A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram.

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees.

BACKGROUND AND OBJECTIVE: Efficient management of low blood pressure (BP) in preterm neonates remains challenging with considerable variability in clinical practice. There is currently no clear consensus on what constitutes a limit for low BP that is a risk to the preterm brain. It is argued that a personalised approach rather than a population based threshold is more appropriate. This work aims to assist healthcare professionals in assessing preterm wellbeing during episodes of low BP in order to decide when and whether hypotension treatment should be initiated. In particular, the study investigates the relationship between heart rate variability (HRV) and BP in preterm infants and its relevance to a short-term health outcome. METHODS: The study is performed on a large clinically collected dataset of 831 h from 23 preterm infants of less than 32 weeks gestational age. The statistical predictive power of common HRV features is first assessed with respect to the outcome. A decision support system, based on boosted decision trees (XGboost), was developed to continuously estimate the probability of neonatal morbidity based on the feature vector of HRV characteristics and the mean arterial blood pressure. RESULTS: It is shown that the predictive power of the extracted features improves when observed during episodes of hypotension. A single best HRV feature achieves an AUC of 0.87. Combining multiple HRV features extracted during hypotensive episodes with the classifier achieves an AUC of 0.97, using a leave-one-patient-out performance assessment. Finally it is shown that good performance can even be achieved using continuous HRV recordings, rather than only focusing on hypotensive events - this had the benefit of not requiring invasive BP monitoring. CONCLUSIONS: The work presents a promising step towards the use of multimodal data in providing objective decision support for the prediction of short-term outcome in preterm infants with hypotensive episodes.

Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms.

Efficient management of low blood pressure (BP) in preterm neonates remains challenging with a considerable variability in clinical practice. The ability to assess preterm wellbeing during episodes of low BP will help to decide when and whether hypotension treatment should be initiated. This work aims to investigate the relationship between heart rate variability (HRV), BP and the short-term neurological outcome in preterm infants less than 32 weeks gestational age (GA). The predictive power of common HRV features with respect to the outcome is assessed and shown to improve when HRV is observed during episodes of low mean arterial pressure (MAP) - with a single best feature leading to an AUC of 0.87. Combining multiple features with a boosted decision tree classifier achieves an AUC of 0.97. The work presents a promising step towards the use of multimodal data in building an objective decision support tool for clinical prediction of short-term outcome in preterms who suffer episodes of low BP.

Coupling between mean blood pressure and EEG in preterm neonates is associated with reduced illness severity scores.

Hypotension or low blood pressure (BP) is a common problem in preterm neonates and has been associated with adverse short and long-term neurological outcomes. Deciding when and whether to treat hypotension relies on an understanding of the relationship between BP and brain functioning. This study aims to investigate the interaction (coupling) between BP and continuous multichannel unedited EEG recordings in preterm infants less than 32 weeks of gestational age. The EEG was represented by spectral power in four frequency sub-bands: 0.3-3 Hz, 3-8 Hz, 8-15 Hz and 15-30 Hz. BP was represented as mean arterial pressure (MAP). The level of coupling between the two physiological systems was estimated using linear and nonlinear methods such as correlation, coherence and mutual information. Causality of interaction was measured using transfer entropy. The illness severity was represented by the clinical risk index for babies (CRIB II score) and contrasted to the computed level of interaction. It is shown here that correlation and coherence, which are linear measures of the coupling between EEG and MAP, do not correlate with CRIB values, whereas adjusted mutual information, a nonlinear measure, is associated with CRIB scores (r = -0.57, p = 0.003). Mutual information is independent of the absolute values of MAP and EEG powers and quantifies the level of coupling between the short-term dynamics in both signals. The analysis indicated that the dominant causality is from changes in EEG producing changes in MAP. Transfer entropy (EEG to MAP) is associated with the CRIB score (0.3-3 Hz: r = 0.428, p = 0.033, 3-8 Hz: r = 0.44, p = 0.028, 8-15 Hz: r = 0.416, p = 0.038) and indicates that a higher level of directed coupling from brain activity to blood pressure is associated with increased illness in preterm infants. This is the first study to present the nonlinear measure of interaction between brain activity and blood pressure and to demonstrate its relation to the initial illness severity in the preterm infant. The obtained results allow us to hypothesise that the normal wellbeing of a preterm neonate can be characterised by a nonlinear coupling between brain activity and MAP, whereas the presence of weak coupling with distinctive directionality of information flow is associated with an increased mortality rate in preterms.

Modelling interactions between blood pressure and brain activity in preterm neonates.

Hypotension or low blood pressure (BP) is a common problem in preterm neonates and has been associated with adverse short and long-term outcomes. Deciding when and whether to treat hypotension relies on an understanding of the relations between blood pressure and brain function. This study aims to investigate the interaction between BP and multichannel EEG in preterm infants less than 32 weeks gestational age. The mutual information is chosen to model interaction. This measure is independent of absolute values of BP and electroencephalography (EEG) power and quantifies the level of coupling between the short-term dynamics in both signals. It is shown that while adverse health conditions as measured by higher clinical risk indices for babies (CRIB II) are accompanied by consistently lower blood pressure (r=0.43), no significant correlation was observed between CRIB scores and EEG spectral power. More importantly, the chosen measure of interaction between dynamics of EEG and BP was found to be more closely related to CRIB scores (r=0.49, p-value=0.012), with higher CRIB score associated with lower levels of interaction.