Mobile, smart neurosensor system for the detection and documentation of epileptic seizures in everyday life
Epilepsy is one of the most common neurological diseases worldwide. Its characteristic symptom is recurrent epileptic seizures, which are very stressful for those affected. Recognizing and documenting epileptic seizures in everyday life is essential for individual therapy. Exactly recorded biosignals at the time of the seizure in everyday processes help in the classification of epilepsy disorders, in an optimal dosage of medication - or even in the development of systems for early warning. Despite intensive research, reliable detection of epileptic seizures with sensors suitable for everyday use is a major challenge. There is also a lack of digitally supported forms of care.
In the "MOND" project, an AI-based sensor system for the automated detection of epileptic seizures in everyday life is being developed based on the results of the "EPITECT" project. Data acquisition is to be performed via ear-worn, mobile sensor technology, which, with particular focus, is also to enable mobile derivation of an electroencephalogram (EEG). An EEG represents electrical activities of the brain, so far mostly with the help of electrodes worn on the head. In addition to developing and testing the system, the project is also working on integrating it into the care process. The perspectives of technology, usability, process and data security of the embedding in clinical practice and health economics are taken into account.
Fraunhofer ISST is responsible for the integration of the AI-based sensor system into the care process. A digitally supported care concept is being developed with experts in health economics, taking into account normative requirements such as the Digital Care Act (DVG). Close collaboration with all future user groups is taking place to derive the requirements and design of the digital health applications. In addition, evaluation results of the health applications developed in the EPItect project (mobile application for epilepsy self-management as well as a portal) are included. In terms of future viability and integrability into the national telematics infrastructure as well as existing IT systems, standards such as Health Level 7 (HL7), Fast Healthcare Interoperability Resources (FHIR) and Integrating the Healthcare Enterprise (IHE) as well as legal framework conditions are taken into account. Another focus is on the further development of multimodal models for the detection of seizures.
The aim of the project is to combine mobile sensor technology to record vital data for the detection and documentation of epileptic seizures in order to improve diagnosis and the adjustment of drug therapy. Furthermore, digital health applications are to promote networking and therapy adherence. Important partial results are:
- Technologies for automated seizure detection and documentation
- Standardized, privacy-compliant and networked applications for patients, relatives and medical institutions
- New digitally supported treatment concepts
- Analysis of integration into reimbursement structures
- Concept for migration to the telematics infrastructure
- Testing within the framework of user studies
- Carl von Ossietzky Universität Oldenburg
- Cosinuss GmbH
- Fraunhofer-Institut für Digitale Medientechnologie IDMT
- HörTech gGmbH
- Institut für Medizinmanagement und Gesundheitswissenschaften, Universität Bayreuth
- Klinik und Poliklinik für Epileptologie, Universitätsklinikum Bonn
- Philipps-Universität Marburg, Universitätsklinikum Marburg
- Funding body: Federal Ministry of Health BMG as part of the funding priority "Digital innovations for improving patient-centered care in healthcare, smart sensor technology"
- Funding code: 2520DAT01A
- Collaborative number: G512F11007
- Duration: 04/2020-09/2022