TOKEYA

Tokeya Deep Data Dive GmbH & Co. KG is investigating EEG data from patients with MDD (major depression disease) and AD (Alzheimer’s disease) using methodsfrom AI, time series analysis and the theory of dynamic systems. The aim of these analyses is to develop methods that considerably improve the early detection of Alzheimer’s dementia and at the same time provide a differentiation of age-related depressions in order to prevent mistreatment.

In the following pictures, the first picture shows the course of the EEG for one electrode at two test persons. The second picture shows the associated “Higuchi Fractal Dimension” (HFD).The lower the HFD, the smoother the EEG raw data curve, even if it has more outliers than the reference curve. If, for example, the HFD in depression patients has a value below 1.6, this is significant for the presence of MDD. In Alzheimer’s the same applies modified: the lower the activity, the smoother the curve and the lower the HFD.In this way, a complete EEG data analysis using a classification with advanced AI methods provides an early diagnosis of the presence of the disease.

We are pleased to announce that Tokeya Deep Data Dive GmbH & Co. KG and A. Eberle GmbH & Co. KG, in cooperation with Fraunhofer ISE, the Chair of Energy Systems and Energy Management at Kaiserslautern University of Technology and innogy SE, have submitted a joint funding application to the German Federal Ministry of Economics (BMWi) entitled “LVFACDS - LowVoltage Flexible Alternating Current Distribution Systems”.

The content of the grant application refers to the development of an intelligent load flow control system with prediction of the state of the power grid by means of AI methods in order to ensure an intelligent adaptation of the load with increasing electromobility and the use of power flow controlling line regulators for the adaptive operation of low-voltage grids.