An evaluation of sedation level using bispectral index (BIS) and correlated adverse events in patients undergoing colonoscopies

The purpose of this prospective, observational study was to provide data for anesthesia providers on current sedation practices during elective colonoscopies. This included determining the incidence of general anesthesia (GA) and the presence or absence of correlated adverse events. Additionally, this research considered if patients who are commonly consented for a MAC anesthetic should be more appropriately consented for GA. Participants (N = 39) consisted of a convenience sampling of physical status (PS) I, II, and III patients scheduled for elective colonoscopies and undergoing sedation with propofol. Data was collected by researchers over a four-week period at a non-teaching rural hospital in Western North Carolina. A bispectral index (BIS) monitor was used to monitor the depth of sedation and values were utilized to determine possible correlated adverse events. Statistical analysis showed that 100% (39/39) of patients reached levels of GA (i.e., BIS = 60) at some point during their procedure. Variables that showed a significant correlation with the occurrence of GA were smaller body mass index (BMI) (r = -.42, r2 = .17, p = .008), longer length of procedure (r = .85, r2 = .72, p < .001), and the number of minutes patients experienced an absent end tidal carbon dioxide (ETCO2) (i.e., apnea) waveform (r = .49, r2 = .24, p = .002). Additionally, greater BMI correlated with a greater nadir BIS value obtained throughout the entire procedure (r = .54, r2 = .29, p < .001), and was found to correlate with less time at BIS values = 40 (r = -.51, r2 = .26, p < .001). Longer procedures correlated with more minutes spent with BIS values = 40 (r = .43, r2 = .18, p = .007), and more minutes with absent ETCO2 waveform (r = .52, r2 = .27, p = .001); however, these findings were clinically insignificant since only one absent ETCO2 waveform actually resulted in a decrease in saturation of peripheral oxygen (SpO2) to = 90% (i.e., hypoxia), which quickly resolved with a chin lift. Additionally, the number of minutes with SpO2 = 90% was not significantly correlated with the minutes of GA (r = -.17, r2 = .03, p = .299). The results of this study indicate, in patients scheduled for colonoscopies who are consented for IV GA, it is common for anesthesia providers to consistently deliver a level of sedation concordant with GA. The significance of this finding relates to the pre-study clinical observation, that endoscopic patients being consented for anesthesia designated as MAC with IV sedation, actually demonstrate intraoperative signs of GA similar to what were observed in this study. Future studies are warranted to determine the frequency of the various forms of anesthesia consent obtained for elective colonoscopies, along with research that assesses anesthetic depth with BIS monitoring in patients consented for MAC with IV sedation. Such research would help to further enhance patient safety and address potential medical legal concerns

An evaluation of sedation level using bispectral index (BIS) and correlated adverse events in patients undergoing colonoscopies

The purpose of this prospective, observational study was to provide data for anesthesia providers on current sedation practices during elective colonoscopies. This included determining the incidence of general anesthesia (GA) and the presence or absence of correlated adverse events. Additionally, this research considered if patients who are commonly consented for a MAC anesthetic should be more appropriately consented for GA. Participants (N = 39) consisted of a convenience sampling of physical status (PS) I, II, and III patients scheduled for elective colonoscopies and undergoing sedation with propofol. Data was collected by researchers over a four-week period at a non-teaching rural hospital in Western North Carolina. A bispectral index (BIS) monitor was used to monitor the depth of sedation and values were utilized to determine possible correlated adverse events. Statistical analysis showed that 100% (39/39) of patients reached levels of GA (i.e., BIS = 60) at some point during their procedure. Variables that showed a significant correlation with the occurrence of GA were smaller body mass index (BMI) (r = -.42, r2 = .17, p = .008), longer length of procedure (r = .85, r2 = .72, p < .001), and the number of minutes patients experienced an absent end tidal carbon dioxide (ETCO2) (i.e., apnea) waveform (r = .49, r2 = .24, p = .002). Additionally, greater BMI correlated with a greater nadir BIS value obtained throughout the entire procedure (r = .54, r2 = .29, p < .001), and was found to correlate with less time at BIS values = 40 (r = -.51, r2 = .26, p < .001). Longer procedures correlated with more minutes spent with BIS values = 40 (r = .43, r2 = .18, p = .007), and more minutes with absent ETCO2 waveform (r = .52, r2 = .27, p = .001); however, these findings were clinically insignificant since only one absent ETCO2 waveform actually resulted in a decrease in saturation of peripheral oxygen (SpO2) to = 90% (i.e., hypoxia), which quickly resolved with a chin lift. Additionally, the number of minutes with SpO2 = 90% was not significantly correlated with the minutes of GA (r = -.17, r2 = .03, p = .299). The results of this study indicate, in patients scheduled for colonoscopies who are consented for IV GA, it is common for anesthesia providers to consistently deliver a level of sedation concordant with GA. The significance of this finding relates to the pre-study clinical observation, that endoscopic patients being consented for anesthesia designated as MAC with IV sedation, actually demonstrate intraoperative signs of GA similar to what were observed in this study. Future studies are warranted to determine the frequency of the various forms of anesthesia consent obtained for elective colonoscopies, along with research that assesses anesthetic depth with BIS monitoring in patients consented for MAC with IV sedation. Such research would help to further enhance patient safety and address potential medical legal concerns

