Autonomic Nervous System in Preterm Very Low Birth Weight Neonates with Intraventricular Hemorrhage

OBJECTIVE

 Heart rate variability (HRV) indicates cardiac autonomic nerve activity and is influenced by brain damage during the neonatal period. We aimed to determine whether a correlation exists between the HRV of extremely preterm neonates and neurodevelopmental test scores.

STUDY DESIGN

 Electrocardiogram data of neonates were assessed and HRV patterns in extremely preterm neonates with severe intraventricular hemorrhage (IVH; n = 6) and those with no/mild IVH (n = 28) were compared. We analyzed the relationship between HRV and neurodevelopmental outcomes at 18 months (n = 21) and 3 years (n = 23) in extremely preterm neonates.

RESULTS

 HRV was significantly associated with IVH severity in extremely preterm neonates (p < 0.05). Neonates with severe IVH exhibited increased HR and decreased mean R-to-R interval (NN) compared with neonates with no/mild IVH. HRV parameters significantly decreased in the severe IVH group, but not in the no/mild IVH group, suggesting that both sympathetic and parasympathetic activities decreased in neonates with severe IVH. Additionally, decreased HR and increased NN were significantly related to impaired neurodevelopmental outcomes in the no/mild IVH group at corrected ages of 18 months and 3 years, respectively (all p < 0.05).

CONCLUSION

 HRV was significantly associated with IVH severity and neurodevelopmental outcome in extremely preterm neonates. HRV can distinguish extremely preterm neonates who subsequently had severe IVH from those who had no/low-grade IVH. HRV may identify extremely preterm neonates needing adjuvant neuroprotective interventions. These findings warrant further investigation in a larger population of extremely preterm neonates.

KEY POINTS

· HRV was associated with IVH severity.. · HRV can predict subsequent severe IVH in extremely preterm neonates.. · HRV are predictive of neurodevelopmental outcomes in extremely premature neonates with low-grade IVH..

Epileptic seizure prediction based on features extracted from lagged Poincaré plots

OBJECTIVE

The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR).

METHODS

In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points.

RESULTS

The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor.

CONCLUSION

The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.

Neonatal Seizure Detection Using a Wearable Multi-Sensor System

Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant's movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children's Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.

A multimodal AI system for out-of-distribution generalization of seizure identification

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

BACKGROUND

Neonatal heart rate variability (HRV) is widely used as a research tool. However, HRV calculation methods are highly variable making it difficult for comparisons between studies.

OBJECTIVES

To describe the different types of investigations where neonatal HRV was used, study characteristics, and types of analyses performed.

ELIGIBILITY CRITERIA

Human neonates ≤1 month of corrected age.

SOURCES OF EVIDENCE

A protocol and search strategy of the literature was developed in collaboration with the McGill University Health Center's librarians and articles were obtained from searches in the Biosis, Cochrane, Embase, Medline and Web of Science databases published between 1 January 2000 and 1 July 2020.

CHARTING METHODS

A single reviewer screened for eligibility and data were extracted from the included articles. Information collected included the study characteristics and population, type of HRV analysis used (time domain, frequency domain, non-linear, heart rate characteristics (HRC) parameters) and clinical applications (physiological and pathological conditions, responses to various stimuli and outcome prediction).

RESULTS

Of the 286 articles included, 171 (60%) were small single centre studies (sample size <50) performed on term infants (n=136). There were 138 different types of investigations reported: physiological investigations (n=162), responses to various stimuli (n=136), pathological conditions (n=109) and outcome predictor (n=30). Frequency domain analyses were used in 210 articles (73%), followed by time domain (n=139), non-linear methods (n=74) or HRC analyses (n=25). Additionally, over 60 different measures of HRV were reported; in the frequency domain analyses alone there were 29 different ranges used for the low frequency band and 46 for the high frequency band.

CONCLUSIONS

Neonatal HRV has been used in diverse types of investigations with significant lack of consistency in analysis methods applied. Specific guidelines for HRV analyses in neonates are needed to allow for comparisons between studies.

