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.

Huge increase of therapeutic window at a bioactive silver/titania nanocomposite coating surface compared to solution.

A silver containing coating used in the human body, e.g., on an implant should be both effectively antimicrobial and non-cytotoxic to human cells. It is generally believed that the biologic effect originates from silver ions released from the coating. Nanocomposites with well controlled Ag filling factor were prepared by co-sputtering, and the silver surface concentration and the silver release were determined by XPS and ICP-MS, respectively. Here we show that only a small therapeutic window exists for dissolved silver but the therapeutic window is largely increased at the surface. While the toxicity observed for mammalian cells in contact with the bioactive Ag/TiO2 nanocomposite surface and for silver ions in solution is rather similar the antimicrobial activity is drastically enhanced at the surface. A model is proposed to explain the strong increase of the antimicrobial activity at the surface. The present results not only question well-established tests for antimicrobial activity but they are also important for the design of antimicrobial coatings, e.g., for biomedical devices.

Conformational adaptation and selective adatom capturing of tetrapyridyl-porphyrin molecules on a copper (111) surface.

We present a combined low-temperature scanning tunneling microscopy and near-edge X-ray adsorption fine structure study on the interaction of tetrapyridyl-porphyrin (TPyP) molecules with a Cu(111) surface. A novel approach using data from complementary experimental techniques and charge density calculations allows us to determine the adsorption geometry of TPyP on Cu(111). The molecules are centered on "bridge" sites of the substrate lattice and exhibit a strong deformation involving a saddle-shaped macrocycle distortion as well as considerable rotation and tilting of the meso-substituents. We propose a bonding mechanism based on the pyridyl-surface interaction, which mediates the molecular deformation upon adsorption. Accordingly, a functionalization by pyridyl groups opens up pathways to control the anchoring of large organic molecules on metal surfaces and tune their conformational state. Furthermore, we demonstrate that the affinity of the terminal groups for metal centers permits the selective capture of individual iron atoms at low temperature.