비침습적 뇌파 신호를 이용한 응급환자의 생체반응 모니터링 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.뇌파는 대뇌피질이나 두피의 전극을 통해서 뇌의 전기적 신호를 기록한 것을 의미한다. 뇌 기능 관찰을 위한 진단도구로써 뇌파는 뇌전증이나 치매 진단 등의 목적으로 활용되고 있다. 본 논문에서는 비침습적 뇌파를 이용하여 응급환자의 주요 생리학적 지표를 모니터링하는 기술을 개발하였다. 처음 두 연구에서 심폐소생술의 효과를 평가하기 위한 심정지 돼지실험모델을 고안하였다. 현재의 심폐소생술 지침은 체순환 평가를 위해 기도삽관을 통한 호기말 이산화탄소 분압의 측정을 권고한다. 하지만, 정확한 기도삽관이 특히 병원 밖 상황에서 어려울 수 있다. 따라서, 간편히 측정할 수 있고 소생 환자의 신경학적 예후를 진단하는데 사용되는 뇌파를 이용한 예측 기술이 심폐소생술 품질평가지표의 대안으로 제안되었다. 첫 번째 실험에서는 고품질과 저품질 기본심폐소생술을 10회 반복하면서 측정된 뇌파를 분석하였다. 심폐소생술의 품질에 따른 뇌파의 변화를 이용하여 체순환 평가를 위한 EEG-based Brain Resuscitation Index (EBRI) 모델을 도출하였다. EBRI 모델에서 획득한 호기말 이산화탄소 분압 예측치는 실제 값과 양의 상관관계를 보이며, 병원 밖 상황에서의 활용 가능성을 보였다. 두 번째 실험에서는 두 가지 심폐소생술(기본심폐소생술, 전문심폐소생술)이 수행되었다. 제세동 직전에 수집된 뇌파는 심폐소생술 도중 경동맥혈류의 회복률과 함께 분석되었다. 심폐소생술 도중 경동맥혈류의 회복률을 반영하는 뇌파 변수를 규명한 후, 이를 이용하여 높은 회복률(30% 이상)과 낮은 회복률(30% 미만)을 구분하는 기계학습 기반 이진분류모델을 도출하였다. 서포트 벡터 머신 기반의 예측모델이 0.853의 정확도와 0.909의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 이러한 예측모델은 심정지 환자의 뇌 소생을 향상시켜 빠른 뇌 기능 회복을 가능하게 할 것으로 기대된다. 세 번째 연구에서 비침습적 뇌파를 이용하여 두개내압을 예측하는 모델을 개발하기 위한 외상성 뇌손상 돼지실험모델이 고안되었다. 외상성 뇌손상은 물리적 충격에 의해 정상적인 뇌 기능이 중단된 상태를 의미하며, 이 때의 두개내압 상승과 관류저하가 뇌파에 영향을 끼칠 수 있다. 따라서, 우리는 뇌파 기반 두개내압 예측모델을 개발하였다. 폴리카테터로 실험동물의 두개내압을 변경하면서 뇌파를 획득하였다. 두개내압의 정상구간(25 mmHg 미만)과 위험구간(25 mmHg 이상)을 유의미하게 구분하는 뇌파 변수를 규명한 후 기계학습 기반 이진분류모델을 도출하였다. 다층 퍼셉트론 기반의 예측모델이 0.686의 정확도와 0.754의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 또다른 비침습 데이터인 심박수 정보와 함께 사용하였을 때 정확도와 곡선하면적은 각각 0.760과 0.834로 향상되었다. 제안된 예측모델은 응급상황에서 비침습적으로 두개내압을 관찰하여 정상 수준의 두개내압을 유지하는데 도움을 줄 것으로 기대된다. 본 논문은 응급환자의 주요 생리학적 지표를 비침습적 뇌파를 이용하여 관찰하는 예측모델을 제안하고 성능을 검증하였다. 본 연구에서는 뇌파를 이용하여 즉각적인 호기말 이산화탄소 분압, 경동맥혈류, 두개내압을 추정하기 위한 예측모델을 수립하였다. 하지만, 뇌파 데이터는 장기간의 신경학적, 기능적 회복과 함께 평가되어야 한다. 본 논문에서 개발한 예측모델의 성능과 적용 가능성은 향후 다양한 임상연구를 통해 cerebral performance category와 modified Rankin scale 등의 신경학적 평가지표와 함께 분석, 개선되어야 할 것이다.Electroencephalogram (EEG) is a recording of the electrical activity of the brain, measured using electrodes attached to the cerebrum cortex or the scalp. As a diagnostic tool for brain disorders, EEG has been widely used for clinical purposes such as epilepsy- and dementia diagnosis. This study develops an EEG-based noninvasive critical care monitoring method for emergency patients. In the first two studies, ventricular fibrillation swine models were designed to develop EEG-based monitoring methods for evaluating the effectiveness of cardiopulmonary resuscitation (CPR). The CPR guidelines recommend measuring end-tidal carbon dioxide (ETCO2) via endotracheal intubation to assess systemic circulation. However, accurate insertion of the endotracheal tube might be difficult in an out-of-hospital setting (OOHS). Therefore, an easily measurable EEG, which has been used to predict resuscitated patients neurologic prognosis, was suggested as a surrogate indicator for CPR feedback. In the first experimental setup, the high- and low quality CPRs were altered 10 times repeatedly, and the EEG parameters were analyzed. Linear regression of an EEG-based brain resuscitation index (EBRI) was used to estimate ETCO2 levels as a novel feedback indicator of systemic circulation during CPR. A positive correlation was found between the EBRI and the real ETCO2, which indicates the feasibility of EBRI in OOHSs. In the second experimental setup, two types of CPR mode were performed: basic life support and advanced cardiovascular life support. EEG signals that were measured between chest compressions and defibrillation shocks were analyzed to monitor the cerebral circulation with respect to the recovery of carotid blood flow (CaBF) during CPR. Significant EEG parameters were identified to represent the CaBF recovery, and machine learning (ML)-based classification models were established to differentiate between the higher (≥ 30%) and lower (< 30%) CaBF recovery. The prediction model based on the support vector machine (SVM) showed the best performance, with an accuracy of 0.853 and an area under the curve (AUC) of 0.909. The proposed models are expected to guide better cerebral resuscitation and enable early recovery of brain function. In the third study, a swine model of traumatic brain injury (TBI) was designed to develop an EEG-based prediction model of an elevated intracranial pressure (ICP). TBI is defined