Epilepsy is a regulatory disorder of the brain that, if left untreated, can manifest itself in adults, for example, through seizures or unconsciousness. Premature infants, newborns, and young children also suffer from epilepsy, but it is difficult to detect because of the lack of seizures and is often overlooked. It is thought that up to 20% of all so-called sudden infant deaths are related to an undiagnosed epileptic condition.

The state of the art is to diagnose epilepsy by analyzing electric brain signal measurements (electroencephalography, EEG). This can only take place at specialized epilepsy centers and for short periods of time, and severely restricts individuals during the measurements. Furthermore, it is only possible to study under these artificial conditions and for short periods of time. In the BrainEpP project, which is funded by the German Federal Ministry of Education and Research (BMBF), the Institute of Radiofrequency Engineering (IHF) at the Hamburg University of Technology (TUHH) and its project partners are investigating a method that enables non-contact and continuous monitoring of cardiovascular functions in young children and young adults and even in premature babies. Research is being conducted to determine whether the regulation of the autonomic nervous system can be inferred from the measured parameters by extracting the ratios of activation of the sympathetic and parasympathetic nervous systems from the cardiac signals. From this, not only could epileptic seizures be estimated without an EEG, but perhaps regulatory disturbances beginning before a seizure could be detected. In the event of such an alarm, the seizure could possibly be suppressed with medication, increasing the quality of life for many affected individuals and reducing the risk of dying during a seizure.

The monitoring itself is also the subject of research and is done without contact from a shorter distance and through clothing or, for example, bed covers. A so-called high-frequency interferometer is used as a research demonstrator, which transmits electromagnetic waves with minimal power that are reflected by the body surface and received by the sensor. From this, the smallest vibrations of the body surface of only a few micrometers deflection can be recorded, such as those caused by heartbeat and breathing. This distance measurement data can be continuously collected and automatically segmented and classified using machine learning. To investigate the research hypothesis in the case of BrainEpP, this involves looking for markers of an impending epileptic seizure.

Project partners are the University Hospital Erlangen and the companies Geratherm Respiratory, Silicon Radar, DeMeTec and Voigtmann.