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

Improvements in Neonatal Brain Monitoring after Perinatal Asphyxia

Perinatal hypoxic ischemic encephalopathy (HIE) is a major cause of morbidity and mortality world-wide. Common sequelae in survivors include cerebral palsy (CP), epilepsy and sensory as well as cognitive problems. The consequences of HIE impose significant long-term personal and financial burden on the affected families and the society. The most cost-effective approach to reducing neonatal mortality world-wide would be to improve access to antenatal care4. However, even in developed countries, the exact factors triggering perinatal asphyxia as well as the time of onset of brain injury are often difficult to determine, and it remains a major clinical problem. Seizures commonly occur in the neonate with HIE and are often the only sign of serious underlying brain dysfunction6. Animal studies have shown that neonatal seizures in the context of HIE may cause additional brain injury and that their pharmacological suppression may improve outcome9. Monitoring of brain function using the electroencephalogram (EEG), continuously or by serial EEGs is well-suited to give insight into brain function and its dynamic changes in neonatal HIE and helps to guide treatment as well as prognostication. A good understanding of the pathophysiology of HIE is needed not only in the selection of suitable diagnostic tests and treatment methods, but also to develop new therapeutic strategies

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Coherence analysis: Methods, solutions and problems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A coherence function is a measure of the correlation of two signals and may be used as a measure for functional relationship between brain areas. In studying functional relationships, referenced EEG (REEG) coherence analysis yields important new aspects of brain activities, which complement the data obtained by power spectral analysis. However, REEG-based coherence tends to show a false high value due to volume conduction from un correlated sources (VCUS). Existing signal processing methods address this issue using a Fourier coherence function of scalp Laplacian. Although this method has been proved useful to reveal correlation between EEG signals with minimum VCUS effects, it only provides frequency-domain analysis. Since EEG signals are highly non-stationary, it is more appropriate to use time-frequency methods for coherence analysis of scalp Laplacian. Thus this research applies the wavelet transform on coherence analysis of scalp Laplacian. To verify our technique, already recorded EEG data of event related potentials were obtained from a study of two large groups of alcoholic and abstinent alcoholic subjects, performing visual picture-recognition tasks. The proposed coherence method successfully detected time-frequency correlation between EEG signals with minimum VCUS effects. It showed significant spatial specificity and revealed detailed coherence patterns. Some new important results regarding time-frequency characteristics of VCUS effects on wavelet and short-time Fourier transform (STFT) coherence analysis of REEG signals were deduced. The proposed coherence method was also compared to a conventional wavelet coherence method of REEG signals in the study of coherence difference between coherences of alcoholic and abstinent alcoholic EEG signals. Results of this study provided substantial evidence that VCUS effects are not additive and therefore can not be ignored in comparison of different brain states between groups of subjects

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Automatic Detection and Classification of Neural Signals in Epilepsy

The success of an epilepsy treatment, such as resective surgery, relies heavily on the accurate identification and localization of the brain regions involved in epilepsy for which patients undergo continuous intracranial electroencephalogram (EEG) monitoring. The prolonged EEG recordings are screened for two main biomarkers of epilepsy: seizures and interictal spikes. Visual screening and quantitation of these two biomarkers in voluminous EEG recordings is highly subjective, labor-intensive, tiresome and expensive. This thesis focuses on developing new techniques to detect and classify these events in the EEG to aid the review of prolonged intracranial EEG recordings. It has been observed in the literature that reliable seizure detection can be made by quantifying the evolution of seizure EEG waveforms. This thesis presents three new computationally simple non-patient-specific (NPS) seizure detection systems that quantify the temporal evolution of seizure EEG. The first method is based on the frequency-weighted-energy, the second method on quantifying the EEG waveform sharpness, while the third method mimics EEG experts. The performance of these new methods is compared with that of three state-of-the-art NPS seizure detection systems. The results show that the proposed systems outperform these state-of-the-art systems. Epilepsy therapies are individualized for numerous reasons, and patient-specific (PS) seizure detection techniques are needed not only in the pre-surgical evaluation of prolonged EEG recordings, but also in the emerging neuro-responsive therapies. This thesis proposes a new model-based PS seizure detection system that requires only the knowledge of a template seizure pattern to derive the seizure model consisting of a set of basis functions necessary to utilize the statistically optimal null filters (SONF) for the detection of the subsequent seizures. The results of the performance evaluation show that the proposed system provides improved results compared to the clinically-used PS system. Quantitative analysis of the second biomarker, interictal spikes, may help in the understanding of epileptogenesis, and to identify new epileptic biomarkers and new therapies. However, such an analysis is still done manually in most of the epilepsy centers. This thesis presents an unsupervised spike sorting system that does not require a priori knowledge of the complete spike data

