Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study.

Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine

Auditory stimulation and deep learning predict awakening from coma after cardiac arrest.

Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies.

Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI). Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2). The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance. While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology

Frequency and evolution of sleep-wake disturbances after ischemic stroke: A 2-year prospective study of 437 patients.

OBJECTIVE In the absence of systematic and longitudinal data, this study prospectively assessed both frequency and evolution of sleep-wake disturbances (SWD) after stroke. METHODS In 437 consecutively recruited patients with ischemic stroke or transient ischemic attack (TIA), stroke characteristics and outcome were assessed within the 1st week and 3.2 ± 0.3 years (M±SD) after the acute event. SWD were assessed by interview and questionnaires at 1 and 3 months as well as 1 and 2 years after the acute event. Sleep disordered breathing (SDB) was assessed by respirography in the acute phase and repeated in one fifth of the participants 3 months and 1 year later. RESULTS Patients (63.8% male, 87% ischemic stroke and mean age 65.1 ± 13.0 years) presented with mean NIHSS-score of 3.5 ± 4.5 at admission. In the acute phase, respiratory event index was >15/h in 34% and >30/h in 15% of patients. Over the entire observation period, the frequencies of excessive daytime sleepiness (EDS), fatigue and insomnia varied between 10-14%, 22-28% and 20-28%, respectively. Mean insomnia and EDS scores decreased from acute to chronic stroke, whereas restless legs syndrome (RLS) percentages (6-9%) and mean fatigue scores remained similar. Mean self-reported sleep duration was enhanced at acute stroke (month 1: 07:54 ± 01:27h) and decreased at chronic stage (year 2: 07:43 ± 01:20h). CONCLUSIONS This study documents a high frequency of SDB, insomnia, fatigue and a prolonged sleep duration after stroke/TIA, which can persist for years. Considering the negative effects of SWD on physical, brain and mental health these data suggest the need for a systematic assessment and management of post-stroke SWD

Simulation of corticogenesis as a self-organizing system

During the past two decades, the focus of biological investigation has begun to shift from reductive analysis of low level mechanisms toward an understanding of how these mechanisms interact in complex ways to elicit large scale organized processes. This shift is clearly seen in neuroscience and molecular biology where there is a thrust to understand the behavioral expression of neuronal interactions, or the control of biochemical pathways in terms of molecular networks. Simulation is a useful tool for exploring such complex processes because it permits rigorous analysis of observed global behavior in terms of the mechanistic axioms declared in the simulated model. In the first part of this thesis (Chapter 2 and 3), we present CX3D, a software package for simulation of neural growth and network development in a physically realistic 3D environment. Chapter 2 describes the physical properties of tissue in our simulator: Neurons are discretized into spheres (for the soma) and chains of cylinders (for the neurites). Three types of mechanical interactions are applied to these discrete neuronal elements: inter-object forces when two elements are in close contact, intra-object forces if neurites are stretched and biological movements (cell movements). These three forces are summed up, and each object moves accordingly. To maintain a neighborhood relation between the objects in the simulator, we use a dynamic Delaunay triangulation. This triangulation (or more exactly its dual graph) provides us with a decomposition of the extracellular space, which we use for the simulation of diffusing of signaling molecules. Chapter 3 describes the software architecture of CX3D, and its four levels of abstraction: the Delaunay triangulation, the physical level (mechanical and diffusive properties) and two levels that are used by the modeler to code the specificities of the cells. We describe how the biological properties of neurons in our simulator are encapsulated into small modules linked to particular physical objects. A commented example guides the reader through the re-implementation, in our simulator, of a famous model of axonal branching proposed by van Ooyen and collaborators. We also present several other models of neural development, illustrating the versatility of our simulator. The second part of the thesis (Chapter 3 and 4) introduces a language for the control and explicit programming of self-assembling of cortical circuits. In Chapter 3, we formalize the description of neural development by defining a set of primitive actions taken locally by neural precursors during corticogenesis. These primitives can be combined into networks of instructions, similar to biochemical pathways, capable of reproducing arbitrarily complex developmental sequences in a biologically plausible way. Furthermore, the conditional activation and deactivation of these instruction networks can also be controlled by the usage of these primitives, allowing for the design of a ‘genetic code’, with coding elements and regulating elements. We show in simulation how such a code can be incorporated into a single initial progenitor, that then reproduces the major steps of corticogenesis, developing into a multilayer connected neural network. In the final Chapter, we apply the same formalism to more detailed models of corticogenesis from mitotic division of progenitor cells, through formation of distinct populations of different cell types, migration of neuronal precursors to form the cortical laminae and finally extension of axons and dendrites reproducing the experimentally determined branching patterns of neurons in the cat visual cortex. Zunehmend richtet sich in der Biologie der Focus von der Analyse der Basisprozesse hin zum Verstehen der Integration der Mechanismen in höheren Organisationsstufen. Dieser Wechsel ist klar sichtbar in Neurowissenschaften und Molekularbiologie; das Ziel ist, zu verstehen wie biochemische Prozesse neuronale Netzwerke bilden, und diese dann Verhalten. Simulation ist ein wichtiges Instrument zur Untersuchung solch komplexer Prozesse: man kann exakt untersuchen wie globales System-Verhalten aus den mechanistischen Axiomen in einem Modell entsteht. Im ersten Teil der Dissertation präsentieren wir CX3D, ein Programm zur Simulation von Netzwerkbildung aus Neuronenvermehrung in einer physikalisch realistischen 3D-Welt. Kapitel 2 beschreibt die physikalischen Eigenschaften von Zellgeweben in unserem Simulator. Die Neuronen sind repräsentiert als Objekte mit mehreren diskreten Elementen: eine Kugel (Zellkörper) verbunden mit Ketten von Zylindern (Axon und Dendriten). Drei Arten von Wirkungen beeinflussen die Objekte/Elemente: Mechanische Kräfte zwischen Objekten (Kollisonen zwischen Neuronen) und innerhalb Objekten (z.B. Zugkräfte im Axon), und Selbstbewegung von Zellen. Ihre Summe bestimmt die Objektbewegungen. Zur Bestimmung von Nachbarschafts-Beziehungen verwenden wir eine dynamische Delauny-Triangulation (die dual graph Methode). Dies erlaubt eine Raumaufteilung, die auch für die Simulation der Diffusion von Signalmolekülen wichtig ist. Kapiel 3 beschreibt weitere Aspekte von CX3D; die vier Abstraktions-Ebenen: die Triangulation, die Mechanik/Diffusions- Vorgänge, und zwei Ebenen für die Kodierung spezifischer Zelleigenschaften. Wir erklären, wie die biologischen Eigenschaften in Module eingegeben werde, welche mit Objekten verbunden werden. Ein Beispiel führt den Leser durch unsere Simulation des berühmten Modells für Axon-Verzweigung von Van Ooyen u. Mitarbeitern. Noch andere Beispiele für Neuronen-Entwicklung zeigen die mannigfaltige Anwendbarkeit unseres Simulators. Der zweite Teil stellt eine neue Programmierung zur Kontrolle von sich selbst bildenden Netzwerken vor. Wir formalisieren dies mit der Definition einer Serie von Elementar-Funktionen der unreifen Zellen. Deren Kombination führt zu vernetzten Informationen ähnlich wie biochemische Prozesse und erlaubt für arbiträr komplexe Entwicklungen auf eine biologisch plausible Weise. Konditionelle Aktivierung/Desaktivierung von Funktionen wird ebenfalls durch Elementar-Funktionen kontrolliert wie durch einen genetischen Code, der auch die Codierung reguliert. Wir zeigen, wie eine einzige Zelle, mit eingebautem Instruktions-Code, wichtige Stadien der Formation des Kortex reproduzieren kann, mit Bildung eines mehrschichtigen Neuronen-Netzwerkes. Im Schlusskapitel verwenden wir den Formalismus für mehr detaillierte Modelle für Kortikogenese durch mitotisch aktive Zellen: von der Bildung diskreter Populationen von different Zell-Typen, und der Formation von Kortex-Schichten durch Zellmigrationen, bis zum Auswachsen von Axonen und Dendriten, das die für die Seh-Rinde der Katze beschriebenen Verästelungen reproduziert

