Association of nucleated red blood cells and severity of encephalopathy in normothermic and hypothermic infants.

AIM: To determine whether hypothermia alters the discriminative ability of postnatal nucleated red blood cells (NRBCs) to distinguish between mild and moderate/severely encephalopathic infants. METHODS: A prospective cohort study recruited full-term neonates with hypoxic ischaemic encephalopathy (HIE) from 2003 to 2012 (prehypothermic and hypothermic eras). The NRBC count was analysed in the first 24 h in all infants and compared between normothermic and hypothermic cohorts. The severity of encephalopathy was categorized using both clinical Sarnat score and continuous multichannel EEG. RESULTS: Eighty-six infants with HIE were included: in the normothermic group, 19 were clinically mild, 24 moderate/severe; in the hypothermic group, 22 were mild, 21 moderate/severe encephalopathy. NRBC count discriminated between mild and moderate/severe Sarnat scores in the normothermic group (p = 0.03) but not in the hypothermic group (p = 0.9). This change was due to a decrease in NRBCs among moderately encephalopathic infants in the hypothermic cohort. CONCLUSION: Postnatal NRBCs distinguished between mild and moderate/severe encephalopathy in normothermic infants but not in infants undergoing therapeutic hypothermia. We advise caution when using postnatal blood samples to study diagnostic biomarkers for HIE without first analysing the potential impact of hypothermia upon these markers.

Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring.

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation. Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made. Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.

Heart rate variability during sleep in healthy term newborns in the early postnatal period.

Normative time- and frequency-domain heart rate variability (HRV) measures were extracted during quiet sleep (QS) and active sleep (AS) periods in 30 healthy babies. All newborn infants studied were less than 12 h old and the sleep state was classified using multi-channel video EEG. Three bands were extracted from the heart rate (HR) spectrum: very low frequency (VLF), 0.01-0.04 Hz; low frequency (LF), 0.04-0.2 Hz, and high frequency (HF), >0.2 Hz. All metrics were averaged across all patients and per sleep state to produce a table of normative values. A noticeable peak corresponding to activity in the RSA band was found in 80% patients during QS and 0% of patients during AS, although some broadband activity was observed. The majority of HRV metrics showed a statistically significant separation between QS and AS. It can be concluded that (i) activity in the RSA band is present during QS in the healthy newborn, in the first 12 h of life, (ii) HRV measures are affected by sleep state and (iii) the averaged HRV metrics reported here could assist the interpretation of HRV data from newborns with neonatal illnesses.

Bag of Patterns for Nearest Neighbour Neonatal EEG Recall.

Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Traditional search methods may take time to retrieve the archived EEGs that could provide the meaning or cause of the specific pattern, which is undesirable as time can be critical for sick neonates. If neurophysiologists had the ability to quickly recall similar patterns, the prior occurrence of the pattern may help make an earlier diagnosis. This paper presents a system that may be used to assist a clinical neurophysiologist in the recall of neonatal EEG patterns. This paper compares two brute force approaches for the task of neonatal EEG recall and looks at the performance accuracy, speed and memory requirements. This system was tested on six different neonatal EEG pattern types with 430 events in total and the results are presented in this paper.

Altered Expression of Umbilical Cord Blood Levels of miR-181b and Its Downstream Target mUCH-L1 in Infants with Moderate and Severe Neonatal Hypoxic-Ischaemic Encephalopathy.

