EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures

In this paper, features which are usually employed in automatic speech recognition (ASR) are used for the detection of seizures in newborn EEG. In particular, spectral envelope-based features, composed of spectral powers and their spectral derivatives are compared to the established feature set which has been previously developed forEEGanalysis.The results indicate that the ASR featureswhich model the spectral derivatives, either full-band or localized in frequency, yielded a performance improvement, in comparison to spectral-power-based features. Indeed it is shown here that they perform reasonably well in comparison with the conventional EEG feature set. The contribution of the ASR features was analyzed here using the support vector machines (SVM) recursive feature elimination technique. It is shown that the spectral derivative features consistently appear among the top-rank features. The study shows that the ASR features should be given a high priority when dealing with the description of the EEG signal.Peer ReviewedPostprint (published version

EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures

In this paper, features which are usually employed in automatic speech recognition (ASR) are used for the detection of seizures in newborn EEG. In particular, spectral envelope-based features, composed of spectral powers and their spectral derivatives are compared to the established feature set which has been previously developed forEEGanalysis.The results indicate that the ASR featureswhich model the spectral derivatives, either full-band or localized in frequency, yielded a performance improvement, in comparison to spectral-power-based features. Indeed it is shown here that they perform reasonably well in comparison with the conventional EEG feature set. The contribution of the ASR features was analyzed here using the support vector machines (SVM) recursive feature elimination technique. It is shown that the spectral derivative features consistently appear among the top-rank features. The study shows that the ASR features should be given a high priority when dealing with the description of the EEG signal.Peer Reviewe

Untargeted metabolomic analysis and pathway discovery in perinatal asphyxia and hypoxic-ischaemic encephalopathy

Elucidating metabolic effects of hypoxic-ischaemic encephalopathy (HIE) may reveal early biomarkers of injury and new treatment targets. This study uses untargeted metabolomics to examine early metabolic alterations in a carefully defined neonatal population. Infants with perinatal asphyxia who were resuscitated at birth and recovered (PA group), those who developed HIE (HIE group) and healthy controls were all recruited at birth. Metabolomic analysis of cord blood was performed using direct infusion FT-ICR mass spectrometry. For each reproducibly detected metabolic feature, mean fold differences were calculated HIE vs. controls (ΔHIE) and PA vs. controls (ΔPA). Putative metabolite annotations were assigned and pathway analysis was performed. Twenty-nine putatively annotated metabolic features were significantly different in ΔPA after false discovery correction ( q < 0.05), with eight of these also significantly altered in ΔHIE. Altered putative metabolites included; melatonin, leucine, kynurenine and 3-hydroxydodecanoic acid which differentiated between infant groups (ΔPA and ΔHIE); and D-erythrose-phosphate, acetone, 3-oxotetradecanoic acid and methylglutarylcarnitine which differentiated across severity grades of HIE. Pathway analysis revealed ΔHIE was associated with a 50% and 75% perturbation of tryptophan and pyrimidine metabolism, respectively. We have identified perturbed metabolic pathways and potential biomarkers specific to PA and HIE, which measured at birth, may help direct treatment

Performance assessment for EEG-based neonatal seizure detectors

AbstractObjectiveThis study discusses an appropriate framework to measure system performance for the task of neonatal seizure detection using EEG. The framework is used to present an extended overview of a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier.MethodsThe appropriate framework for performance assessment of neonatal seizure detectors is discussed in terms of metrics, experimental setups, and testing protocols. The neonatal seizure detection system is evaluated in this framework. Several epoch-based and event-based metrics are calculated and curves of performance are reported. A new metric to measure the average duration of a false detection is proposed to accompany the event-based metrics. A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps proposed to increase temporal precision and robustness of the system are investigated and their influence on various metrics is shown. The resulting system is validated on a large clinical dataset of 267h.ResultsIn this paper, it is shown how a complete set of metrics and a specific testing protocol are necessary to extensively describe neonatal seizure detection systems, objectively assess their performance and enable comparison with existing alternatives. The developed system currently represents the best published performance to date with an ROC area of 96.3%. The sensitivity and specificity were ∼90% at the equal error rate point. The system was able to achieve an average good detection rate of ∼89% at a cost of 1 false detection per hour with an average false detection duration of 2.7min.ConclusionsIt is shown that to accurately assess the performance of EEG-based neonatal seizure detectors and to facilitate comparison with existing alternatives, several metrics should be reported and a specific testing protocol should be followed. It is also shown that reporting only event-based metrics can be misleading as they do not always reflect the true performance of the system.SignificanceThis is the first study to present a thorough method for performance assessment of EEG-based seizure detection systems. The evaluated SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG

Supplementary Material for: Short-Term Effects of Phenobarbitone on Electrographic Seizures in Neonates