Isolated mandibular sleep-related rhythmic movement disorder: A case report.

Background: Rhythmic masticatory muscle activity (RMMA) in sleep is usually not considered pathological unless associated with bruxism. On the other hand, so-called sleep-related rhythmic movement disorders (SRRMD) are a recognized category of sleep disorders, which involve prolonged rhythmic activity of large muscle groups, such as the whole body, the head, or a limb, but typically not the masticatory muscles.Clinical Presentation: A polysomnographic description of a patient with symptomatic RMMA without bruxism, fulfilling the diagnostic criteria of an SRRMD, is presented. The symptoms were initially misdiagnosed as bruxism and then as sleep-related epilepsy, which delayed an adequate treatment. Therapy of the comorbid obstructive sleep apnea with a positive airway pressure device (APAP) led to a self-reported improvement.Conclusion: The differential diagnosis of jaw movement in sleep is vast; a correct diagnosis is of the essence for adequate treatment. The prevalence of isolated RMMA resulting in perturbation of sleep warrants further exploration

Ein Fall von limbischer Autoimmunenzephalitis mit seriellen Anfällen

Die limbische Autoimmunenzephalitis ist oftmals von epileptischen Anfällen begleitet und tritt als paraneoplastisches Syndrom, aber auch rein als Autoimmunerkrankung ohne obligatorische Assoziation mit einem Tumor auf. Die Diagnosestellung ist aufgrund der Vielfalt der klinischen Primärpräsentation schwierig und erfolgt häufig verzögert, was mit einem schlechteren klinischen Outcome assoziiert sein kann. Hinsichtlich der Therapie fehlen derzeit noch grössere kontrollierte klinische Studien. Empfohlen wird eine Immuntherapie und – sofern vorhanden und möglich – eine Behandlung der assoziierten Tumorerkrankung. Begleitende epileptische Anfälle zeigen häufig ein unzureichendes Ansprechen auf anfallsunterdrückende Medikamente. Wir präsentieren den Fall einer klassischen paraneoplastischen limbischen Enzephalitis, die sich mit schwierig zu therapierenden epileptischen Anfällen manifestierte