NeuroAssist - decision support system for assisting treatment and diagnosis of neurological disorders
In this project we will develop NeuroAssist - a decision support system for assisting treatment and diagnosis of neurological disorders - and integrate it tightly into current hospital information systems. Based on SYMPTOMA’s search engine, information from many different sources of medical data will be combined and physicians will be reminded of rare disease while getting an overview of what is most common for a patient with the respective set of findings. The decision support system will get its input form newly developed tools for automatic computer assisted EEG analyses and a new automatic semantic analysis of written neurological reports for the extraction of clinical findings. To make the system usable for as many hospitals as possible we will define a new standardized DICOM waveform format for EEG storage and propose it to the standardization committee. In addition, NeuroAssist will support medical research and will make the information of different sources accessible to the research community.
NeuroOnline - EEG web-based service
In this project we will develop a web-based service which will include tools for computerassisted analysis of long-term electroencephalographic (EEG) data for the diagnosis of neurological disorders as well as for polysomnographic (PSG) data for the diagnosis of sleep disorders. Based on the existing background on deep-learning in the consortium we will develop algorithms and web-based visualization tools for sleep staging, sleep apnea detection, and ICU-EEG-monitoring. Furthermore our developments will result in a technology for data streaming, - archiving, and exchange across departments. The integration of the computational results into the electronic health record and the conversation of proprietary data formats in standard formats (DICOM) are further important goals. This project will pave the way for national health services such as ELGA (AUT) and EFA (GER) to include EEG/PSG data in the document exchange infrastructures.
EpiMon - Monitoring EEG in the intensive care
According to current studies, up to 18% of all patients with severe brain diseases at intensive care units suffer from unrecognized non-convulsive epileptic seizures or status epileptici, which lead to significantly increased mortality or permanent neurological impairments. They are clinically incomprehensible and can be recognized reliably exclusively when long-term recording with EEG (cEEG) are performed. However, in clinical practice cEEGs are only available in exceptional cases. The reason for this is above all the time-consuming visual analysis of the very large amounts of EEG signals, which also requires the expertise of a physician experienced in EEG analysis. The lack of EEG monitoring of these patients thus represents a clinically relevant, largely unsolved problem. A computer-assisted, continuous automatic evaluation of the EEG signals can provide a significant contribution here and thus enable automatic alerting in case of an epileptic attack. Based on the highly reliable algorithms for the detection of epileptic seizures in patients undergoing pre-surgical evaluation, the problem of seizure detection in patients with severe brain diseases is now to be solved. In the project new mathematical methods for the detection of non-convulsive epileptic seizures in EEG signals of these patients were developed as well as correlations between the EEG signal and other physiological parameters such as heart rate, blood pressure, pO2 or brain pressure were be investigated.
DeNeCoR - Devices for NeuroControl and NeuroRehabilitation
The project addresses the diagnosis and treatment of neurological diseases which will have an increased incidence due to the aging society. DeNeCoR will develop, test and demonstrate neuro devices, systems and methods for a diverse set of diagnostic and treatment modalities.
Start: June 1st, 2013
ZIT - EEG monitoring for Stroke
Within the scope of the project, a system consisting of hardware and software is being developed, which is to be used for the rapid analysis of long-term EEG recordings in stroke patients. For the first time, our results will enable continuous EEG monitoring of brain function in stroke patients with reasonable resources and improve the quality of the diagnosis in this field of application.
HIGH-throughput production of functional 3D images of the brain
HIGH PROFILE aims to “establish an overall system approach for healthcare, based on an integrated system concept of seamless integration of interoperable components”. HIGH PROFILE addresses this explicitly in the field of Advanced Imaging Systems. The project will elevate the state-of-the-art by integrating imaging equipment for neurological diagnostics to support improved diagnosis.
Central Nervous System Imaging - advances of 3D imaging techniques (EEG, fMRI, PET)
This three-year ENIAC (European Nanoelectronics Initiative Advisory Council) project aims to achieve substantial advances in state-of-the-art medical 3D-imaging platforms by focusing on the diagnosis and therapy of serious diseases of the central nervous system and brain. Key medical-imaging technologies will be significantly enhanced by means of major improvements in sensors, equipment and computing platforms to boost early diagnostics and prevention capability while reducing total equipment costs.
ZIT - Spike detection
Within the framework of the proposed project, a new method is to be developed to recognize spikes in EEG data from epilepsy patients. Epilepsy is one of the most common neurological disorders. About one per cent of the world's population suffers from this disease, with about 80,000 people in Austria alone. Spikes are characteristic patterns in the brain signal of an epileptic. A new method is to be developed, which automatically determines the spike frequency and the morphology of the spikes from the brain signals.