Contributions to an improved phenytoin monitoring and dosing in hospitalized patients

Phenytoin (PHT) is one of the mostly used and well established anticonvulsants for the treatment of epilepsy and a standard in the antiepileptic prophylaxis in adults with severe traumatic brain injuries before and after neurosurgical intervention. Its therapeutic use is challenging as PHT has a narrow therapeutic range and shows non-linear kinetics. It is extensively metabolized by a variety of CYP enzymes. PHT shows 85-95% binding to plasma proteins mostly albumin. This renders PHT also an important drug interaction candidate. Therefore, therapeutic drug monitoring is often required. A rational timing for good interpretation of the lab data translated in optimal individual dosing are necessary. Therapeutic guidance especially in teaching hospitals are needed and have to be implemented. Bayesian Forecasting (BF) versus conventional dosing (CD): a retrospective, long-term, single centre analysis In the hospital, medication management for effective antiepileptic therapy with PHT often needs rapid IV loading and subsequent dose adjustment according to TDM. To investigate PHT performance in reaching therapeutic target serum concentration, a BF regimen was compared to CD, according to the official summary of product characteristics. In a Swiss acute care teaching hospital (Kantonsspital Aarau), a retrospective, single centre, and long-term analysis was assessed by using all PHT serum tests from the central lab from 1997 to 2007. The BF regimen consisted of a guided, body weight-adapted rapid IV PHT loading over five days with pre-defined TDM time points. The CD was applied without written guidance. Assuming non-normally distributed data, non-parametric statistical methods were used. A total of 6’120 PHT serum levels (2’819 BF and 3’301 CD) from 2’589 patients (869 BF and 1’720 CD) were evaluated and compared. 63.6% of the PHT serum levels from the BF group were within the therapeutic range versus only 34.0% in the CD group (p<0.0001). The mean BF serum level was 52.0 ± 22.1 µmol/L (within target range), whereas the mean serum level of the CD was 39.8 ± 28.2 µmol/L (sub-target range). In the BF group, men had small but significantly lower PHT serum levels compared to women (p<0.0001). The CD group showed no significant gender difference (p=0.187). A comparative sub-analysis of age-related groups (children, adolescents, adults, seniors, and elderly) showed significant lower target levels (p<0.0001) for each group in the CD group, compared to BF. Comparing the two groups, BF showed significantly better performance in reaching therapeutic PHT serum levels. Free PHT assessment However, total serum drug levels of difficult-to-dose drugs like PHT are sometimes insufficient. The knowledge of the free fraction is necessary for correct dosing. In a subgroup analysis of the above BF vs. CD study we evaluated the suitability of the Sheiner-Tozer algorithm to calculate the free PHT fraction in hypoalbuminemic patients. Free PHT serum concentrations were calculated from total PHT concentration in hypoalbuminemic patients and compared with the measured free PHT. The patients were separated into two groups (a low albumin group; 35 ≤ albumin ≥ 25 g/L and a very low albumin group; albumin < 25 g/L). These two groups were compared and statistically analysed for the calculated and the measured free PHT concentration. The calculated (1.2 mg/L, SD=0.7) and the measured (1.1 mg/L, SD=0.5) free PHT concentration correlated. The mean difference in the low and the very low albumin group was 0.10 mg/L (SD=1.4, n=11) and 0.13 mg/L (SD=0.24, n=12), respectively. Although the variability of the data could be a bias, no statistically significant difference between the groups was found: t-test (p=0.78), the Passing-Bablok regression, the Spearman’s rank correlation coefficient of r=0.907 and p=0.00, and the Bland-Altman plot including the regression analysis between the calculated and the measured value (M=0.11, SD=0.28). We concluded that in absence of a free PHT serum concentration measurement also in hypoalbuminemic patients, the Sheiner-Tozer algorithm represents a useful tool to assist TDM to calculate or control free PHT by using total PHT and the albumin concentration. GC-MS Analysis of biological PHT samples To correlate PHT blood serum levels, with “brain PHT levels” (the site of action of PHT), extracellular fluid from microdialysates in neurosurgical patients could be analyzed for PHT by an appropriate quantifying analytical method. In this investigation we describe the development and validation of a sensitive gas chromatography–mass spectrometry (GC–MS) method to identify and quantitate PHT in brain microdialysate, saliva and blood from human samples. For sample clean-up a SPE was performed with a nonpolar C8-SCX column. The eluate was evaporated with nitrogen (50°C) and derivatized with trimethylsulfonium hydroxide before GC-MS analysis. 5-(p-methylphenyl)-5-phenylhydantoin was used as internal standard. The MS was run in scan mode and the identification was made with three ion fragment masses. All peaks were identified with MassLib. Spiked PHT samples showed recovery after SPE of ≥ 94%. The calibration curve (PHT 50 to 1’200 ng/ml, n=6 at six concentration levels) showed good linearity and correlation (r2 > 0.998). The limit of detection was 15 ng/mL, the limit of quantification was 50 ng/mL. Dried extracted samples were stable within a 15% deviation range for ≥ 4 weeks at room temperature. The method met International Organization for Standardization standards and was able to detect and quantify PHT in different biological matrices and patient samples. The GC-MS method with SPE is specific, sensitive, robust and well reproducible and therefore, an appropriate candidate for pharmacokinetic assessment of PHT concentrations in different biological samples of treated patients

