of the types of neonatal seizures based on video recordings. This will be accomplished by enhancing the accuracy and improving the reliability of the computational tools and methods developed during the course of the study outlined here.

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.

There are many photo-protective measures adopted for protection from the solar radiation especially the UV radiation spectrum, sunscreens being the main agents. Besides the traditional approach of topical use of sunscreens, both chemical and physical, a new approach has emerged to use systemic agents in the form of vitamins and minerals. In this review, we are describing the major aspects related to sunscreens and anti-oxidants as photo-protective measures.

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This work introduces predictive block matching, a method developed to track motion in video by exploiting the advantages of block motion estimation and adaptive block matching. The proposed method relies on a pure translation motion model to estimate the displacement of a block between two successive video frames before initiating the search for the best match of the block tracked throughout the frame sequence. The search for the best match relies on adaptive block matching, which employs an update strategy based on Kalman filtering to account for the changing appearance of the block. Predictive block matching was used to extract motor activity signals from video recordings of neonatal seizures.