Hypoxic-ischaemic encephalopathy (HIE) remains one of the leading causes of neurological disability worldwide. No blood biomarker capable of early detection and classification of injury severity in HIE has been identified. This study aimed to investigate the potential of miRNA-181b (miR-181b) and its downstream target, ubiquitin C-terminal hydrolase-L1 (UCH-L1), to predict the severity of HIE. Full-term infants with perinatal asphyxia were recruited at birth and observed for the development of HIE, along with healthy controls. Levels of miR-181b and messenger UCH-L1 (mUCH-L1) in umbilical cord blood were determined using qRT-PCR. In total, 131 infants; 40 control, 50 perinatal asphyxia without HIE (PA) and 41 HIE, recruited across two separate cohorts (discovery and validation) were included in this study. Significant and consistent downregulation of miR-181b was observed in infants with moderate/severe HIE compared to all other groups in both cohorts: discovery 0.25 (0.16-0.32) vs 0.61 (0.26-1.39), p = 0.027 and validation 0.33 (0.15-1.78) vs 1.2 (0.071-2.09), p = 0.035. mUCH-L1 showed increased expression in infants with HIE in both cohorts. The expression ratio of miR-181b to mUCH-L1 was reduced in those infants with moderate/severe HIE in both cohorts: discovery cohort 0.23 (0.06-0.44) vs 1.59 (0.46-2.54), p = 0.01 and validation cohort 0.41 (0.10-0.81) vs 1.38 (0.59-2.56) in all other infants, p = 0.009. We have validated consistent patterns of altered expression in miR-181b/mUCH-L1 in moderate/severe neonatal HIE which may have the potential to guide therapeutic intervention in HIE.

A comparison of quantitative EEG features for neonatal seizure detection.

OBJECTIVE: This study was undertaken to identify the best performing quantitative EEG features for neonatal seizures detection from a test set of 21. METHODS: Each feature was evaluated on 1-min, artefact-free segments of seizure and non-seizure neonatal EEG recordings. The potential utility of each feature for neonatal seizure detection was determined using receiver operating characteristic analysis and repeated measures t-tests. A performance estimate of the feature set was obtained using a cross-fold validation and combining all features together into a linear discriminant classifier model. RESULTS: Significant differences between seizure and non-seizure segments were found in 19 features for 17 patients. The best performing features for this application were the RMS amplitude, the line length and the number of local maxima and minima. An estimate of the patient independent classifier performance yielded a sensitivity of 81.08% and specificity of 82.23%. CONCLUSIONS: The individual performances of 21 quantitative EEG features in detecting electrographic seizure in the neonate were compared and numerically quantified. Combining all features together into a classifier model led to superior performance than that provided by any individual feature taken alone. SIGNIFICANCE: The results documented in this study may provide a reference for the optimum quantitative EEG features to use in developing and enhancing neonatal seizure detection algorithms.

Behaviour of spectral entropy, spectral edge frequency 90%, and alpha and beta power parameters during low-dose propofol infusion.

BACKGROUND: In this study we analyse the behaviour, potential clinical application and optimal cortical sampling location of the spectral parameters: (i) relative alpha and beta power; (ii) spectral edge frequency 90%; and (iii) spectral entropy as monitors of moderate propofol-induced sedation. METHODS: Multi-channel EEG recorded from 12 ASA 1 (American Society of Anesthesiologists physical status 1) patients during low-dose, target effect-site controlled propofol infusion was used for this analysis. The initial target effect-site concentration was 0.5 microg ml(-1) and increased at 4 min intervals in increments of 0.5 to 2 microg ml(-1). EEG parameters were calculated for 2 s epochs in the frequency ranges 0.5-32 and 0.5-47 Hz. All parameters were calculated in the channels: P4-O2, P3-O1, F4-C4, F3-C3, F3-F4, and Fp1-Fp2. Sedation was assessed clinically using the OAA/S (observer's assessment of alertness/sedation) scale. RESULTS: Relative beta power and spectral entropy increased with increasing propofol effect-site concentration in both the 0.5-47 Hz [F(18, 90) = 3.455, P<0.05 and F(18, 90) = 3.33, P<0.05, respectively] and 0.5-32 Hz frequency range. This effect was significant in each individual channel (P<0.05). No effect was seen of increasing effect-site concentration on relative power in the alpha band. Averaged across all channels, spectral entropy did not outperform relative beta power in either the 0.5-32 Hz [Pk=0.79 vs 0.814 (P>0.05)] or 0.5-47 Hz range [Pk=0.81 vs 0.82 (P>0.05)]. The best performing indicator in any single channel was spectral entropy in the frequency range 0.5-47 Hz in the frontal channel F3-F4 (Pk=0.85). CONCLUSIONS: Relative beta power and spectral entropy when considered over the propofol effect-site range studied here increase in value, and correlate well with clinical assessment of sedation.