A Meta-GNN approach to personalized seizure detection and classification

In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We achieve this by combining two novel paradigms that have recently seen much success in a wide variety of real-world applications: graph neural networks (GNN), and meta-learning. We train a Meta-GNN based classifier that learns a global model from a set of training patients such that this global model can eventually be adapted to a new unseen patient using very limited samples. We apply our approach on the TUSZ-dataset, one of the largest and publicly available benchmark datasets for epilepsy. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients

Towards developing a reliable medical device for automated epileptic seizure detection in the ICU

Abstract. Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced. The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients

Accelerometry based detection of epileptic seizures

Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there is still a large group of epilepsy patients that continues to have frequent seizures. For these patients automated detection of epileptic seizures can be of great clinical importance. Seizure detection can influence daily care or can be used to evaluate treatment effect. Furthermore automated detection can be used to trigger an alarm system during seizures that might be harmful to the patient. This thesis focusses on accelerometry (ACM) based seizure detection. A detailed overview is provided, on the perspectives for long-term epilepsy monitoring and automated seizure detection. The value of accelerometry for seizure detection is shown by means of a clinical evaluation and the first steps are made towards automatic detection of epileptic seizures based on ACM. With accelerometers movements are recorded. A large group of epileptic seizures manifest in specific movement patterns, so called motor seizures. Chapter 2 of this thesis presents an overview of the published literature on available methods for epileptic seizure detection in a long-term monitoring context. Based on this overview recommendations are formulated that should be used in seizure detection research and development. It is shown that for seizure detection in home environments, other sensor modalities besides EEG become more important. The use of alternative sensor modalities (such as ACM) is relatively new and so is the algorithm development for seizure detection based on these measures. It was also found that for both the adaptation of existing techniques and the development of new algorithms, clinical information should be taken more into account. The value of ACM for seizure detection is shown by means of a clinical evaluation in chapter 3. Here 3-D ACM- and EEG/video-recordings of 18 patients with severe epilepsy are visually analyzed. A striking outcome presented in this chapter is the large number of visually detected seizures versus the number of seizures that was expected on forehand and the number of seizures that was observed by the nurses. These results underscore the need for an automatic seizure detection device even more, since in the current situation many seizures are missed and therefore it is possible that patients do not get the right (medical) treatment. It was also observed that 95% of the ACM-patterns during motor seizures are sequences of three elementary patterns: myoclonic, tonic and clonic patterns. These characteristic patterns are a starting point for the development of methods for automated seizure detection based on ACM. It was decided to use a modular approach for the detection methodology and develop algorithms separately for motor activity in general, myoclonic seizures and tonic seizures. Furthermore, clinical information is incorporated in the detection methodology. Therefore in this thesis features were used that are either based on the shape of the patterns of interest as described in clinical practice (chapter 4 and 7), or the features were based on a physiological model with parameters that are related to seizure duration and intensity (chapter 5 and 6). In chapter 4 an algorithm is developed to distinguish periods with and without movement from ACM-data. Hence, when there is no movement there is no motor seizure. The amount of data that needs further analysis for seizure detection is thus reduced. From 15 ACM-signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space a linear threshold function is used for classification. For training and testing the algorithm ACM data along with video data are used from nocturnal recordings in mentally retarded patients with severe epilepsy. Using this algorithm the amount of data that needs further analysis is reduced considerably. The results also indicate that the algorithm is robust for fluctuations across patients and thus there is no need for training the algorithm for each new patient. For the remaining data it needs to be established whether the detected movement is seizure related or not. To this purpose a model is developed for the accelerometer pattern measured on the arm during a myoclonic seizure (chapter 5). The model consists of a mechanical and an electrophysiological part. This model is used as a matched wavelet filter to detect myoclonic seizures. In chapter 6 the model based wavelet is compared to three other time frequency measures: the short time Fourier transform, the Wigner distribution and the continuous wavelet transform using a Daubechies wavelet. All four time-frequency methods are evaluated in a linear classification setup. Data from mentally retarded patients with severe epilepsy are used for training and evaluation. The results show that both wavelets are useful for detection of myoclonic seizures. On top of that, our model based wavelet has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiological meaningful. Besides myoclonic seizures, the model is also useful for the detection of clonic seizures; physiologically these are repetitive myoclonic seizures. Finally for the detection of tonic seizures, in chapter 7 a set of features is studied that incorporate the mean characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification in the multi-dimensional feature space. For training and testing the algorithm, again data are used from recordings in mentally retarded patients with severe epilepsy. The results show that our approach is useful for the automated detection of tonic seizures based on 3-D ACM and that it is a promising contribution in a complete multi-sensor seizure detection setup