Implantable Asynchronous Epilectic Seizure Detector

RÉSUMÉ Plusieurs algorithmes de détection à faible consommation ont été proposés pour le traitement de l'épilepsie focale. La gestion de l'énergie dans ces microsystèmes est une question importante qui dépend principalement de la charge et de la décharge des capacités parasites des transistors et des courants de court-circuit pendant les commutations. Dans ce mémoire, un détecteur asynchrone de crise pour le traitement de l'épilepsie focale est présenté. Ce système fait partie d'un dispositif implantable intégré pour stopper la propagation de la crise. L'objectif de ce travail est de réduire la dissipation de puissance en évitant les transitions inutiles de signaux grâce à la technique du « clock tree » ; en conséquence, les transistors ne changent pas d'état transitoire dans ce mode d'économie d'énergie (période de surveillance des EEG intracrâniens), sauf si un événement anormal est détecté. Le dispositif intégré proposé comporte un bio-amplificateur en amont (front-end) à faible bruit, un processeur de signal numérique et un détecteur. Un délai variable et quatre détecteurs de fenêtres de tensions variables en parallèles sont utilisés pour extraire de l’information sur le déclenchement des crises. La sensibilité du détecteur est améliorée en optimisant les paramètres variables en fonction des activités de foyers épileptiques de chaque patient lors du début des crises. Le détecteur de crises asynchrone proposé a été implémenté premièrement en tant que prototype sur un circuit imprimé circulaire, ensuite nous l’avons intégré sur une seule puce dans la technologie standard CMOS 0.13μm. La puce fabriquée a été validée in vitro en utilisant un total de 34 enregistrements EEG intracrâniens avec la durée moyenne de chaque enregistrement de 1 min. Parmi ces jeux de données, 15 d’entre eux correspondaient à des enregistrements de crises, tandis que les 19 autres provenaient d’enregistrements variables de patients tels que de brèves crises électriques, des mouvements du corps et des variations durant le sommeil. Le système proposé a réalisé une performance de détection précise avec une sensibilité de 100% et 100% de spécificité pour ces 34 signaux icEEG enregistrés. Le délai de détection moyen était de 13,7 s après le début de la crise, bien avant l'apparition des manifestations cliniques, et une consommation d'énergie de 9 µW a été obtenue à partir d'essais expérimentaux.----------ABSTRACT Several power efficient detection algorithms have been proposed for treatment of focal epilepsy. Power management in these microsystems is an important issue which is mainly dependent on charging and discharging of the parasitic capacitances in transistors and short-circuit currents during switching. In this thesis, an asynchronous seizure detector for treatment of the focal epilepsy is presented. This system is part of an implantable integrated device to block the seizure progression. The objective of this work is reducing the power dissipation by avoiding the unnecessary signal transition and clock tree; as a result, transistors do not change their transient state in power saving mode (icEEG monitoring period) unless an abnormal event detected. The proposed integrated device contains a low noise front-end bioamplifier, a digital signal processor and a detector. A variable time frame and four concurrent variable voltage window detectors are used to extract seizure onset information. The sensitivity of the detector is enhanced by optimizing the variable parameters based on specific electrographic seizure onset activities of each patient. The proposed asynchronous seizure detector was first implemented as a prototype on a PCB and then integrated in standard 0.13 μm CMOS process. The fabricated chip was validated offline using a total of 34 intracranial EEG recordings with the average time duration of 1 min. 15 of these datasets corresponded to seizure activities while the remaining 19 signals were related to variable patient activities such as brief electrical seizures, body movement, and sleep patterns. The proposed system achieved an accurate detection performance with 100% sensitivity and 100 % specificity for these 34 recorded icEEG signals. The average detection delay was 13.7 s after seizure onset, well before the onset of the clinical manifestations. Finally, power consumption of the chip is 9 µW obtained from experimental tests

Coherence analysis : methods, solutions and problems

A coherence function is a measure of the correlation of two signals and may be used as a measure for functional relationship between brain areas. In studying functional relationships, referenced EEG (REEG) coherence analysis yields important new aspects of brain activities, which complement the data obtained by power spectral analysis. However, REEG-based coherence tends to show a false high value due to volume conduction from un correlated sources (VCUS). Existing signal processing methods address this issue using a Fourier coherence function of scalp Laplacian. Although this method has been proved useful to reveal correlation between EEG signals with minimum VCUS effects, it only provides frequency-domain analysis. Since EEG signals are highly non-stationary, it is more appropriate to use time-frequency methods for coherence analysis of scalp Laplacian. Thus this research applies the wavelet transform on coherence analysis of scalp Laplacian. To verify our technique, already recorded EEG data of event related potentials were obtained from a study of two large groups of alcoholic and abstinent alcoholic subjects, performing visual picture-recognition tasks. The proposed coherence method successfully detected time-frequency correlation between EEG signals with minimum VCUS effects. It showed significant spatial specificity and revealed detailed coherence patterns. Some new important results regarding time-frequency characteristics of VCUS effects on wavelet and short-time Fourier transform (STFT) coherence analysis of REEG signals were deduced. The proposed coherence method was also compared to a conventional wavelet coherence method of REEG signals in the study of coherence difference between coherences of alcoholic and abstinent alcoholic EEG signals. Results of this study provided substantial evidence that VCUS effects are not additive and therefore can not be ignored in comparison of different brain states between groups of subjects.EThOS - Electronic Theses Online ServiceGBUnited Kingdo