Evolution of Global Ocean Tide Levels Since the Last Glacial Maximum

This study addresses the evolution of global tidal dynamics since the Last Glacial Maximum focusing on the extraction of tidal levels that are vital for the interpretation of geologic sea-level markers. For this purpose, we employ a truly-global barotropic ocean tide model which considers the non-local effect of Self-Attraction and Loading. A comparison to a global tide gauge data set for modern conditions yields agreement levels of 65%–70%. As the chosen model is data-unconstrained, and the considered dissipation mechanisms are well understood, it does not have to be re-tuned for altered paleoceanographic conditions. In agreement with prior studies, we find that changes in bathymetry during glaciation and deglaciation do exert critical control over the modeling results with minor impact by ocean stratification and sea ice friction. Simulations of 4 major partial tides are repeated in time steps of 0.5–1 ka and augmented by 4 additional partial tides estimated via linear admittance. These are then used to derive time series from which the tidal levels are determined and provided as a global data set conforming to the HOLSEA format. The modeling results indicate a strengthened tidal resonance by M2, but also by O1, under glacial conditions, in accordance with prior studies. Especially, a number of prominent changes in local resonance conditions are identified, that impact the tidal levels up to several meters difference. Among other regions, resonant features are predicted for the North Atlantic, the South China Sea, and the Arctic Ocean

A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices

INTRODUCTION: About 30% of epilepsy patients are resistant to treatment with antiepileptic drugs, and only a minority of these are surgical candidates. A recent therapeutic approach is the application of electrical stimulation in the early phases of a seizure to interrupt its spread across the brain. To accomplish this, energy-efficient seizure detectors are required that are able to detect a seizure in its early stages. METHODS: Three patient-specific, energy-efficient seizure detectors are proposed in this study: (i) random forest (RF); (ii) long short-term memory (LSTM) recurrent neural network (RNN); and (iii) convolutional neural network (CNN). Performance evaluation was based on EEG data (n = 40 patients) derived from a selected set of surface EEG electrodes, which mimic the electrode layout of an implantable neurostimulation system. As for the RF input, 16 features in the time- and frequency-domains were selected. Raw EEG data were used for both CNN and RNN. Energy consumption was estimated by a platform-independent model based on the number of arithmetic operations (AOs) and memory accesses (MAs). To validate the estimated energy consumption, the RNN classifier was implemented on an ultra-low-power microcontroller. RESULTS: The RNN seizure detector achieved a slightly better level of performance, with a median area under the precision-recall curve score of 0.49, compared to 0.47 for CNN and 0.46 for RF. In terms of energy consumption, RF was the most efficient algorithm, with a total of 67k AOs and 67k MAs per classification. This was followed by CNN (488k AOs and 963k MAs) and RNN (772k AOs and 978k MAs), whereby MAs contributed more to total energy consumption. Measurements derived from the hardware implementation of the RNN algorithm demonstrated a significant correlation between estimations and actual measurements. DISCUSSION: All three proposed seizure detection algorithms were shown to be suitable for application in implantable devices. The applied methodology for a platform-independent energy estimation was proven to be accurate by way of hardware implementation of the RNN algorithm. These findings show that seizure detection can be achieved using just a few channels with limited spatial distribution. The methodology proposed in this study can therefore be applied when designing new models for responsive neurostimulation

A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection

The closed-loop application of electrical stimulation via chronically implanted electrodes is a novel approach to stop seizures in patients with focal-onset epilepsy. To this end, an energy efficient seizure detector that can be implemented in an implantable device is of crucial importance. In this study, we first evaluated the performance of two machine learning algorithms (Random Forest classifier and support vector machine (SVM)) by using selected time and frequency domain features with a limited need of computational resources. Performance of the algorithms was further compared to a detection strategy implemented in an existing closed loop neurostimulation device for the treatment of epilepsy. The results show a superior performance of the Random Forest classifier compared to the SVM classifier and the reference approach. Next, we implemented the feature extraction and classification process of the Random Forest classifier on a microcontroller to evaluate the energy efficiency of this seizure detector. In conclusion, the feature set in combination with Random Forest classifier is an energy efficient hardware implementation that shows an improvement of detection sensitivity and specificity compared to the presently available closed-loop intervention in epilepsy while preserving a low detection delay

230 days of ultra long‐term subcutaneous EEG : seizure cycle analysis and comparison to patient diary