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 μW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions

EEG-Biofeedback and epilepsy: concept, methodology and tools for (neuro)therapy planning and objective evaluation

EEG-Biofeedback and Epilepsy: Concept, Methodology and Tools for (Neuro)therapy Planning and Objective Evaluation ABSTRACT Objective diagnosis and therapy evaluation are still challenging tasks for many neurological disorders. This is highly related to the diversity of cases and the variety of treatment modalities available. Especially in the case of epilepsy, which is a complex disorder not well-explained at the biochemical and physiological levels, there is the need for investigations for novel features, which can be extracted and quantified from electrophysiological signals in clinical practice. Neurotherapy is a complementary treatment applied in various disorders of the central nervous system, including epilepsy. The method is subsumed under behavioral medicine and is considered an operant conditioning in psychological terms. Although the application areas of this promising unconventional approach are rapidly increasing, the method is strongly debated, since the neurophysiological underpinnings of the process are not yet well understood. Therefore, verification of the efficacy of the treatment is one of the core issues in this field of research. Considering the diversity in epilepsy and its various treatment modalities, a concept and a methodology were developed in this work for increasing objectivity in diagnosis and therapy evaluation. The approach can also fulfill the requirement of patient-specific neurotherapy planning. Neuroprofile is introduced as a tool for defining a structured set of quantifiable measures which can be extracted from electrophysiological signals. A set of novel quantitative features (i.e., percentage epileptic pattern occurrence, contingent negative variation level difference measure, direct current recovery index, heart rate recovery ratio, and hyperventilation heart rate index) were defined, and the methods were introduced for extracting them. A software concept and the corresponding tools (i.e., the neuroprofile extraction module and a database) were developed as a basis for automation to support the methodology. The features introduced were investigated through real data, which were acquired both in laboratory studies with voluntary control subjects and in clinical applications with epilepsy patients. The results indicate the usefulness of the introduced measures and possible benefits of integrating the indices obtained from electroencephalogram (EEG) and electrocardiogram for diagnosis and therapy evaluation. The applicability of the methodology was demonstrated on sample cases for therapy evaluation. Based on the insights gained through the work, synergetics was proposed as a theoretical framework for comprehending neurotherapy as a complex process of learning. Furthermore, direct current (DC)-level in EEG was hypothesized to be an order parameter of the brain complex open system. For future research in this field, investigation of the interactions between higher cognitive functions and the autonomous nervous system was