Classifier models and architectures for EEG-based neonatal seizure detection.

Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with a poor long-term outcome. Early detection and treatment may improve prognosis. This paper aims to develop an optimal set of parameters and a comprehensive scheme for patient-independent multi-channel EEG-based neonatal seizure detection. We employed a dataset containing 411 neonatal seizures. The dataset consists of multi-channel EEG recordings with a mean duration of 14.8 h from 17 neonatal patients. Early-integration and late-integration classifier architectures were considered for the combination of information across EEG channels. Three classifier models based on linear discriminants, quadratic discriminants and regularized discriminants were employed. Furthermore, the effect of electrode montage was considered. The best performing seizure detection system was found to be an early integration configuration employing a regularized discriminant classifier model. A referential EEG montage was found to outperform the more standard bipolar electrode montage for automated neonatal seizure detection. A cross-fold validation estimate of the classifier performance for the best performing system yielded 81.03% of seizures correctly detected with a false detection rate of 3.82%. With post-processing, the false detection rate was reduced to 1.30% with 59.49% of seizures correctly detected. These results represent a comprehensive illustration that robust reliable patient-independent neonatal seizure detection is possible using multi-channel EEG.

An Approximate Nearest Neighbour System For Neonatal EEG Recall.

Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Traditional search methods may take time to retrieve the archived EEGs that could provide the meaning or cause of the specific pattern which is not acceptable as time can be critical for sick neonates. If neurophysiologists had the ability to quickly recall similar patterns, the prior occurrence of the pattern may help make an earlier diagnosis. This paper presents a system that may be used to assist a clinical neurophysiologist in the recall of neonatal EEG patterns. The proposed system consists of an alignment technique followed by an approximate nearest neighbour search algorithm called locality sensitive hashing. The system was tested on six different neonatal EEG pattern types with 430 events in total and the results are presented in this paper.

Mild hypoxic ischaemic encephalopathy and long term neurodevelopmental outcome - A systematic review.

AIMS: Hypoxic ischaemic encephalopathy (HIE) remains a significant cause of long term neurodisability despite therapeutic hypothermia (TH). Infants with mild HIE, representing 50% of those with HIE, are perceived as low risk and are currently not eligible for TH [1]. This review examines the available evidence of outcome in term infants with mild HIE. METHODS: Medline, Embase and Cochrane Clinical Trials databases were searched in March 2017. Studies with well-defined HIE grading at birth and standardised neurodevelopmental assessment at ≥18 months were included. Abnormal outcome was defined as death, cerebral palsy or standardised neurodevelopmental test score more than 1 standard deviation below the mean. RESULT: Twenty studies were included. Abnormal outcome was reported in 86/341 (25%) of infants. There was insufficient evidence to examine the effect of TH on outcome. CONCLUSION: A significant proportion of infants with mild HIE have abnormal outcome at follow up.

Monitoring cerebral oxygenation of preterm infants using a neonatal specific sensor.

INTRODUCTION: Cerebral oxygenation (rcSO2) monitoring in preterm infants may identify periods of cerebral hypoxia or hyperoxia. We hypothesised that there was a relationship between rcSO2 values and short term outcome in infants of GA < 32weeks. METHODS: RcSO2 values were recorded for the first 48 h of life using an INVOS monitor with a neonatal sensor. The association between cranial ultrasound scan measured brain injury and rcSO2 was assessed. RESULTS: 120 infants were included. Sixty-nine percent (83) of infants had a normal outcome (no IVH, no PVL, and survival at 1 month); less than one-quarter, 22% (26), had low grade IVH 1 or 2 (moderate outcome); and 9% (11) of infants had a severe outcome (IVH ≥ 3, PVL or died before 1 month age). rcSO2 values were lower for infants GA < 28weeks when compared with those GA 28-32, p < 0.001. There was no difference in absolute rcSO2 values between the three outcome groups but a greater degree of cerebral hypoxia was associated with preterm infants who had low grade 1 or 2 IVH. CONCLUSION: Infants of GA < 28 weeks have lower cerebral oxygenation in the first 2 days of life. A greater degree of hypoxia was seen in infants with grade 1 or 2 haemorrhage. Normative ranges need to be gestation specific.