© 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.We describe the longest period of subcutaneous EEG (sqEEG) monitoring to date, in a 35-year-old female with refractory epilepsy. Over 230 days, 4791/5520 h of sqEEG were recorded (86%, mean 20.8 [IQR 3.9] hours/day). Using an electronic diary, the patient reported 22 seizures, while automatically-assisted visual sqEEG review detected 32 seizures. There was substantial agreement between days of reported and recorded seizures (Cohen's kappa 0.664), although multiple clustered seizures remained undocumented. Circular statistics identified significant sqEEG seizure cycles at circadian (24-hour) and multidien (5-day) timescales. Electrographic seizure monitoring and analysis of long-term seizure cycles are possible with this neurophysiological tool.This work was supported by the Epilepsy Foundation’s Epilepsy Innovation Institute My Seizure Gauge Project. MPR is supported by the NIHR Biomedical Research Centre; the MRC Centre for Neurodevelopmental Disorders (MR/N026063/1); the EPSRC Centre for Predictive Modelling in Healthcare (EP/N014391/1); the RADAR‐CNS project (www.radar‐cns.org, grant agreement 115902).info:eu-repo/semantics/publishedVersio

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

High frequency oscillations (HFOs) are a promising biomarker of epileptic tissue, but detection of these electrographic events remains a challenge. Automatic detectors show encouraging results, but they typically require optimization of multiple parameters, which is a barrier to good performance and broad applicability. We therefore propose a new automatic HFO detection algorithm, focusing on simplicity and ease of implementation. It requires tuning of only an amplitude threshold, which can be determined by an iterative process or directly calculated from statistics of the rectified filtered data (i.e. mean plus standard deviation). The iterative approach uses an estimate of the amplitude probability distribution of the background activity to calculate the optimum threshold for identification of transient high amplitude events. We tested both the iterative and non-iterative approaches using a dataset of visually marked HFOs, and we compared the performance to a commonly used detector based on the root-mean-square. When the threshold was optimized for individual channels via ROC curve, all three methods were comparable. The iterative detector achieved a sensitivity of 99.6%, false positive rate (FPR) of 1.1%, and false detection rate (FDR) of 37.3%. However, in an eight-fold cross-validation test, the iterative method had better sensitivity than the other two methods (80.0% compared to 64.4 and 65.8%), with FPR and FDR of 1.3, and 49.4%, respectively. The simplicity of this algorithm, with only a single parameter, will enable consistent application of automatic detection across research centers and recording modalities, and it may therefore be a powerful tool for the assessment and localization of epileptic activity

Capturing of structural integrity by measuring the timedependent preload force in bolted joints

Schraubenverbindungen werden seit Jahrhunderten erfolgreich eingesetzt. Trotz anerkannter Auslegungs- und Montageverfahren kommt es immer wieder zu schadhaften Schraubenverbindungen, die im Extremfall zu Unfällen führen. Dies gilt es mit Hilfe der Zustandsüberwachung zu vermeiden. Die vorliegende Dissertation zielt darauf ab, mittels Sensorschrauben die Strukturintegrität zu erfassen und Schäden der Verbindung sowie der Struktur zu detektieren. Zusätzlich wird der Begriff des Bolt-Condition-Monitorings (BCM) eingeführt und definiert. Einleitend werden die empirisch ermittelten Eigenschaften der Sensorschraube unter Standardlastfällen dargestellt. Dann wird mittels verschiedener Prüfaufbauten die Detektion der einzelnen Schadensmechanismen von Schraubenverbindungen untersucht. Die anschließende Behandlung von Strukturschäden erfolgt an einem Windenergieanlagenturmmodell sowie an Flanschverbindungen. Abschließend wird das Betriebslastverhalten anhand eines Feldversuchs geprüft. Die Ergebnisse zeigen, dass durch die Wahl eines geeigneten Schädigungsindikators Schäden in Schraubenverbindungen und Strukturen frühzeitig erkannt werden können. Die dazu notwendige Methodik sowie das nötige Vorgehen wird illustriert und erläutert.Bolted joints haven been used successfully for centuries and have not yet reached the end of their development. Despite standards, simulations and new methods of assembly, defects in bolted joints still occur repeatedly, which in the worst case can lead to serious accidents. Condition monitoring aims to prevent these risks as well as avoiding production downtimes, serious damage to machines and danger to humans as well as the environment. By using proved and tested measurement technology to upgrade common bolts, the prevailing bolt load can be transmitted; and the bolt itself functions as a sensor. This thesis aims to use sensor bolts in order to measure structural integrity and detect damage of the connection as well as of the structure. Furthermore, the term bolt-condition-monitoring (BCM) is introduced and defined. The thesis is addressed to interested engineers and researchers in the fields of connection and measurement technology as well as condition monitoring. The empirically determined properties of the sensor bolts in cases of standard loads will be presented first. They serve as a valid basis for further investigations. Subsequently, the detection of the individual damage mechanisms of bolted joints is examined by means of various testing setups. The following treatment of structural damage is examined on the model of a wind turbine tower and flange connections. Finally, the operating load behavior is tested by means of a field trial. The results of these studies show that damages in bolted joints and structures can be detected at an early stage by selecting a suitable damage indicator and in certain cases in combination with a BCM function. This thesis shows and explains the required methods and processes