as the disruption of normal brain function due to physical impact. This can increase ICP, and the resulting hypoperfusion can affect the cerebral electrical activity. Thus, we developed EEG-based prediction models to monitor ICP levels. During the experiments, EEG was measured while the ICP was adjusted with the Foley balloon catheter. Significant EEG parameters were determined to differentiate between the normal (< 25 mmHg) and dangerous (≥ 25 mmHg) ICP levels and ML-based binary classifiers were established to distinguish between these two groups. The multilayer perceptron model showed the best performance with an accuracy of 0.686 and an AUC of 0.754, which were improved to 0.760 and 0.834, respectively, when a noninvasive heart rate was also used as an input. The proposed prediction models are expected to instantly treat an elevated ICP (≥ 25 mmHg) in emergency settings. This study presents a new EEG-based noninvasive monitoring method of the physiologic parameters of emergency patients, especially in an OOHS, and evaluates the performance of the proposed models. In this study, EEG was analyzed to predict immediate ETCO2, CaBF, and ICP. The prediction models demonstrate that a noninvasive EEG can yield clinically important predictive outcomes. Eventually, the EEG parameters should be investigated with regard to the long-term neurological and functional outcomes. Further clinical trials are warranted to improve and evaluate the feasibility of the proposed method with respect to the neurological evaluation scores, such as the cerebral performance category and modified Rankin scale.Abstract i Contents iv List of Tables viii List of Figures x List of Abbreviations xii Chapter 1 General Introduction 1 1.1 Electroencephalogram 1 1.2 Clinical use of spontaneous EEG 5 1.3 EEG and cerebral hemodynamics 7 1.4 EEG use in emergency settings 9 1.5 Noninvasive CPR assessment 10 1.6 Noninvasive traumatic brain injury assessment 16 1.7 Thesis objectives 21 Chapter 2 EEG-based Brain Resuscitation Index for Monitoring Systemic Circulation During CPR 23 2.1 Introduction 23 2.2 Methods 25 2.2.1 Ethical statement 25 2.2.2 Study design and setting 25 2.2.3 Experimental animals and housing 27 2.2.4 Surgical preparation and hemodynamic measurements 27 2.2.5 EEG measurement 29 2.2.6 Data analysis 32 2.2.7 EBRI calculation 33 2.2.8 Delta-EBRI calculation 34 2.3 Results 36 2.3.1 Hemodynamic parameters 36 2.3.2 Changes in EEG parameters 37 2.3.3 EBRI calculation 39 2.3.4 Delta-EBRI calculation 41 2.4 Discussion 42 2.4.1 Accomplishment 42 2.4.2 Limitations 45 2.5 Conclusion 46 Chapter 3 EEG-based Prediction Model of the Recovery of Carotid Blood Flow for Monitoring Cerebral Circulation During CPR 47 3.1 Introduction 47 3.2 Methods 50 3.2.1 Ethical statement 50 3.2.2 Study design and setting 50 3.2.3 Experimental animals and housing 52 3.2.4 Surgical preparation and hemodynamic measurements 54 3.2.5 EEG measurement 55 3.2.6 Data processing 57 3.2.7 Data analysis 58 3.2.8 Development of machine-learning based prediction model 59 3.3 Results 63 3.3.1 Results of CPR process 63 3.3.2 EEG changes with the recovery of CaBF 66 3.3.3 Changes in EEG parameters depending on four CaBF groups 68 3.3.4 Changes in EEG parameters depending on two CaBF groups 69 3.3.5 EEG parameters for prediction models 70 3.3.6 Performances of prediction models 73 3.4 Discussion 76 3.4.1 Accomplishment 76 3.4.2 Limitations 78 3.5 Conclusion 80 Chapter 4 EEG-based Prediction Model of an Increased Intra-Cranial Pressure for TBI patients 81 4.1 Introduction 81 4.2 Methods 83 4.2.1 Ethical statement 83 4.2.2 Study design and setting 83 4.2.3 Experimental animals and housing 85 4.2.4 Surgical preparation and hemodynamic measurements 86 4.2.5 EEG measurement 88 4.2.6 Data processing 90 4.2.7 Data analysis 90 4.2.8 Development of machine-learning based prediction model 91 4.3 Results 92 4.3.1 Hemodynamic changes during brain injury phase 92 4.3.2 EEG changes with an increase of ICP 93 4.3.3 EEG parameters for prediction models 94 4.3.4 Performances for prediction models 95 4.4 Discussion 100 4.4.1 Accomplishment 100 4.4.2 Limitations 104 4.5 Conclusion 104 Chapter 5 Summary and Future works 105 5.1 Thesis summary and contributions 105 5.2 Future direction 108 Bibilography 113 Abstract in Korean 135Docto

