Abstract-In this paper, we consider a novel low-complexity realtime image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.Index Terms-Average luminance signal, image processing, neonatal clonic seizure, periodicity analysis.

No abstract

No abstract

In this paper we present a novel approach to early diagnosis of Necrotizing Entercolitis in premature newborns. In particular, using an infrared thermal camera, thermal image of newborn abdomen is acquired. Image processing and spatial segmentation are then used to retrieve thermal signature which is represented by a sample distribution of values from an 8-bit grey level color palette. First order statistical features are then extracted from thermal signature and are used into a classifier. Preliminary results are encouraging and show the potential use of the proposed approach for classification between healthy and sick newborns.

No abstract