Real-time automated detection of clonic seizures in newborns☆
Introduction
Seizures are the most common symptom of acute neurological disease in newborns (Volpe, 2001). The incidence rate, as reported in population-based studies, corresponds to 2.6 per 1000 live births, increasing to 11.1‰ for preterm neonates and to 13.5‰ for infants with a birth weight lower than 2500 g (Ronen et al., 1999). Therefore, this may represent a common neurological sign in the neonatal intensive care unit (NICU) and, furthermore, could carry an increased risk of long-term morbidity (Mizrahi and Clancy, 2000).
Thus, neonatal seizures have to be promptly and accurately recognized in order to establish timely treatments. Although the traditional method of diagnosis is based on Video-ElectroEncephaloGraphic (v-EEG) monitoring, EEG interpretation is a time-consuming technique requiring specialised skills, not always readily available in the neonatal intensive care setting (Ntonfo et al., 2012). Therefore, automatic and real-time diagnostic equipment able to reliably recognize neonatal seizures would be of significant value (Kilbride et al., 2009, Shah et al., 2012, Alegre and Urrestarazu, 2011). Automatic detection of seizures by analysing EEG abnormalities has been considered (Deburchgraeve et al., 2008, Cherian et al., 2011). Alternatively, the movements of the newborn’s body could be acquired through a video camera and the corresponding video signal properly processed, with the aim to detect the newborn’s “unusual” movements (Ntonfo et al., 2012). The acquisition of the motion strength (through image processing techniques) has been proposed as an expedient to detect the presence of neonatal seizures (Kilbride et al., 2009). To achieve this objective, clonic seizures were detected by analysing relevant motion trajectory features for gesture recognition (Karayiannis et al., 2006a, Karayiannis et al., 2006b, Karayiannis et al., 2006c).
In this paper, we rely on the low complexity image processing-based approach to the detection of clonic neonatal seizures proposed in (Ntonfo et al., 2012). This method consists of the extraction of an average differential luminance signal from acquired videos, where the average is carried out over all pixels of the difference between consecutive frames. Therefore, as periodic body movements lead to periodic average luminance signals, seizure detection reduces to periodicity detection. This low-complexity algorithm naturally leads to the implementation of low-cost camera-based diagnostic apparels to assist clinical practice (Kouamou et al., 2011, Ntonfo et al., 2012).
The aim of this study is to apply in a clinical setting a new real-time algorithm (Ntonfo et al., 2012) for clonic neonatal seizures detection, based on a low complexity image processing approach extracting the differential average luminance from videotaped body movements.
Section snippets
Methods
This study was conducted on video-EEG recordings collected in the neonatal seizures database elaborated by the Child Neuropsychiatry Unit of the Neuroscience Department at Parma University. This database collects all neonatal seizures of newborns consecutively admitted to the NICU of Parma University-Hospital between June 2001 and August 2012. In our Unit all newborns at high risk of seizures, on the basis of predisposing factors (such as birth asphyxia, sepsis, meningitis, metabolic disorders,
Results
The considered approach, outlined above, has been tuned in order to guarantee good sensitivity and specificity values. The ROC curves obtained are shown below (Fig. 4): they express the varying ratio between TPs (sensitivity) and false alarms (1-specificity) for single, double or triple window, respectively. In particular, an optimized value of the decision threshold has been determined by means of ROC curves, more specifically, the best value of the decision threshold has been selected as the
Discussion
Automatic detection systems have been developed to overcome limitations in neonatal seizure identification mainly linked to the difficulties in clinical recognition and the need for highly specialised expertise to correctly interpret v-EEG. Alternative strategies already used in clinical practice, for example amplitude-integrated EEG, have proven very useful but unable to completely substitute conventional v-EEG, due to various drawbacks, such as the use of a limited set of electrodes, the
Disclosure
All the authors report no conflicts of interest or financial disclosures. The manuscript does not report results of a clinical trial. This study is not industry-sponsored.
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GM Kouamou Ntonfo completed the statistical analysis.