4 papers
Comparison of Dimension Reduction Methods for EEG Seizure Detection Using Autonomous AI-Driven Optimization
Annika Stiehl, Vishal Kagade, Nicolas Weeger +3
Automated epileptic seizure detection from multichannel electroencephalography (EEG) benefits from dimension reduction to obtain compact, discriminative representations. We compare…
Low-Density EEG for Seizure Detection: Evaluating CNN-RNN Architectures on a Behind-the-Ear Montage Setup
Annika Stiehl, Patrick Wingert, Nicolas Weeger +3
Epilepsy affects over 50 million individuals globally, underscoring the need for automated seizure detection systems that can alleviate clinicians workload and enhance the accuracy…
Towards Automated EEG-Based Epilepsy Detection Using Deep Convolutional Autoencoders
Annika Stiehl, Nicolas Weeger, Christian Uhl +3
Epilepsy is one of the most common neurological disorders. This disease requires reliable and efficient seizure detection methods. Electroencephalography (EEG) is the gold standard…
Dimension reduction methods, persistent homology and machine learning for EEG signal analysis of Interictal Epileptic Discharges
Annika Stiehl, Stefan Geißelsöder, Nicole Ille +3
Recognizing specific events in medical data requires trained personnel. To aid the classification, machine learning algorithms can be applied. In this context, medical records are…