Downstream mRNA Target Analysis in Neonatal Hypoxic-Ischaemic Encephalopathy Identifies Novel Marker of Severe Injury: a Proof of Concept Paper.

Human microRNA miR-374a is downregulated in the umbilical cord blood (UCB) of infants with hypoxic-ischaemic encephalopathy (HIE). The downstream targets of this microRNA (miRNA) are unclear, but one putative target is the activin-A receptor type IIb (ACVR2B). ACVR2B is required for activin-A function and previous reports have shown alterations of activin-A levels in neonatal HIE. Our aim was to investigate the expression of the potential downstream targets of miR-374a, activin-A and ACVR2B, at birth in a cohort of full-term infants with perinatal asphyxia (PA) only, and those with PA who developed clinical and electrographic HIE. UCB was drawn and processed immediately after delivery. Levels of serum activin-A were measured using ELISA. mRNA levels of ACVR2B in whole blood were quantified using qRT-PCR. Outcome was assessed at 3 years of age using standardised developmental assessment. In total, 171 infants were enrolled: 88 healthy controls, 56 PA and 27 HIE. A statistically significant elevation of median (IQR) ACVR2B was detected in infants with severe HIE compared to moderate/mild HIE, PA and control groups (3.3 (2.94-3.67) vs. 0.91 (0.55-1.21) vs. 0.88 (0.57-1.38) vs. 0.84 (0.74-1.24), p values = 0.04, 0.027 and 0.025, respectively). Although serum activin-A levels were elevated in infants with severe HIE, this elevation did not reach significance. ACVR2B may be a potential novel marker of HIE severity. This is the first study to examine the relationship between activin-A, its receptor AVCR2B and potentially upstream miRNA miR-374a in a cohort of carefully categorised and phenotyped infants. We have shown that miRNA analysis, combined with downstream target exploration, may yield novel biomarkers for the prediction of HIE severity.

Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram.

OBJECTIVE: To develop an automated estimate of EEG maturational age (EMA) for preterm neonates. METHODS: The EMA estimator was based on the analysis of hourly epochs of EEG from 49 neonates with gestational age (GA) ranging from 23 to 32weeks. Neonates had appropriate EEG for GA based on visual interpretation of the EEG. The EMA estimator used a linear combination (support vector regression) of a subset of 41 features based on amplitude, temporal and spatial characteristics of EEG segments. Estimator performance was measured with the mean square error (MSE), standard deviation of the estimate (SD) and the percentage error (SE) between the known GA and estimated EMA. RESULTS: The EMA estimator provided an unbiased estimate of EMA with a MSE of 82days (SD=9.1days; SE=4.8%) which was significantly lower than a nominal reading (the mean GA in the dataset; MSE of 267days, SD of 16.3days, SE=8.4%: p<0.001). The EMA estimator with the lowest MSE used amplitude, spatial and temporal EEG characteristics. CONCLUSIONS: The proposed automated EMA estimator provides an accurate estimate of EMA in early preterm neonates. SIGNIFICANCE: Automated analysis of the EEG provides a widely accessible, noninvasive and continuous assessment of functional brain maturity.

In-depth performance analysis of an EEG based neonatal seizure detection algorithm.