HRV analysis: a non-invasive approach to discriminate between newborns with and without seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:52-55. [PMID: 34891237 DOI: 10.1109/embc46164.2021.9629741]

Early neonatal seizures detection is one of the most challenging issues in Neonatal Intensive Care Units. Several EEG-based Neonatal Seizure Detectors were proposed to support the clinical staff. However, less invasive and more easily interpretable methods than EEG are still missing. In this work, we investigated if Heart Rate Variability analysis and related measures as input features of supervised classifiers could be a valid support for discriminating between newborns with seizures and seizure-free ones. The proposed methods were validated on 52 subjects (33 with seizures and 19 seizure-free) of a public dataset collected at the Helsinki University Hospital. Encouraging results are achieved using a Linear Support Vector Machine, obtaining about 87% Area Under ROC Curve. This suggests that Heart Rate Variability analysis might be a non-invasive pre-screening tool to identify newborns with seizures.Clinical Relevance- Heart Rate Variability analysis for detecting newborns with seizures in NICUs could speed up the diagnosis process and appropriate treatments for a better neurodevelopmental outcome of the infant.

Heart rate variability in neonatal seizures: Investigation and implications for management

Many factors acting during the neonatal period can affect neurological development of the infant. Neonatal seizures (NS) that frequently occur in the immature brain may influence autonomic maturation and lead to detectable cardiovascular signs. These autonomic manifestations can also have significant diagnostic and prognostic value. The analysis of Heart Rate Variability (HRV) represents the most used and feasible method to evaluate cardiac autonomic regulation. This narrative review summarizes studies investigating HRV dynamics in newborns with seizures, with the aim of highlighting the potential utility of HRV measures for seizure detection and management. While HRV analysis in critically ill newborns is influenced by many potential confounders, we suggest that it can enhance the ability to better diagnose seizures in the clinical setting. We present potential applications of the analysis of HRV, which could have a useful future role, beyond the research setting.

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) presents a simpler and more wearable alternative for long-term ambulatory monitoring. To assess the performance of EEG and ECG signals, AI systems offer a promising way forward for developing high performing models in securing both a reasonable sensitivity and specificity. There are crucial needs for these AI systems to be developed with more clinical relevance and inference generalization. In this work, we implement an ECG-specific convolutional neural network (CNN) model with residual layers and an EEG-specific convolutional long short-term memory (ConvLSTM) model. We trained, validated, and tested these models on a publicly accessible Temple University Hospital (TUH) dataset for reproducibility and performed a non-patient-specific inference-only test on patient EEG and ECG data of The Royal Prince Alfred Hospital (RPAH) in Sydney, Australia. We selected 31 adult patients to balance groups with the following seizure types: generalized, frontal, frontotemporal, temporal, parietal, and unspecific focal epilepsy. Our tests on both EEG and ECG of these patients achieve an AUC score of 0.75. Our results show ECG outperforms EEG with an average improvement of 0.21 and 0.11 AUC score in patients with frontal and parietal focal seizures, respectively.Clinical relevance-Prior research has demonstrated the value of using ECG for seizure documentation. It is believed that specific epileptic foci (seizure origin) may involve network inputs to the autonomic nervous system. Our result indicates that ECG could outperform EEG for individuals with specific seizure origin, particularly in the frontal and parietal lobes.

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.

Heart rate variability as possible marker of brain damage in neonates with hypoxic ischemic encephalopathy: a systematic review

Heart rate variability (HRV) is currently considered the most valuable non-invasive test to investigate the autonomic nervous system function, based on the fact that fast fluctuations might specifically reflect changes of sympathetic and vagal activity. An association between abnormal values of HRV and brain impairment has been reported in the perinatal period, although data are still fragmentary. Considering such association, HRV has been suggested as a possible marker of brain damage also in case of hypoxic-ischemic encephalopathy following perinatal asphyxia. The aim of the present manuscript was to review systematically the current knowledge about the use of HRV as marker of cerebral injury in neonates suffering from hypoxic-ischemic encephalopathy. Findings reported in this paper were based on qualitative analysis of the reviewed data. Conclusion: A growing body of research supports the use of HRV as non-invasive, bedside tool for the monitoring of hypoxic-ischemic encephalopathy. The currently available data about the role of HRV as prognostic tool in case of hypoxic ischemic encephalopathy are promising but require further validation by future studies. What is Known: • Heart rate variability (HRV) is a non-invasive monitoring technique to assess the autonomic nervous system activity. • A correlation between abnormal HRV and cerebral injury has been reported in the perinatal period, and HRV has been suggested as possible marker of brain damage in case of hypoxic-ischemic encephalopathy. What is New: • HRV might provide precocious information about the entity of brain injury in asphyxiated neonates and be of help to design early, specific, and personalized treatments according to severity. • Further investigations are required to confirm these preliminary data.