proposed. Keywords: EEG-biofeedback, epilepsy, neurotherapy, slow cortical potentials, objective diagnosis, therapy evaluation, epileptic pattern quantification, fractal dimension, contingent negative variation, hyperventilation, DC-shifts, instantaneous heart rate, neuroprofile, database system, synergetics.Die Epilepsie ist eine komplexe neurologische Erkrankung, die auf biochemischer und physiologischer Ebene nicht ausreichend geklärt ist. Die Vielfalt der epileptischen Krankheitsbilder und der Behandlungsmodalitäten verursacht ein Defizit an quantitativen Kenngrößen auf elektrophysiologischer Basis, die die Objektivität und die Effizienz der Diagnose und der Therapieevaluierung signifikant erhöhen können. Die Neurotherapie (bzw. EEG-Biofeedback) ist eine komplementäre Behandlung, die bei Erkrankungen, welche in Zusammenhang mit Regulationsproblemen des Zentralnervensystems stehen, angewandt wird. Obwohl sich die Applikationen dieser unkonventionellen Methode erweitern, wird sie nach wie vor stark diskutiert, da deren neuro- und psychophysiologischen Mechanismen wenig erforscht sind. Aus diesem Grund ist die Ermittlung von Kenngrößen als elektrophysiologische Korrelaten der ablaufenden Prozesse zur objektiven Einstellung und Therapievalidierung eines der Kernprobleme des Forschungsgebietes und auch der vorliegenden Arbeit. Unter Berücksichtigung der aktuellen neurologischen Erkenntnisse und der durch Untersuchungen an Probanden, sowie an Epilepsie-Patienten gewonnenen Ergebnisse, wurden ein Konzept und eine Methodologie entwickelt, um die Objektivität in der Diagnose und Therapieevaluierung zu erhöhen. Die Methodologie basiert auf einem Neuroprofil, welches als ein signalanalytisches mehrdimensionales Modell eingeführt wurde. Es beschreibt einen strukturierten Satz quantifizierbarer Kenngrößen, die aus dem Elektroenzephalogramm (EEG), den ereignisbezogenen Potentialen und dem Elektrokardiogramm extrahiert werden können. Als Komponenten des Neuroprofils wurden neuartige quantitative Kenngrößen (percentage epileptic pattern occurrence, contingent negative variation level difference measure, direct current recovery index, heart rate recovery ratio, hyperventilation heart rate index) definiert und die Methoden zu deren Berechnung algorithmisiert. Die Anwendbarkeit der Methodologie wurde beispielhaft für die Evaluierung von Neurotherapien an Epilepsie-Patienten demonstriert. Als Basis für eine zukünftige Automatisierung wurden ein Softwarekonzept und entsprechende Tools (neuroprofile extraction module und die Datenbank ?NeuroBase?) entwickelt. Der Ansatz erfüllt auch die Anforderungen der patientenspezifischen Therapieplanung und kann auf andere Krankheitsbilder übertragen werden. Durch die neu gewonnenen Erkenntnisse wurde die Synergetik als ein theoretischer Rahmen für die Analyse der Neurotherapie als komplexer Lernprozess vorgeschlagen. Es wurde die Hypothese aufgestellt, dass das Gleichspannungsniveau im EEG ein Ordnungsparameter des Gehirn ist, wobei das Gehirn als ein komplexes offenes System betrachtet wird. Für zukünftige Forschungen auf dem Gebiet wird empfohlen, die Wechselwirkungen zwischen den höheren kognitiven Funktionen und dem autonomen Nervensystem in diesem Kontext zu untersuchen