Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units through EEG, ECG and Video Recordings: A Survey

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEG-based NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis

Multiscale entropy analysis of heart rate variability in neonatal patients with and without seizures

The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns

Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time

Patient-Specific Epileptic Seizure Onset Detection via Fused Eeg and Ecg Signals

Epilepsy is a neurological disorder that is associated with sudden and recurrent seizures. Epilepsy affects 65 million people world-wide and is the third most common neurological disorder, after stroke and Alzheimer disease. During an epileptic seizure, the brain endures a transient period of abnormally excessive synchronous activity, leading to a state of havoc for many epileptic patients. Seizures can range from being mild and unnoticeable to extremely violent and life threating. Many epileptic individuals are not able to control their seizures with any form of treatment or therapy. These individuals often experience serious risk of injury, limited independence and mobility, and social isolation. In an attempt to increase the quality of life of epileptic individuals, much research has been dedicated to developing seizure onset detection systems that are capable of accurately and rapidly detecting signs of seizures. This thesis presents a novel seizure onset detection system that is based on the fusion of independent electroencephalogram (EEG) and electrocardiogram (ECG) based decisions. The EEG-based detector relies on a on a common spatial pattern (CSP)-based feature enhancement stage that enables better discrimination between seizure and non-seizure features. The EEG-based detector also introduces a novel classification system that uses logical operators to pool support vector machine (SVM) seizure onset detections made independently across different relevant EEG spectral bands. In the ECG-based detector, heart rate variability (HRV) is extracted and analyzed using a Matching-Pursuit and Wigner-Ville Distribution algorithm in order to effectively extract meaningful HRV features representative of seizure and non-seizure states. Two fusion systems are adopted to fuse the EEG- and ECG-based decisions. In the first system, EEG- and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an over-ride option that allows for the EEG-based decision to over-ride the fusion-based decision in an event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6 seconds, and a specificity of 99.91% for the MAJORITY fusion case. In addition, a novel method to calculate the amount of neural synchrony that exists between the channels of an EEG matrix is carried out. This method is based on extracting the condition number from multi-channel EEG at a particular time instant to indicate the level of neural synchrony at that particular time instant. The proposed method of neural synchrony calculation is implemented in two detection systems. The first system uses only neural synchrony as the feature for seizure classification whereas the second system fuses energy and synchrony based decision to make a final classification decision. Both systems show promising results when tested on a set of clinical patients

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

Intelligent monitoring and interpretation of preterm physiological signals using machine learning

Every year, more than one in ten babies are born prematurely. In Ireland of the 70000 babies delivered every year, 4500 are born too early. Premature babies are at a higher risk of complications, which may lead to both short-term and long-term adverse health outcomes. The neonatal population is especially vulnerable and a delay in the identification of medical conditions, as well as delays in the initiating the correct treatment, may be fatal. After birth, preterms are admitted to the neonatal intensive care unit (NICU), where a continuous flow of information in the form of physiological signals is available. Physiological signals can assist clinicians in decision making related to the diagnosis and treatment of various diseases. This information, however, can be highly complex, and usually requires expert analysis which may not be available at all times. The work conducted in this thesis develops a decision support systems for the intelligent monitoring of preterms in the NICU. This will allow for an accurate estimation of the current health status of the preterm neonate as well as the prediction of possible long-term complications. This thesis is comprised of three main work packages (WP), each addressing health complication of preterm on three different stages of life. At the first 12 hours of life the health status is quantified using the clinical risk index for babies (CRIB). This is followed by the assessment of the preterm’s well-being at discharge from the NICU using the clinical course score (CCS). Finally, the long-term neurodevelopmental follow-up is assessed using the Bayley III scales of development at two years. This is schematically represented in Figure 1 along with the main findings and contributions. Low blood pressure (BP) or hypotension is a recognised problem in preterm infants particularly during the first 72 hours of life. Hypotension may cause decreased cerebral perfusion, resulting in deprived oxygen delivery to the brain. Deciding when and whether to treat hypotension relies on our understanding of the relation between BP, oxygenation and brain activity. The electroencephalogram (EEG) is the most commonly used technology to assess the ‘brain health’ of a newborn. The first WP investigates the relationship between short-term dynamics in BP and EEG energy in the preterm on a large dataset of continuous multi-channel unedited EEG recordings in the context of the health status measured by the CRIB score. The obtained results indicate that a higher risk of mortality for the preterm is associated with a lower level of nonlinear interaction between EEG and BP. The level of coupling between these two systems can potentially serve as an additional source of information when deciding whether or not to intervene in the preterm. The electrocardiogram (ECG) is also routinely recorded in preterm infants. Analysis of heart rate variability (HRV) provides a non-invasive assessment of both the sympathetic and parasympathetic control of the heart rate. A novel automated objective decision support tool for the prediction of the short-term outcome (CCS) in preterm neonates who may have low BP is proposed in the second WP. Combining multiple HRV features extracted during hypotensive episodes, the classifier achieved an AUC of 0.97 for the task of short-term outcome prediction, using a leave-one-patient-out performance assessment. The developed system is based on the boosted decision tree classifier and allows for the continuous monitoring of the preterm. The proposed system is validated on a large clinically collected dataset of multimodal recordings from preterm neonates. If the correct treatment is initiated promptly after diagnosis, it can potentially improve the neurodevelopmental outcome of the preterm infant. The third WP presents a pilot study investigating the predictive capability of the early EEG recorded at discharge from the NICU with respect to the 2-year neurodevelopmental outcome using machine learning techniques. Two methods are used: 1) classical feature-based classifier, and 2) end-to-end deep learning. This is a fundamental study in this area, especially in the context of applying end-to-end learning to the preterm EEG for the problem of long-term outcome prediction. It is shown that for the available labelled dataset of 37 preterm neonates, the classical feature-based approach outperformed the end-to-end deep learning technique. A discussion of the obtained result as well as a section highlighting the possible limitations and areas that need to be investigated in the future are provided