Funding: The project is funded by the Excellence Initiative of the German federal and state governments
Project ID: G:(DE-82)ZUK2-SF-OPSF424
Duration: January 2018 – December 2018
Project Lead: Ekaterina Kutafina, mHealth, Department of Medical Informatics, Uniklinik RWTH Aachen
Project Partners: Prof. Dr. Maria Kateri, Lehrstuhl für Statistik und Stochastische Modellierung
Univ.-Prof. Dr. med. Dr. rer. nat. Klaus Mathiak, Klinik für Psychiatrie, Psychotherapie und Psychosomatik
Schizophrenia is the mental disorder that creates widespread suffering and high economic costs. Antipsychotic treatment is only partially successful in improving the symptoms of schizophrenia. Clozapine is a second generation antipsychotic drug, which is shown to be more effective than typical antipsychotics in treatment-resistant patients, but it is associated with severe side effects such as an elevated risk of epileptic seizures. In recent decades, one research focus is the link between clozapine treatment, EEG abnormalities and observed seizures. However, no direct link between clozapine dosage, EEG findings, and seizure risk could be established. EEG abnormalities are even reported be an indicator of better clinical response to the treatment. Due to the importance to improve the quality of life in schizophrenia, there is a need to study EEG in clozapine patients to acquire a better understanding on the dependencies between the dosage, EEG abnormalities and seizures. The common stationary EEG provides high data quality but generates high costs and offers limited access to brain recordings across circadian rhythms or other environmental factors. The development of mobile technologies allows exploring the possibility of long-term recording.
We propose a pilot study to investigate the possibilities of mobile EEG recordings in psychiatry. Despite the common notion that the quality of Emotiv Epoc mobile EEG is suitable for research purposes, its usability, acceptance, and validity needs to be shown for this application before systematic clinical trials can take place. This project is aiming at a) developing a user friendly platform for long term data acquisition, b) measuring user experience and acceptance, c) optimizing the study design and proposing a statistical data analysis procedure for detection of EEG abnormalities, and d) providing the scientific ground for follow-up research on EEG abnormalities, which will enable future multidisciplinary project proposals.