Enabling Large-Scale Probabilistic Seizure Detection with a Tensor-Network Kalman Filter for LS-SVM

Recent advancements in wearable EEG devices have highlighted the importance of accurate seizure detection algorithms, yet the ever-increasing size of the generated datasets poses a significant challenge to existing seizure detection methods based on kernel machines. Typically, this problem is mitigated by significantly undersampling the...

The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels

Objective: Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self-reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind-the-ear electroencephalography (EEG). However, the obtained...

Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

Objective: Automated seizure detection is a key aspect of wearable seizure warning systems. As a result, the quality of life of refractory epilepsy patients could be improved. Most state-of-the-art algorithms for heart rate-based seizure detection use a so-called patient-independent approach, which do not take into account patient-specific...

Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels

Objective: Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a...