A wire-free, non-invasive, low-cost video processing-based approach to neonatal apnoea detection

In this paper, we present a non-invasive, low-cost, wire-free video processing-based approach to neonatal apnoea detection. Our method consists in evaluating the presence or absence of apnoea events through an innovative analysis of a motion signal extracted from a video live capturing or recording of a patient. In particular, we pre-process the video by a recently proposed selective magnification algorithm, which has the purpose of emphasizing respiratory movements. Subsequently, by relying on a motion detection method based on the difference of consecutive frames, we extract a signal representative of the 'quantity' of movement. Then, since breathing is characterized by periodic movements of specific body parts (e.g., the chest), using the Maximum Likelihood (ML) criterion we detect the presence or absence of a periodic component in the motion signal, so that the presence/absence of the respiratory movements and, therefore, of apnoea episodes can be inferred. Our method is tested on a newborn with recurrent apnoea events, affected by Congenital Central Hypoventilation Syndrome (CCHS). With the proposed method, we can identify 90-100% of the apnoea events detected by polysomnography, depending on the acceptable detection delay. The results, although preliminary, are thus very promising and show that apnoea events can be identified with non-invasive, low-cost, wire-free devices.