Heart rate variability in neonatal patients with seizures

OBJECTIVE

Seizures are frequently observed in neurological conditions affecting newborns. Since autonomic alterations are commonly associated with neonatal seizures (NS), we investigated the utility of heart rate variability (HRV) indexes of cardiac autonomic regulation for NS detection.

METHODS

HRV analysis was conducted on ECG tracings recorded during video-EEG monitoring in newborns with NS and matched-controls. The effects of gestational age on HRV were also evaluated.

RESULTS

Newborns with NS showed lower resting state HRV compared to controls. Moreover, seizure episodes were characterized by a short-lasting increase in vagal indexes of HRV. Pre-term newborns with NS had a lower HRV than full-term at rest. In pre-term newborns, no changes in HRV were observed before and during NS. On the contrary, full-term newborns showed significantly higher HRV before and during NS compared to the respective baseline values.

CONCLUSION

Our data point to resting autonomic impairment in newborns with NS. In addition, an increment in HRV has been observed during NS only in full term newborns.

SIGNIFICANCE

Although these findings do not allow validation of HRV measures for NS prediction and detection, they suggest that a putative protective vagal mechanism might be adopted when an advanced maturation of autonomic nervous system is achieved.

Autonomic Dysfunction in Neonates with Hypoxic Ischemic Encephalopathy Undergoing Therapeutic Hypothermia Impairs Physiological Responses to Routine Care Events

OBJECTIVE

To evaluate whether infants with hypoxic-ischemic encephalopathy and evidence of autonomic dysfunction have aberrant physiological responses to care events that could contribute to evolving brain injury.

STUDY DESIGN

Continuous tracings of heart rate (HR), blood pressure (BP), cerebral near infrared spectroscopy, and video electroencephalogram data were recorded from newborn infants with hypoxic-ischemic encephalopathy who were treated with hypothermia. Videos between 16 and 24 hours of age identified 99 distinct care events, including stimulating events (diaper changes, painful procedures), and vagal stimuli (endotracheal tube manipulations, pupil examinations). Pre-event HR variability was used to stratify patients into groups with impaired versus intact autonomic nervous system (ANS) function. Postevent physiological responses were compared between groups with the nearest mean classification approach.

RESULTS

Infants with intact ANS had increases in HR/BP after stimulating events, whereas those with impaired ANS showed no change or decreased HR/BP. With vagal stimuli, the HR decreased in infants with intact ANS but changed minimally in those with impaired ANS. A pupil examination in infants with an intact ANS led to a stable or increased BP, whereas the BP decreased in the group with an impaired ANS. Near infrared spectroscopy measures of cerebral blood flow/blood volume increased after diaper changes in infants with an impaired ANS, but were stable or decreased in those with an intact ANS.

CONCLUSION

HR variability metrics identified infants with impaired ANS function at risk for maladaptive responses to care events. These data support the potential use of HR variability as a real-time, continuous physiological biomarker to guide neuroprotective care in high-risk newborns.

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h⁻¹ in the recordings from patients with epilepsy and 0.19 h⁻¹ in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

Effect of Temperature on Heart Rate Variability in Neonatal ICU Patients With Hypoxic-Ischemic Encephalopathy

OBJECTIVE

To determine whether measures of heart rate variability are related to changes in temperature during rewarming after therapeutic hypothermia for hypoxic-ischemic encephalopathy.

DESIGN

Prospective observational study.

SETTING

Level 4 neonatal ICU in a free-standing academic children's hospital.

PATIENTS

Forty-four infants with moderate to severe hypoxic-ischemic encephalopathy treated with therapeutic hypothermia.

