Assessment of quality of ECG for accurate estimation of Heart Rate Variability in newborns

Heart Rate Variability has been recently used to determine the severity of Hypoxic Ischemic Encephalopathy in neonates. However, it was shown that ECG and subsequently Instantaneous Heart Rate can be heavily corrupted by artefacts which have to be manually removed. This work analyses a set of features to assess their sensitivity to normal and corrupted ECG in newborns. Specifically, the IHR signal is obtained by detecting R-Peaks using the Pan-Tompkins algorithm. Four features are extracted from both ECG and IHR signal using various temporal resolutions to discriminate normal and corrupted signal. The performance of these features in discrimination is then assessed using statistical tests.