An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers

Protocol for the Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography (P-DROWS-E) study: A prospective observational study of delirium in elderly cardiac surgical patients

INTRODUCTION: Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome. METHODS AND ANALYSIS: P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time. ETHICS AND DISSEMINATION: P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media. TRIAL REGISTRATION NUMBER: NCT03291626

Dynamic Complexity and Causality Analysis of Scalp EEG for Detection of Cognitive Deficits

This dissertation explores the potential of scalp electroencephalography (EEG) for the detection and evaluation of neurological deficits due to moderate/severe traumatic brain injury (TBI), mild cognitive impairment (MCI), and early Alzheimer’s disease (AD). Neurological disorders often cannot be accurately diagnosed without the use of advanced imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Non-quantitative task-based examinations are also used. None of these techniques, however, are typically performed in the primary care setting. Furthermore, the time and expense involved often deters physicians from performing them, leading to potential worse prognoses for patients. If feasible, screening for cognitive deficits using scalp EEG would provide a fast, inexpensive, and less invasive alternative for evaluation of TBI post injury and detection of MCI and early AD. In this work various measures of EEG complexity and causality are explored as means of detecting cognitive deficits. Complexity measures include eventrelated Tsallis entropy, multiscale entropy, inter-regional transfer entropy delays, and regional variation in common spectral features, and graphical analysis of EEG inter-channel coherence. Causality analysis based on nonlinear state space reconstruction is explored in case studies of intensive care unit (ICU) signal reconstruction and detection of cognitive deficits via EEG reconstruction models. Significant contributions in this work include: (1) innovative entropy-based methods for analyzing event-related EEG data; (2) recommendations regarding differences in MCI/AD of common spectral and complexity features for different scalp regions and protocol conditions; (3) development of novel artificial neural network techniques for multivariate signal reconstruction; and (4) novel EEG biomarkers for detection of dementia

Advances in Clinical Neurophysiology

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests

Dog electroencephalogram for early safety seizure liability assessments and investigation of species-specific sensitivity for neurological symptoms