<br><strong><em>Background:</em></strong> Phenobarbitone is the most common first-line anti-seizure drug and is effective in approximately 50% of all neonatal seizures. <b><i>Objective:</i></b> To describe the response of electrographic seizures to the administration of intravenous phenobarbitone in neonates using seizure burden analysis techniques. <b><i>Methods:</i></b> Multi-channel conventional EEG, reviewed by experts, was used to determine the electrographic seizure burden in hourly epochs. The maximum seizure burden evaluated 1 h before each phenobarbitone dose (T_-1) was compared to seizure burden in periods of increasing duration after each phenobarbitone dose had been administered (T₊₁, T₊₂ to seizure offset). Differences were analysed using linear mixed models and summarized as means and 95% CI. <b><i>Results:</i></b> Nineteen neonates had electrographic seizures and met the inclusion criteria for the study. Thirty-one doses were studied. The maximum seizure burden was significantly reduced 1 h after the administration of phenobarbitone (T₊₁) [-14.0 min/h (95% CI: -19.6, -8.5); p < 0.001]. The percentage reduction was 74% (IQR: 36-100). This reduction was temporary and not significant within 4 h of administrating phenobarbitone. Subgroup analysis showed that only phenobarbitone doses at 20 mg/kg resulted in a significant reduction in the maximum seizure burden from T_-1 to T₊₁ (p = 0.002). <b><i>Conclusions:</i></b> Phenobarbitone significantly reduced seizures within 1 h of administration as assessed with continuous multi-channel EEG monitoring in neonates. The reduction was not permanent and seizures were likely to return within 4 h of treatment

A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity

The NFM is a new method of signal representation that can be used to detecting pseudo-periodicity in the neonatal EEG, using data-driven TF path integration to compress a TFD into a representation of nonstationary signal component power and mean frequency. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).Automated methods of neonatal EEG seizure detection attempt to highlight the evolving, stereotypical, pseudo-periodic, nature of EEG seizure while rejecting the nonstationary, modulated, coloured stochastic background in the presence of various EEG artefacts. An important aspect of neonatal seizure detection is, therefore, the accurate representation and detection of pseudo-periodicity in the neonatal EEG. This paper describes a method of detecting pseudo-periodic components associated with neonatal EEG seizure based on a novel signal representation; the nonstationary frequency marginal (NFM). The NFM can be considered as an alternative time-frequency distribution (TFD) frequency marginal. This method integrates the TFD along data-dependent, time-frequency paths that are automatically extracted from the TFD using an edge linking procedure and has the advantage of reducing the dimension of a TFD. The reduction in dimension simplifies the process of estimating a decision statistic designed for the detection of the pseudo-periodicity associated with neonatal EEG seizure. The use of the NFM resulted in a significant detection improvement compared to existing stationary and nonstationary methods. The decision statistic estimated using the NFM was then combined with a measurement of EEG amplitude and nominal pre- and post-processing stages to form a seizure detection algorithm. This algorithm was tested on a neonatal EEG database of 18 neonates, 826 h in length with 1389 seizures, and achieved comparable performance to existing second generation algorithms (a median receiver operating characteristic area of 0.902; IQR 0.835–0.943 across 18 neonates)

Primordial Rotating Disk Composed of ≥\geq15 Dense Star-Forming Clumps at Cosmic Dawn

International audienceEarly galaxy formation, initiated by the dark matter and gas assembly, evolves through frequent mergers and feedback processes into dynamically hot, chaotic structures. In contrast, dynamically cold, smooth rotating disks have been observed in massive evolved galaxies merely 1.4 billion years after the Big Bang, suggesting rapid morphological and dynamical evolution in the early Universe. Probing this evolution mechanism necessitates studies of young galaxies, yet efforts have been hindered by observational limitations in both sensitivity and spatial resolution. Here we report high-resolution observations of a strongly lensed and quintuply imaged, low-luminosity, young galaxy at $z=6.072$ (dubbed the Cosmic Grapes), 930 million years after the Big Bang. Magnified by gravitational lensing, the galaxy is resolved into at least 15 individual star-forming clumps with effective radii of $r_{\rm e}\simeq$ 10--60 parsec (pc), which dominate $\simeq$ 70% of the galaxy's total flux. The cool gas emission unveils a smooth, underlying rotating disk characterized by a high rotational-to-random motion ratio and a gravitationally unstable state (Toomre $Q \simeq$ 0.2--0.3), with high surface gas densities comparable to local dusty starbursts with $\simeq10^{3-5}$$M_{\odot}$/pc$^{2}$. These gas properties suggest that the numerous star-forming clumps are formed through disk instabilities with weak feedback effects. The clumpiness of the Cosmic Grapes significantly exceeds that of galaxies at later epochs and the predictions from current simulations for early galaxies. Our findings shed new light on internal galaxy substructures and their relation to the underlying dynamics and feedback mechanisms at play during their early formation phases, potentially explaining the high abundance of bright galaxies observed in the early Universe and the dark matter core-cusp problem