INTERVENTIONS

Continuous electrocardiogram data from 2 hours prior to rewarming through 2 hours after completion of rewarming (up to 10 hr) were analyzed.

MEASUREMENTS AND MAIN RESULTS

Median beat-to-beat interval and measures of heart rate variability were quantified including beat-to-beat interval SD, low and high frequency relative spectral power, detrended fluctuation analysis short and long α exponents (αS and αL), and root mean square short and long time scales. The relationships between heart rate variability measures and esophageal/axillary temperatures were evaluated. Heart rate variability measures low frequency, αS, and root mean square short and long time scales were negatively associated, whereas αL was positively associated, with temperature (p < 0.01). These findings signify an overall decrease in heart rate variability as temperature increased toward normothermia.

CONCLUSIONS

Measures of heart rate variability are temperature dependent in the range of therapeutic hypothermia to normothermia. Core body temperature needs to be considered when evaluating heart rate variability metrics as potential physiologic biomarkers of illness severity in hypoxic-ischemic encephalopathy infants undergoing therapeutic hypothermia.

Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update

PURPOSE

Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications.

METHOD

We performed a thorough literature review and had contact with manufacturers of commercially available devices.

RESULTS

This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component.

CONCLUSION

Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences.

Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure

A novel approach using neuronal potential similarity (NPS) of two intracranial electroencephalogram (iEEG) electrodes placed over the foci is proposed for automated early seizure detection in patients with refractory partial epilepsy. The NPS measure is obtained from the spectral analysis of space-differential iEEG signals. Ratio between the NPS values obtained from two specific frequency bands is then investigated as a robust generalized measure, and reveals invaluable information about seizure initiation trends. A threshold-based classifier is subsequently applied on the proposed measure to generate alarms. The performance of the method was evaluated using cross-validation on a large clinical dataset, involving 183 seizure onsets in 1785 h of long-term continuous iEEG recordings of 11 patients. On average, the results show a high sensitivity of 86.9% (159 out of 183), a very low false detection rate of 1.4 per day, and a mean detection latency of 13.1 s from electrographic seizure onsets, while in average preceding clinical onsets by 6.3 s. These high performance results, specifically the short detection latency, coupled with the very low computational cost of the proposed method make it adequate for using in implantable closed-loop seizure suppression systems.

Heart rate variability in encephalopathic newborns during and after therapeutic hypothermia

OBJECTIVE

To evaluate whether heart rate variability (HRV) measures are predictive of neurological outcome in babies with hypoxic ischemic encephalopathy (HIE).

STUDY DESIGN

This case-control investigation included 20 term encephalopathic newborns treated with systemic hypothermia in a regional neonatal intensive care unit. Electrocardiographic data were collected continuously during hypothermia. Spectral analysis of beat-to-beat heart rate interval was used to quantify HRV. HRV measures were compared between infants with adverse outcome (death or neurodevelopmental impairment at 15 months, n = 10) and those with favorable outcome (survivors without impairment, n = 10).

RESULT

HRV differentiated infants by outcome during hypothermia through post-rewarming, with the best distinction between groups at 24 h and after 80 h of life.

CONCLUSION

HRV during hypothermia treatment distinguished HIE babies who subsequently died or had neurodevelopmental impairment from intact survivors. This physiological biomarker may identify infants in need of adjuvant neuroprotective interventions. These findings warrant further investigation in a larger population of infants with HIE.

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.

Monitoring neonatal seizures

Neonatal seizures are a neurological emergency and prompt treatment is required. Seizure burden in neonates can be very high, status epilepticus a frequent occurrence, and the majority of seizures do not have any clinical correlate. Detection of neonatal seizures is only possible with continuous electroencephalogram (EEG) monitoring. EEG interpretation requires special expertise that is not available in most neonatal intensive care units (NICUs). As a result, a simplified method of EEG recording incorporating an easy-to-interpret compressed trend of the EEG output (amplitude integrated EEG) from one of the EEG output from one or two channels has emerged as a popular way to monitor neurological function in the NICU. This is not without limitations; short duration and low amplitude seizures can be missed, artefacts are problematic and may mimic seizure-like activity and only a restricted area of the brain is monitored. Continuous multichannel EEG is the gold standard for detecting seizures and monitoring response to therapy but expert interpretation of the EEG output is generally not available. Some centres have set up remote access for neurophysiologists to the cot-side EEG, but reliable interpretation is wholly dependent on the 24 h availability of experts, an expensive solution. A more practical solution for the NICU without such expertise is an automated seizure detection system. This review outlines the current state of the art regarding cot-side monitoring of neonatal seizures in the NICU.

