As an Associate Professor, my research primarily focuses on the application of deep learning for the classification of neurophysiological disorders based on EMG, as well as seizure detection algorithms and wearable technologies for monitoring neurological conditions. I hold a PhD in Health Sciences. My publications mainly cover research on seizure detection using heart rate variability and the development of automated algorithms in clinical neurophysiology.

I investigate the use of deep learning for classifying neuromuscular diseases based on EMG data. Through the development of advanced algorithms, my goal is to enhance diagnostic accuracy and efficiency in clinical practice. My research has also focused on the use of heart rate variability for seizure detection in epilepsy patients, contributing to the development of a wearable real-time seizure detection device with an associated smartphone app that alerts users during seizures.

I serve as a project supervisor for master's students in biomedical engineering at Aarhus University, where their thesis projects are derived from the clinical research projects I am involved in. Through project-based learning and real-world research challenges, my goal is to help students gain a deep understanding of both the theoretical and practical aspects of neurophysiological diagnostics.

I collaborate with national and international research institutions as well as hospitals on projects related to the application of deep learning in neurophysiological diagnostics. The goal is to develop innovative solutions that can be integrated into clinical practice to improve patient care. Previous collaborations have included the development of wearable technologies for epilepsy monitoring in partnership with technology companies and clinical departments.

Based on my research in deep learning and neurophysiological diagnostics, I advise colleagues on implementing seizure detection using heart rate variability, signal processing, and algorithms in clinical practice. I also participate in international collaborations, including the publication of guidelines for the standardization and recommendations of wearable seizure detection for epilepsy patients. My previous experience includes advising on the use of wearables for epilepsy monitoring.

My current primary responsibility is research in neurophysiological diagnostics, including the development of deep learning algorithms for EMG-based classification of neuromuscular diseases. I am also involved in the development of wearable technologies for epilepsy monitoring and have experience with signal processing and machine learning in clinical neurophysiology.