A unified approach to contact-less and low-cost video processing for automatic detection of neonatal diseases characterized by specific movement patterns is presented. This disease category includes neonatal clonic seizures and apneas. Both disorders are characterized by the presence or absence, respectively, of periodic movements of parts of the body—e.g., the limbs in case of clonic seizures and the chest/abdomen in case of apneas. Therefore, one can analyze the data obtained from multiple video sensors placed around a patient, extracting relevant motion signals and estimating, using the Maximum Likelihood (ML) criterion, their possible periodicity. This approach is very versatile and allows to investigate various scenarios, including: a single Red, Green and Blue (RGB) camera, an RGB-depth sensor or a network of a few RGB cameras. Data fusion principles are considered to aggregate the signals from multiple sensors. In the case of apneas, since breathing movements are subtle, the video can be pre-processed by a recently proposed algorithm which is able to emphasize small movements. The performance of the proposed contact-less detection algorithms is assessed, considering real video recordings of newborns, in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to medical gold standard devices. The obtained results show that a video processing-based system can effectively detect the considered specific diseases, with increasing performance for increasing number of sensors.
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