FB Technik
Filtern
Dokumenttyp
Volltext vorhanden
- ja (13)
Gehört zur Bibliographie
- nein (13)
Schlagworte
- Elektrode (2)
- Flüssigkristalline Polymere (2)
- Parkinson-Krankheit (2)
- Rauigkeit (2)
- electrode model (2)
- surface roughness (2)
- Algorithmus (1)
- Astrozyt (1)
- BEV (1)
- Bewegung (1)
Institut
- FB Technik (13) (entfernen)
Decoding the cellular network interaction of neurons and glial cells are important in the development of new therapies for diseases of the central nervous system (CNS). Electrophysiological in vivo studies in mice will help to understand the highly complex network. In this paper, the optimization of epidural liquid crystal polymer (LCP) electrodes for different platinum electroplating parameters are presented and compared. Constant current and pulsed current electroplating varied in strength and duration was used to decrease the electrode impedance and to increase the charge storage capacity (CSCc). In best cases, both methods generated similar results with an impedance reduction of about 99%. However, electroplating with pulsed currents was less parameter-dependent than the electroplating with constant current. The use of ultrasound was essential to generate platinum coatings without plating defects. Electrode model parameters extracted from the electrode impedance reflected the increase in surface porosity due to the electroplating processes.
Abstract: This paper is about detecting the difference between fully-random and semi-random shuffleing data sets, with the use of unsupervised learning algorithms. Because of the limits of the k-means algorithm alone, a recurrent autoencoder is used for feature extraction to improve the results of k-means. In the next step the autoencoder alone is used for clustering.
Introduction: In the last years, machine learning has been used more and more in different areas and it is also appropriate for for pattern recognition in data. Random data is characterized through the missing of defined patterns. Permutations without repetitions have the highest amount of entropy for a sequence of its length, which is similar to random data according to Andrei Kolmogorov, who states that random data have the highest amount of information and can’t be compressed. Therefore, this paper analyses the difference between random permutations and good shuffled permutations, which have some remaining patterns left. This is done via a recurrent autoencoder.
Deep brain stimulation (DBS) is an established therapy for movement disorders such as in Parkinson's disease (PD) and essential tremor (ET). Adjusting the stimulation parameters, however, is a labour-intensive process and often requires several patient visits. Physicians prefer objective tools to improve (or maintain) the performance in DBS. Wearable motion sensors (WMS) are able to detect some manifestations of pathological signs, such as tremor in PD. However, the interpretation of sensor data is often highly technical and methods to visualise tremor data of patients undergoing DBS in a clinical setting are lacking. This work aims to visualise the dynamics of tremor responses to DBS parameter changes with WMS while patients performing clinical hand movements. To this end, we attended DBS programming sessions of two patients with the aim to visualise certain aspects of the clinical examination. PD tremor and ET were effectively quantified by acceleration amplitude and frequency. Tremor dynamics were analysed and visualised based on setpoints, movement transitions and stability aspects. These methods have not yet been employed and examples demonstrate how tremor dynamics can be visualised with simple analysis techniques. We therefore provide a base for future research work on visualisation tools in order to assist clinicians who frequently encounter patients for DBS therapy. This could lead to benefits in terms of enhanced evaluation of treatment efficacy in the future.