Preclinical safety is an important part of drug development in animals and humans. In toxicology studies, seizure liability can be detected at high doses as convulsions. Non-convulsive seizures induce only subtle behavioral changes and their assessment in animals is challenging. Electroencephalography (EEG) is the only method to correlate animal behavior to seizure activity and video-EEG is the current gold-standard for preclinical seizure liability assessments (Authier et al., 2014b). In most cases there are no clear premonitory signs that forewarn of convulsions but epileptiform EEG activity prior to clinical manifestation has been reported during a period potentially sufficient for prophylactic anticonvulsive treatment (Dürmüller et al., 2007). Aim of this thesis was investigation of a study design for assessment of neurological symptoms in dogs. This design should optimize detection of neurological signs while minimizing study duration and animal numbers. Video-EEG was used to increase symptom detection rate and to explore the possibility to refine seizure liability testing by enabling EEG-based anticonvulsive treatment. For establishment of the EEG system in our facility, reference substances were tested first. Then, three in-house drug candidates with different modes of action and known neurological side effects were chosen. Two telemetered beagle dogs were used per experiment. Substance effects on clinical symptoms and on the EEG were investigated. CSF and blood samples for analysis of drug exposure and biomarkers were collected simultaneous to symptoms. Results were compared to previous toxicological studies thereby enabling evaluation of non-rodent species differences in sensitivity for neurological symptoms. Results showed that combination of implants for CSF collection and EEG recording is possible. In this study design, intravenous administration was superior to oral dosing as it led to a reduced variability in exposure levels. Also, experimental time was significantly reduced compared to standard toxicology studies while the same neurological symptoms were induced. This shortened duration enabled continuous clinical observations for a better evaluation of CNS effects and immediate veterinary assistance in the spirit of animal welfare. The EEG was not superior to clinical observations in forewarning of convulsion risk and did not enable convulsion prevention. This was due first to the short latency between onset of abnormal EEG activity and convulsions which was below one minute with in-house compounds. Secondly, accurate interpretation of the unfiltered EEG signal was limited, especially differentiation of artefacts and epileptiform activity. In conclusion, a study design using intravenous infusions is suitable for the characterization of neurological symptoms. All the symptoms, which were already known from studies with a longer duration, were also seen. This allowed better correlation of neurological symptoms to exposure and immediate veterinarian treatments. For substances with a high risk to induce severe neurological symptoms, such studies can guide dose selection for longer regulatory toxicological studies to prevent occurrence of severe neurological symptoms.Im Rahmen der Entwicklung von Human- und Veterinärarzneimitteln wird die Anwendersicherheit neuer Medikamente in präklinischen Sicherheitsstudien erforscht. Zentralnervöse Nebenwirkungen werden häufig erst in toxikologischen Prüfungen erkannt, wenn bei hohen Dosierungen Krampfanfälle bei den Versuchstieren auftreten. Epileptische Anfälle können allerdings auch subtilere Symptome, deren Erkennen in Tieren schwierig ist, verursachen. Die