Weaning infants from mechanical ventilation

Protracted mechanical ventilation is associated with increased morbidity and mortality in preterm infants and thus the earliest possible weaning from mechanical ventilation is desirable. Weaning protocols may be helpful in achieving more rapid reduction in support. There is no clear consensus regarding the level of support at which an infant is ready for extubation. An improved ability to predict when a preterm infant has a high likelihood of successful extubation is highly desirable. In this article, available evidence is reviewed and reasonable evidence-based recommendations for expeditious weaning and extubation are provided.

A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs

PURPOSE

Epilepsy is associated with near-fatal and fatal arrhythmias, and sudden unexpected death in epilepsy (SUDEP) is partly related to cardiac events. Dysfunction of the autonomous nervous system causes arrhythmias and, although previous studies have investigated the effects of epilepsy on the autonomic control of the heart, the results are still mixed regarding whether imbalance of sympathetic, vagal, or both systems is present in epilepsy, and also the importance of anticonvulsant treatment on the autonomic system. Therefore, we aimed to investigate epilepsy and its treatment impact on heart rate variability (HRV), assessed by sympathetic and parasympathetic activity expressed as low-frequency (LF) and high-frequency (HF) power spectrum, respectively.

METHOD

We performed a systematic review from the first date available to July 2011 in Medline and other databases; key search terms were "epilepsy"; "anticonvulsants"; "heart rate variability"; "vagal"; and "autonomous nervous system." Original studies that reported data and/or statistics of at least one HRV value were included, with data being extracted by two independent authors. We used a random-effects model with Hedges's g as the measurement of effect size to perform two main meta-analyses comparing LF and HF HRV values in (1) epilepsy patients versus controls; (2) patients receiving versus not receiving treatment; and (3) well-controlled versus refractory patients. Secondary analyses assessed other time- and frequency-domain measurements (nonlinear methods were not analyzed due to lack of sufficient data sets). Quality assessment of each study was verified and also meta-analytic techniques to identify and control bias. Meta-regression for age and gender was performed.

KEY FINDINGS

Initially, 366 references were identified. According to our eligibility criteria, 30 references (39 studies) were included in our analysis. Regarding HF, epilepsy patients presented lower values (g -0.69) than controls, with the 95% confidence interval (CI) ranging from -1.05 to -0.33. No significant differences were observed for LF (g -0.18; 95% CI -0.71 to 0.35). Patients receiving treatment presented HF values to those not receiving treatment (g -0.05; 95% CI -0.37 to 0.27), with a trend for having higher LF values (g 0.1; 95% CI -0.13 to 0.33), which was more pronounced in those receiving antiepileptic drugs (vs. vagus nerve stimulation). No differences were observed for well-controlled versus refractory patients, possibly due to the low number of studies. Regression for age and gender did not influence the results. Finally, secondary time-domain analyses also showed lower HRV and lower vagal activity in patients with epilepsy, as shown by the standard deviation of normal-to-normal interval (SDNN) and the root mean square of successive differences (RMSSD) indexes, respectively.

SIGNIFICANCE

We confirmed and extended the hypothesis of sympathovagal imbalance in epilepsy, as showed by lower HF, SDNN, and RMSSD values when compared to controls. In addition, there was a trend for higher LF values in patients receiving pharmacotherapy. As lower vagal (HF) and higher sympathetic (LF) tone are predictors of morbidity and mortality in cardiovascular samples, our findings highlight the importance of investigating autonomic function in patients with epilepsy in clinical practice. Assessing HRV might also be useful when planning therapeutic interventions, as some antiepileptic drugs can show hazardous effects in cardiac excitability, potentially leading to cardiac arrhythmia.