OBJECTIVE: To describe a novel neurophysiology based performance analysis of automated seizure detection algorithms for neonatal EEG to characterize features of detected and non-detected seizures and causes of false detections to identify areas for algorithmic improvement. METHODS: EEGs of 20 term neonates were recorded (10 seizure, 10 non-seizure). Seizures were annotated by an expert and characterized using a novel set of 10 criteria. ANSeR seizure detection algorithm (SDA) seizure annotations were compared to the expert to derive detected and non-detected seizures at three SDA sensitivity thresholds. Differences in seizure characteristics between groups were compared using univariate and multivariate analysis. False detections were characterized. RESULTS: The expert detected 421 seizures. The SDA at thresholds 0.4, 0.5, 0.6 detected 60%, 54% and 45% of seizures. At all thresholds, multivariate analyses demonstrated that the odds of detecting seizure increased with 4 criteria: seizure amplitude, duration, rhythmicity and number of EEG channels involved at seizure peak. Major causes of false detections included respiration and sweat artefacts or a highly rhythmic background, often during intermediate sleep. CONCLUSION: This rigorous analysis allows estimation of how key seizure features are exploited by SDAs. SIGNIFICANCE: This study resulted in a beta version of ANSeR with significantly improved performance.

Application of audio fingerprinting to neonatal EEG.

A clinical neurophysiologist must recognize patterns in EEG signals to evaluate the health of a patient's brain activity. Rare or unusual patterns may take time to correctly identify. The ability to automatically assist this recall would be beneficial in ensuring that appropriate measures could be taken in a timely fashion. Audio fingerprinting is a method used to identify songs using only a snippet of the song. Fingerprints are extracted from a sub-section of the song and matched against a database of previously computed fingerprints. In this paper, a fingerprint quantization technique is implemented on neonatal EEG data to attempt to identify sections of EEG data when only seeing a sub-section of the data. The impact of signal distortions is investigated and results from a database of one hour recordings from 40 newborns are presented.

Early Cord Metabolite Index and Outcome in Perinatal Asphyxia and Hypoxic-Ischaemic Encephalopathy.

BACKGROUND: A 1H-NMR-derived metabolomic index based on early umbilical cord blood alterations of succinate, glycerol, 3-hydroxybutyrate and O-phosphocholine has shown potential for the prediction of hypoxic-ischaemic encephalopathy (HIE) severity. OBJECTIVE: To evaluate whether this metabolite score can predict 3-year neurodevelopmental outcome in infants with perinatal asphyxia and HIE, compared with current standard biochemical and clinical markers. METHODS: From September 2009 to June 2011, infants at risk of perinatal asphyxia were recruited from a single maternity hospital. Cord blood was drawn and biobanked at delivery. Neonates were monitored for development of encephalopathy both clinically and electrographically. Neurodevelopmental outcome was assessed at 36-42 months using the Bayley Scales of Infant and Toddler Development, ed. III (BSID-III). Death and cerebral palsy were also considered as abnormal end points. RESULTS: Thirty-one infants had both metabolomic analysis and neurodevelopmental outcome at 36-42 months. No child had a severely abnormal BSID-III result. The metabolite index significantly correlated with outcome (ρ2 = 0.30, p < 0.01), which is robust to predict both severe outcome (area under the receiver operating characteristic curve: 0.92, p < 0.01) and intact survival (0.80, p = 0.01). There was no correlation between the index score and performance in the individual BSID-III subscales (cognitive, language, motor). CONCLUSIONS: The metabolite index outperformed other standard biochemical markers at birth for prediction of outcome at 3 years, but was not superior to EEG or the Sarnat score.

Stream computing for biomedical signal processing: A QRS complex detection case-study.

Recent developments in "Big Data" have brought significant gains in the ability to process large amounts of data on commodity server hardware. Stream computing is a relatively new paradigm in this area, addressing the need to process data in real time with very low latency. While this approach has been developed for dealing with large scale data from the world of business, security and finance, there is a natural overlap with clinical needs for physiological signal processing. In this work we present a case study of streams processing applied to a typical physiological signal processing problem: QRS detection from ECG data.

The effect of haemolysis on the metabolomic profile of umbilical cord blood.

