Background: Heart rate (HR) patterns can inform on central nervous system dysfunction. We previously used highly comparative time series analysis (HCTSA) to identify HR patterns predicting mortality among patients in the neonatal intensive care unit (NICU) and now use this methodology to discover patterns predicting cerebral palsy (CP) in preterm infants.
Method: We studied NICU patients <37 weeks' gestation with archived every-2-s HR data throughout the NICU stay and with or without later diagnosis of CP (n = 57 CP and 1119 no CP).
Objective: Infants in the neonatal intensive care unit (NICU) are at high risk of adverse neuromotor outcomes. Atypical patterns of heart rate (HR) and pulse oximetry (SpO) may serve as biomarkers for risk assessment for cerebral palsy (CP). The purpose of this study was to determine whether atypical HR and SpO patterns in NICU patients add to clinical variables predicting later diagnosis of CP.
View Article and Find Full Text PDFThis paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform.
View Article and Find Full Text PDFObjectives: The objective of this study was to define the association between the burden of severe hypoxemia (SpO ≤70%) in the first week of life and development of severe ICH (grade III/IV) in preterm infants.
Study Design: Infants born at <32 weeks or weighing <1500 g underwent prospective SpO recording from birth through 7 days. Severe hypoxemia burden was calculated as the percentage of the error-corrected recording where SpO ≤70%.