A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively)

Determination and evaluation of clinically efficient stopping criteria for the multiple auditory steady-state response technique

Background: Although the auditory steady-state response (ASSR) technique utilizes objective statistical detection algorithms to estimate behavioural hearing thresholds, the audiologist still has to decide when to terminate ASSR recordings introducing once more a certain degree of subjectivity. Aims: The present study aimed at establishing clinically efficient stopping criteria for a multiple 80-Hz ASSR system. Methods: In Experiment 1, data of 31 normal hearing subjects were analyzed off-line to propose stopping rules. Consequently, ASSR recordings will be stopped when (1) all 8 responses reach significance and significance can be maintained for 8 consecutive sweeps; (2) the mean noise levels were ≤ 4 nV (if at this “≤ 4-nV” criterion, p-values were between 0.05 and 0.1, measurements were extended only once by 8 sweeps); and (3) a maximum amount of 48 sweeps was attained. In Experiment 2, these stopping criteria were applied on 10 normal hearing and 10 hearing-impaired adults to asses the efficiency. Results: The application of these stopping rules resulted in ASSR threshold values that were comparable to other multiple-ASSR research with normal hearing and hearing-impaired adults. Furthermore, in 80% of the cases, ASSR thresholds could be obtained within a time-frame of 1 hour. Investigating the significant response-amplitudes of the hearing-impaired adults through cumulative curves indicated that probably a higher noise-stop criterion than “≤ 4 nV” can be used. Conclusions: The proposed stopping rules can be used in adults to determine accurate ASSR thresholds within an acceptable time-frame of about 1 hour. However, additional research with infants and adults with varying degrees and configurations of hearing loss is needed to optimize these criteria

Amplitude-integrated EEG assists in detecting cerebral dysfunction in the newborn

Background: Amplitude-integrated encephalography (aEEG) in term-born encephalopathic infants has been shown to be predictive of later neurodevelopmental outcomes, but little is known about the mediating cerebral pathology. In addition, the aEEG is commonly used to monitor electrographic seizures in the newborn, an important manifestation of cerebral pathology, but there is limited data on it’s efficacy for this purpose. It’s clinical application in the preterm infant remains to be explored. Aim: The central aim of this thesis is to prove the hypothesis that the aEEG assists in detecting cerebral dysfunction in the newborn. Methods: 1) In a cohort of term-born infants with encephalopathy and/or seizures digital aEEG background measures of the lower and upper aEEG margins were related to a numeric MRI abnormality score. 2) In at-risk term newborns, the accuracy of two-channel digital aEEG monitoring was compared with continuous concurrent conventional EEG for seizure detection. 3) In preterm infants (gestation at birth < 30 weeks) aEEG measures of lower and upper margin collected in the first week of life were compared in infants with substantial cerebral abnormality to infants without. Results: 1) For all infants in the term cohort, the severity of abnormality of aEEG background was strongly related to severity of abnormality seen on cerebral MRI. 2) Using the aEEG pattern with the raw EEG signal, 76% of electrographic seizures were correctly identified in the term infants. 3) In the preterm cohort, the lower and upper aEEG amplitude margins increased significantly during the first week of life. In the presence of substantial cerebral abnormality, these margins were significantly depressed. Seizures were noted in the smaller and sicker, infants. Conclusion: The central hypothesis of this thesis, that the aEEG assists in detecting cerebral dysfunction in the newborn was proved

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

Cerebral autoregulation, brain injury, and the transitioning premature infant

Improvements in clinical management of the preterm infant have reduced the rates of the two most common forms of brain injury, such as severe intraventricular hemorrhage and white matter injury, both of which are contributory factors in the development of cerebral palsy. Nonetheless, they remain a persistent challenge and are associated with a significant increase in the risk of adverse neurodevelopment outcomes. Repeated episodes of ischemia–reperfusion represent a common pathway for both forms of injury, arising from discordance between systemic blood flow and the innate regulation of cerebral blood flow in the germinal matrix and periventricular white matter. Nevertheless, establishing firm hemodynamic boundaries, as a part of neuroprotective strategy, has challenged researchers. Existing measures either demonstrate inconsistent relationships with injury, as in the case of mean arterial blood pressure, or are not feasible for long-term monitoring, such as cardiac output estimated by echocardiography. These challenges have led some researchers to focus on the mechanisms that control blood flow to the brain, known as cerebrovascular autoregulation. Historically, the function of the cerebrovascular autoregulatory system has been difficult to quantify; however, the evolution of bedside monitoring devices, particularly near-infrared spectroscopy, has enabled new insights into these mechanisms and how impairment of blood flow regulation may contribute to catastrophic injury. In this review, we first seek to examine how technological advancement has changed the assessment of cerebrovascular autoregulation in premature infants. Next, we explore how clinical factors, including hypotension, vasoactive medications, acute and chronic hypoxia, and ventilation, alter the hemodynamic state of the preterm infant. Additionally, we examine how developmentally linked or acquired dysfunction in cerebral autoregulation contributes to preterm brain injury. In conclusion, we address exciting new approaches to the measurement of autoregulation and discuss the feasibility of translation to the bedside

