Designing a trial for neonatal seizure treatment

Neonatal seizures are widely considered a neurological emergency with a need for prompt treatment, yet they are known to present a highly elusive target for bedside clinicians. Recent studies have suggested that the design of a neonatal seizure treatment trial will profoundly influence the sample size, which may readily increase to hundreds or even thousands as the achieved effect size diminishes to clinical irrelevance. The self-limiting and rapidly resolving nature of neonatal seizures diminishes the measurable treatment effect every hour after seizure onset and any effect may potentially be confused with spontaneous resolution, precluding the value of many observational studies. The large individual variability in seizure occurrence over time and between etiologies challenges group comparisons, while the absence of clinical signs mandates quantification of seizure occurrence with continuous multi-channel EEG monitoring. A biologically sound approach that views neonatal seizures as a functional cot-side biomarker rather than an object to treat can overcome these challenges. (C) 2018 Elsevier Ltd. All rights reserved.Peer reviewe

The effect of reducing EEG electrode number on the visual interpretation of the human expert for neonatal seizure detection

Objectives: To measure changes in the visual interpretation of the EEG by the human expert for neonatal seizure detection when reducing the number of recording electrodes. Methods: EEGs were recorded from 45 infants admitted to the neonatal intensive care unit (NICU). Three experts annotated seizures in EEG montages derived from 19, 8 and 4 electrodes. Differences between annotations were assessed by comparing intra-montage with inter-montage agreement (K). Results: Three experts annotated 4464 seizures across all infants and montages. The inter-expert agreement was not significantly altered by the number of electrodes in the montage (p = 0.685, n = 43). Reducing the number of EEG electrodes altered the seizure annotation for all experts. Agreement between the 19-electrode montage (K-19,K-19 = 0.832) was significantly higher than the agreement between 19 and 8-electrode montages (dK = 0.114; p <0.001, n = 42) or 19 and 4-electrode montages (dK = 0.113, p <0.001, n = 43). Seizure burden and number were significantly underestimated by the 4 and 8-electrode montage (p <0.001). No significant difference in agreement was found between 8 and 4-electrode montages (dK = 0.002; p = 0.07, n = 42). Conclusions: Reducing the number of EEG electrodes from 19 electrodes resulted in slight but significant changes in seizure detection. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.Peer reviewe

Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection

The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-frequency domain (time-frequency correlation; TFC). These measures were incorporated into a seizure detection algorithm (SDA) based on a support vector machine to detect periods of seizure and nonseizure. The performance of these nonstationary correlation measures was evaluated using EEG recordings from 79 term neonates annotated by three human experts. The proposed measures were highly discriminative for seizure detection (median AUC(SC): 0.933 IQR: 0.821-0.975, median AUC(TFC): 0.883 IQR: 0.707-0.931). The resultant SDA applied to multi-channel recordings had a median AUC of 0.988 (IQR: 0.931-0.998) when compared to consensus annotations, outperformed two state-of-the-art SDAs (p <0.001) and was noninferior to the human expert for 73/79 of neonates.Peer reviewe

Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

Objective To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. Methods A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysomnography recordings. In addition to training and validating a single EEG channel quiet sleep, we constructed Sleep State Trend (SST), a bedside-ready means for visualising classifier outputs. Results The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalised well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualisation of the classifier output. Conclusions Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualised as a transparent and intuitive trend in the bedside monitors. Significance The Sleep State Trend (SST) may provide caregivers a real-time view of sleep state fluctuations and its cyclicity.Peer reviewe

Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy

Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human expert annotation. Measures of algorithm generalizability and non-inferiority as well as measures of clinical efficacy are needed to assess the full scope of neonatal SDA performance. We validated our neonatal SDA on an independent data set of 28 neonates. Generalizability was tested by comparing the performance of the original training set (cross -validation) to its performance on the validation set. Non-inferiority was tested by assessing inter-observer agreement between combinations of SDA and two human expert annotations. Clinical efficacy was tested by comparing how the SDA and human experts quantified seizure burden and identified clinically significant periods of seizure activity in the EEG. Algorithm performance was consistent between training and validation sets with no significant worsening in AUC (p > 0.05, n = 28). SDA output was inferior to the annotation of the human expert, however, re-training with an increased diversity of data resulted in non-inferior performance (delta kappa = 0.077, 95% CI:-0.002-0.232, n = 18). The SDA assessment of seizure burden had an accuracy ranging from 89 to 93%, and 87% for identifying periods of clinical interest. The proposed SDA is approaching human equivalence and provides a clinically relevant interpretation of the EEG.Peer reviewe

Automated detection of artefacts in neonatal EEG with residual neural networks

Background and objective: To develop a computational algorithm that detects and identifies different arte-fact types in neonatal electroencephalography (EEG) signals. Methods: As part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Per-formance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm. Results: The Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact. Conclusion: Artefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms. (c) 2021 Elsevier B.V. All rights reserved.Peer reviewe

Precision control of thermal transport in cryogenic single-crystal silicon devices

We report on the diffusive-ballistic thermal conductance of multi-moded single-crystal silicon beams measured below 1 K. It is shown that the phonon mean-free-path $\ell$ is a strong function of the surface roughness characteristics of the beams. This effect is enhanced in diffuse beams with lengths much larger than $\ell$, even when the surface is fairly smooth, 5-10 nm rms, and the peak thermal wavelength is 0.6 $\mu$m. Resonant phonon scattering has been observed in beams with a pitted surface morphology and characteristic pit depth of 30 nm. Hence, if the surface roughness is not adequately controlled, the thermal conductance can vary significantly for diffuse beams fabricated across a wafer. In contrast, when the beam length is of order $\ell$, the conductance is dominated by ballistic transport and is effectively set by the beam area. We have demonstrated a uniformity of $\pm$8% in fractional deviation for ballistic beams, and this deviation is largely set by the thermal conductance of diffuse beams that support the micro-electro-mechanical device and electrical leads. In addition, we have found no evidence for excess specific heat in single-crystal silicon membranes. This allows for the precise control of the device heat capacity with normal metal films. We discuss the results in the context of the design and fabrication of large-format arrays of far-infrared and millimeter wavelength cryogenic detectors

Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants bornPeer reviewe