Elektroenzephalographie (EEG) bietet in Tierstudien die einzige Möglichkeit, nicht-konvulsive Anfälle zu diagnostizieren. Daher ist die Kombination von Videoüberwachung und EEG in der präklinischen Arzneimittelentwicklung gegenwärtig der Goldstandard für die Sicherheitsbewertung einer Substanz im Hinblick auf ihr Risiko, Anfälle auszulösen (Authier et al., 2014b). Meist gibt es keine klinischen Warnzeichen vor dem Auftreten von Krampfanfällen. Allerdings wurde das Auftreten epileptiformer EEG-Aktivität vor klinischen Symptomen beobachtet. Das beschriebene Zeitfenster ist potentiell ausreichend für prophylaktische antikonvulsive Behandlung (Dürmüller et al., 2007). Ziel dieser Arbeit war es, in Pilotstudien ein neues Studiendesign für die Charakterisierung neurologischer Nebenwirkungen zu evaluieren. Dieses Studiendesign sollte die Erkennungsrate neurologischer Nebenwirkungen optimieren und dabei gleichzeitig eine Reduktion der dazu nötigen Tiere und der Studiendauer ermöglichen. Der Einsatz von EEG und Videoüberwachung sollte es ermöglichen, Substanz-induzierte Anfälle im Frühstadium zu erkennen und ihr klinisches Auftreten zu verhindern. Um das EEG-System in der Forschungseinrichtung neu zu etablieren und um zu evaluieren, ob Implantate für Liquor-Entnahme und EEG-Aufzeichnung kompatibel sind, wurden zuerst Referenzsubstanzen getestet. Zur Beantwortung der eigentlichen Fragestellung wurden drei Arzneimittelkandidaten mit unterschiedlichen Wirkmechanismen ausgewählt, von denen bekannt war, dass sie neurologische Symptome verursachen. Je Substanztest wurden zwei Hunde mit implantierten EEG-Sendern verwendet. Zwei der Substanzen wurden in eskalierenden intravenösen Dosen verabreicht, die dritte wurde als einzelne orale Dosis gegeben. Effekte der Substanzen auf klinische Symptome und auf das EEG wurden evaluiert. Parallel wurden Blut- und Liquor-Proben zur Bestimmung der Substanzspiegel und potentieller Biomarker genommen. Die Auswahl der Substanzen bot zusätzlich die Möglichkeit, die Empfindlichkeit der beiden regelmäßig in Arzneimittelprüfungen verwendeten Nicht-Nager Spezies Hund und Affe für neurologische Symptome vergleichend zu bewerten. Die Ergebnisse zeigen, dass die Kombination von Implantaten für EEG-Aufzeichnung und CSF-Probennahme möglich ist. Die intravenöse Applikation war der oralen Substanzgabe vorzuziehen, da die Variabilität der Substanz-Plasmaspiegel geringer war. Alle Symptome, die aus früheren toxikologischen Studien mit längerer Dauer bekannt waren, wurden ebenso beobachtet. Durch das Dosierungsschema war ihr Auftreten allerdings auf eine verkürzte Zeitspanne reduziert. Die kurze Studiendauer ermöglichte durchgehende klinische Beobachtung, somit die Erkennung aller Symptome und zeitnahe veterinärmedizinische Behandlungen, was im Sinne des Tierschutzes einen Vorteil darstellt. Für eine frühzeitige Erkennung von Krampfanfällen war das EEG nicht besser geeignet als klinische Beobachtung, da die Interpretation des ungefilterten EEG Signals durch das Auftreten von Artefakten erschwert war. Das Studiendesign, in dem das EEG angewendet wurde, ist zur Charakterisierung neurologischer Nebenwirkungen geeignet, da alle Symptome, die aus Studien mit längerer Dauer bekannt waren, ebenso beobachtet wurden. Durch die verkürzte Dauer wurde ermöglicht, Symptome und Substanzplasmaspiegel zu korrelieren und zeitnahe tierärztliche Behandlungen durchzuführen. Bei Substanzen, die ein hohes Risiko für neurologische Nebenwirkungen haben, kann dieses Studiendesign genutzt werden um im Vorfeld von behördlich geforderten toxikologischen Studien Dosierungen zu bestimmen, bei denen keine schweren neurologischen Nebenwirkungen zu erwarten sind