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

Peer reviewe

Neuromonitoring during newborn transition

Background: Newborn infant neurological function can be measured by monitoring electrical activity (electroencephalography) or cerebral oxygenation via NIRS (near infrared spectroscopy). In practice the clinical applications of electroencephalography (EEG) are limited to monitoring infants following moderate to severe hypoxic ischemic injury (HIE), and for the detection of seizures in at risk infants. NIRS monitoring has been the focus of a number of research trials but has no clinical applications in the immediate newborn period to date, and is not routinely performed in neonatal units. Aim: To assess the feasibility of infant neuromonitoring in the immediate period in two important clinical scenarios. Firstly, to assess the feasibility of monitoring brain activity during the first minutes of life in healthy term infants. Secondly, to assess the feasibility and utility of monitoring newborn preterm infants’ brain activity and cerebral oxygenation in the context of an interventional randomized controlled trial. Methods: 1. Healthy term newborn infants had EEG monitoring performed for the first ten minutes of life. EEG was assessed both qualitatively and quantitatively. All infants had respiratory function monitoring performed simultaneously. 2. Forty-five infants (< 32 weeks gestation) were randomly assigned to different methods of newborn infant cord clamping. All infants had EEG and NIRS monitoring for the first 72 hours of life. Quantitative features of EEG and median NIRS values were compared between groups at 6 and 12 hours of life as a primary outcome measure. Results: 1. Forty-nine infants had EEG recordings. Median (IQR) age at time of initial EEG recording was 3.0 (2·5 to 3·8) minutes. End tidal CO2 and tidal volumes increased over the first 3 minutes of life and then stabilized. Good quality EEG, with continuous mixed frequency activity with a range of 25-50μV, was observed in all infants. The majority of EEG spectral power was within the delta band. 2. There were 45 infants included. One infant died in the delivery room. Median time (IQR) from birth until EEG application was 3.05 (1.85 to 5.38) hrs. For primary outcome measures, data was available for 42/44 (95%) at 6 hrs and 44/44 (100%) at 12 hours. There was no significant difference between groups for measures for EEG values or cerebral NIRS. Conclusion: Infant neuromonitoring in the immediate newborn period is feasible in the first minutes of life in healthy term infants and within the first hours of life in preterm infants. Normative quantitative data for electrical activity in healthy newborn term infants during the first minutes of life is described for the first time. Neuromonitoring during the first day of life as an outcome measure for preterm interventional trials is possible and the outcomes from this research is promising for further trials

Study of the Term Neonatal Brain Injury with combined Diffuse Optical Tomography and Electroencephalography

This thesis describes the application of combined diffuse optical tomography (DOT) and electroencephalography (EEG) in the investigation of neonatal term brain injury. With hypoxic ischaemic encephalopathy (HIE) and perinatal stroke being the most frequent contributors to brain injury in the term neonatal population, the first part of the thesis focuses on the description and ongoing requirement for their further investigation. In continuation to that, the characteristics and unique properties of both DOT and EEG are described and explored. The combination of these two modalities was utilised in elucidating the relationship between neuronal activity and cerebral haemodynamics both in physiological processes as well as in disease, by the infant’s cot side. This work differs to previous studies using near-infrared technologies and EEG, as a denser whole head array was used, offering the potential of 3-dimensional image reconstruction of the cortical haemodynamic events in relation to electro-cortical activity. These methods were applied in the study of critically ill infants presenting with seizures in the first few days of life. The relevant results are presented in three separate chapters of the thesis. Distinct neurophysiological phenomena such as seizures and burst suppression were detected and studied in association to underlying HIE. On the grounds of a pre-existing pilot study of our research group, distinct prolonged de-oxygenated cortical areas were identified following electrical seizure activity. Further exploration of infants with seizures provided limited supporting evidence. The investigation of burst suppression in HIE led to the first ever identification of repeated, waveform, cortical haemodynamic events in response to bursts of electrical activity with some spatial correlation to regions of brain injury. Further analysis of the low frequencies within the diffuse optical signal in cases of perinatal stroke, showed a consistent interhemispheric difference between the healthy and stroke-affected brain regions. The limitations, prospects and conclusions are presented in the final chapter. The use of simultaneous DOT and EEG offers a unique neuro-monitoring and neuro-investigating tool in the neonatal intensive care environment, which is safe, portable, and cost-effective, Ongoing research is required for the exploration and development of the methodology and its potential diagnostic properties