OBJECTIVES: Metabolomics is defined as the comprehensive study of all low molecular weight biochemicals, (metabolites) present in an organism. Using a systems biology approach, metabolomics in umbilical cord blood (UCB) may offer insight into many perinatal disease processes by uniquely detecting rapid biochemical pathway alterations. In vitro haemolysis is a common technical problem affecting UCB sampling in the delivery room, and can hamper metabolomic analysis. The extent of metabolomic alteration which occurs in haemolysed samples is unknown. DESIGN AND METHODS: Visual haemolysis was designated by the laboratory technician using a standardised haemolysis index colour chart. The metabolomic profile of haemolysed and non-haemolysed UCB serum samples from 69 healthy term infants was compared using both (1)H-NMR and targeted DI and LC-MS/MS approach. RESULTS: We identified 43 metabolites that are significantly altered in visually haemolysed UCB samples, acylcarnitines (n=2), glycerophospholipids (n=23), sphingolipids (n=7), sugars (n=3), amino acids (n=4) and Krebs cycle intermediates (n=4). CONCLUSION: This information will be useful for researchers in the field of neonatal metabolomics to avoid false findings in the presence of haemolysis, to ensure reproducible and credible results.

Early serial EEG in hypoxic ischaemic encephalopathy.

OBJECTIVES: To perform early serial EEGs in infants with hypoxic ischaemic encephalopathy (HIE) and compare the findings with neurodevelopmental outcome. METHODS: Nine full-term neonates with HIE had simultaneous video-EEG polygraphic studies within 8 h of birth. The EEG was repeated at 12-24 h intervals. All surviving infants had a neurodevelopmental assessment at 1 year. RESULTS: Two infants had a normal or mildly abnormal EEG within 8 h of birth and neurodevelopmental outcome was normal. Seven infants had severely depressed background activity in the first 8 h of life. In 3 infants the EEG activity recovered within 12-24 h showing continuous activity with no or only minor abnormalities. All these infants had a normal outcome. The remaining 4 infants, who also had an initially inactive recording, subsequently developed severe background abnormalities. At follow-up, two infants had died and the remainder developed major neurological sequelae. CONCLUSIONS: Early EEG is an excellent prognostic indicator for a favourable outcome if normal within the first 8 h of life and for a poor outcome if the background activity continues to be inactive or grossly abnormal beyond 8-12 h of life. However, an inactive or very depressed EEG within the first 8 h of life can be associated with good outcome if the EEG activity recovers within 12 h.

Phenobarbitone, neonatal seizures, and video-EEG.

AIMS: To evaluate the effectiveness of phenobarbitone as an anticonvulsant in neonates. METHODS: An observational study using video-EEG telemetry. Video-EEG was obtained before treatment was started, for an hour after treatment was given, two hours after treatment was given, and again between 12 and 24 hours after treatment was given. Patients were recruited from all babies who required phenobarbitone (20-40 mg/kg intravenously over 20 minutes) for suspected clinical seizures and had EEG monitoring one hour before and up to 24 hours after the initial dose. An EEG seizure discharge was defined as a sudden repetitive stereotyped discharge lasting for at least 10 seconds. Neonatal status epilepticus was defined as continuous seizure activity for at least 30 minutes. Seizures were categorised as EEG seizure discharges only (electrographic), or as EEG seizure discharges with accompanying clinical manifestations (electroclinical). Surviving babies were assessed at one year using the Griffiths neurodevelopmental score. RESULTS: Fourteen babies were studied. Four responded to phenobarbitone; these had normal or moderately abnormal EEG background abnormalities and outcome was good. In the other 10 babies electrographic seizures increased after treatment, whereas electroclinical seizures reduced. Three babies were treated with second line anticonvulsants, of whom two responded. One of these had a normal neurodevelopmental score at one year, but the outcome for the remainder of the whole group was poor. CONCLUSION: Phenobarbitone is often ineffective as a first line anticonvulsant in neonates with seizures in whom the background EEG is significantly abnormal.