Laskennalliset muuttujat vastasyntyneen aivomonitoroinnin arvioinnissa

The aim of this thesis was to find out if computational electroencephalography (EEG) features can be used in the automated monitoring of newborns after asphyxia. EEG is already widely used in the neonatal intensive care units but there is a need for quantitative measures that can be obtained without the presence of a clinical expert. One of the biggest challenges in the treatment of newborns with asphyxia is also to correctly estimate the severity of the resulting neurological problems. Eight different feature classes were computed for 42 full-term babies from periods of quiet and active sleep. These feature classes measured correlations of amplitude and phase, interhemispheric synchrony, multifractality and spectral properties. We then studied the ability of these features to distinguish between different severity groups and also tested a classification algorithm to predict the outcome of the babies. Quiet sleep was noted to be more sensitive when separating groups with different grades of severity and most of the used feature classes showed significant results in statistical testing between the groups. The babies with the normal outcome were classified more accurately with the EEG based classification algorithm, than with only the clinical estimation.Työn tarkoituksena oli selvittää, onko aivosähkökäyrästä (EEG) laskettuja parametreja mahdollista käyttää happivajeesta kärsineiden vastasyntyneiden automaattisessa monitoroinnissa. EEG on jo nyt yleisesti käytössä vastasyntyneiden teho-osastoilla, mutta tarve kvantitatiivisille mittareille, joiden tulkintaan ei tarvita lääketieteen asiantuntijaa, on suuri. Lisäksi yksi suurimmista haasteista on pystyä arvioimaan tarkasti, kuinka vakaviin neurologisiin ongelmiin happivaje johtaa. Työssä laskettiin kahdeksan erilaista muuttujajoukkoa 42 täysiaikaiselle vauvalle sekä hiljaisen että aktiivisen unen aikana. Nämä muuttujat mittasivat amplitudin ja vaiheen korrelaatioita, aivopuoliskojen välistä synkroniaa, multifraktaalisuutta sekä taajuusjakaumaa. Tämän jälkeen tutkittiin muuttujien kykyä erotella eri vakavuusasteisia ryhmiä ja testattiin luokittelualgoritmia vauvojen tulevan terveydentilan ennustamiseen. Hiljaisen unen huomattiin olevan herkempi havaitsemaan eroja eri vakavuusasteisten ryhmien välillä ja tilastollisen testauksen perusteella suurin osa valituista muuttujajoukoista erotteli merkittävästi eri vakavuusryhmiä. Ne vauvat, jotka toipuivat hapenpuutteesta täysin, pystyttiin löytämään EEG-pohjaisella luokittimella tarkemmin kuin pelkän kliinisen arvion avulla

Design of a Simulator for Neonatal Multichannel EEG: Application to Time-Frequency Approaches for Automatic Artifact Removal and Seizure Detection

The electroencephalogram (EEG) is used to noninvasively monitor brain activities; it is the most utilized tool to detect abnormalities such as seizures. In recent studies, detection of neonatal EEG seizures has been automated to assist neurophysiologists in diagnosing EEG as manual detection is time consuming and subjective; however it still lacks the necessary robustness that is required for clinical implementation. Moreover, as EEG is intended to record the cerebral activities, extra-cerebral activities external to the brain are also recorded; these are called “artifacts” and can seriously degrade the accuracy of seizure detection. Seizures are one of the most common neurologic problems managed by hospitals occurring in 0.1%-0.5% livebirths. Neonates with seizures are at higher risk for mortality and are reported to be 55-70 times more likely to have severe cerebral-palsy. Therefore, early and accurate detection of neonatal seizures is important to prevent long-term neurological damage. Several attempts in modelling the neonatal EEG and artifacts have been done, but most did not consider the multichannel case. Furthermore, these models were used to test artifact or seizure detection separately, but not together. This study aims to design synthetic models that generate clean or corrupted multichannel EEG to test the accuracy of available artifact and seizure detection algorithms in a controlled environment. In this thesis, synthetic neonatal EEG model is constructed by using; single-channel EEG simulators, head model, 21-electrodes, and propagation equations, to produce clean multichannel EEG. Furthermore, neonatal EEG artifact model is designed using synthetic signals to corrupt EEG waveforms. After that, an automated EEG artifact detection and removal system is designed in both time and time-frequency domains. Artifact detection is optimised and removal performance is evaluated. Finally, an automated seizure detection technique is developed, utilising fused and extended multichannel features along a cross-validated SVM classifier. Results show that the synthetic EEG model mimics real neonatal EEG with 0.62 average correlation, and corrupted-EEG can degrade seizure detection average accuracy from 100% to 70.9%. They also show that using artifact detection and removal enhances the average accuracy to 89.6%, and utilising the extended features enhances it to 97.4% and strengthened its robustness.لمراقبة ورصد أنشطة واشارات المخ، دون الحاجة لأي عملیات (EEG) یستخدم الرسم أو التخطیط الكھربائي للدماغ للدماغجراحیة، وھي تعد الأداة الأكثر استخداما في الكشف عن أي شذوذأو نوبات غیر طبیعیة مثل نوبات الصرع. وقد أظھرت دراسات حدیثة، أن الكشف الآلي لنوبات حدیثي الولادة، ساعد علماء الفسیولوجیا العصبیة في تشخیص الاشارات الدماغیة بشكل أكبر من الكشف الیدوي، حیث أن الكشف الیدوي یحتاج إلى وقت وجھد أكبر وھوذو فعالیة أقل بكثیر، إلا أنھ لا یزال یفتقر إلى المتانة الضروریة والمطلوبة للتطبیق السریري.علاوة على ذلك؛ فكما یقوم الرسم الكھربائي بتسجیل الأنشطة والإشارات الدماغیة الداخلیة، فھو یسجل أیضا أي نشاط أو اشارات خارجیة، مما یؤدي إلى -(artifacts) :حدوث خلل في مدى دقة وفعالیة الكشف عن النوبات الدماغیة الداخلیة، ویطلق على تلك الاشارات مسمى (نتاج صنعي) . 0.5٪ولادة حدیثة في -٪تعد نوبات الصرع من أكثر المشكلات العصبیة انتشارا،ً وھي تصیب ما یقارب 0.1المستشفیات. حیث أن حدیثي الولادة المصابین بنوبات الصرع ھم أكثر عرضة للوفاة، وكما تشیر التقاریر الى أنھم 70مرة أكثر. لذا یعد الكشف المبكر والدقیق للنوبات الدماغیة -معرضین للإصابة بالشلل الدماغي الشدید بما یقارب 55لحدیثي الولادة مھم جدا لمنع الضرر العصبي على المدى الطویل. لقد تم القیام بالعدید من المحاولات التي كانتتھدف الى تصمیم نموذج التخطیط الكھربائي والنتاج الصنعي لدماغ حدیثي الولادة, إلا أن معظمھا لم یعر أي اھتمام الى قضیة تعدد القنوات. إضافة الى ذلك, استخدمت ھذه النماذج , كل على حدة, أو نوبات الصرع. تھدف ھذه الدراسة الى تصمیم نماذج مصطنعة من شأنھا (artifact) لإختبار كاشفات النتاج الصنعيأن تولد اشارات دماغیة متعددة القنوات سلیمة أو معطلة وذلك لفحص مدى دقة فعالیة خوارزمیات الكشف عن نوبات ضمن بیئة یمكن السیطرة علیھا. (artifact) الصرع و النتاج الصنعي في ھذه الأطروحة, یتكون نموذج الرسم الكھربائي المصطنع لحدیثي الولادة من : قناة محاكاة واحده للرسم الكھربائي, نموذج رأس, 21قطب كھربائي و معادلات إنتشار. حیث تھدف جمیعھا لإنتاج إشاراة سلیمة متعدده القنوات للتخطیط عن طریق استخدام اشارات مصطنعة (artifact) الكھربائي للدماغ.علاوة على ذلك, لقد تم تصمیم نموذجالنتاج الصنعيفي نطاقالوقت و (artifact) لإتلاف الرسم الكھربائي للدماغ. بعد ذلك تم انشاء برنامج لكشف و إزالةالنتاج الصناعينطاقالوقت و التردد المشترك. تم تحسین برنامج الكشف النتاج الصناعيالى ابعد ما یمكن بینما تمت عملیة تقییم أداء الإزالة. وفي الختام تم التمكن من تطویر تقنیة الكشف الآلي عن نوبات الصرع, وذلك بتوظیف صفات مدمجة و صفات الذي تم التأكد من صحتھ. (SVM) جدیدة للقنوات المتعددة لإستخدامھا للمصنفلقد أظھرت النتائج أن نموذج الرسم الكھربائي المصطنع لحدیثي الولادة یحاكي الرسمالكھربائي الحقیقي لحدیثي الولادة بمتوسط ترابط 0.62, و أنالرسم الكھربائي المتضرر للدماغ قد یؤدي الى حدوث ھبوطفي مدى دقة متوسط الكشف عن نوبات الصرع من 100%الى 70.9%. وقد أشارت أیضا الى أن استخدام الكشف والإزالة عن النتاج الصنعي (artifact) یؤدي الى تحسن مستوى الدقة الى نسبة 89.6 %, وأن توظیف الصفات الجدیدة للقنوات المتعددة یزید من تحسنھا لتصل الى نسبة 94.4 % مما یعمل على دعم متانتھا