Print Email Facebook Twitter The power of ECG in multimodal patient-specific seizure monitoring Title The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels Author Vandecasteele, Kaat (Katholieke Universiteit Leuven) De Cooman, Thomas (Katholieke Universiteit Leuven) Chatzichristos, Christos (Katholieke Universiteit Leuven) Cleeren, Evy (University Hospital Leuven) Swinnen, Lauren (University Hospital Leuven) Ortiz, Jaiver Macea (University Hospital Leuven) Van Huffel, Sabine (Katholieke Universiteit Leuven) Dümpelmann, Matthias (University of Freiburg) Schulze- Bonhage, Andreas (University of Freiburg) De Vos, Maarten (Katholieke Universiteit Leuven) Van Paesschen, Wim (Katholieke Universiteit Leuven) Hunyadi, Borbala (TU Delft Signal Processing Systems) Date 2021 Abstract 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 sensitivities were too low for practical use, because not all seizures are associated with typical ictal EEG patterns. Therefore, in this paper, we aim to develop a multimodal automated seizure detection algorithm integrating behind-the-ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind-the-ear EEG. Methods: This study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896 seizures. A patient-specific multimodal automated seizure detection algorithm was developed using behind-the-ear/temporal EEG and single-lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions. Results: The multimodal algorithm outperformed the EEG-based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate. Significance: ECG can be of added value to an EEG-based seizure detection algorithm using only behind-the-ear/temporal lobe electrodes for patients with focal epilepsy. Subject behind-the-ear EEGECGepilepsymultimodal algorithmsreduced electrode montageseizure detectionwearable sensors To reference this document use: http://resolver.tudelft.nl/uuid:69d523e4-1bb8-463c-823c-2dbd62a13a7f DOI https://doi.org/10.1111/epi.16990 ISSN 0013-9580 Source Epilepsia, 62 (10), 2333-2343 Part of collection Institutional Repository Document type journal article Rights © 2021 Kaat Vandecasteele, Thomas De Cooman, Christos Chatzichristos, Evy Cleeren, Lauren Swinnen, Jaiver Macea Ortiz, Sabine Van Huffel, Matthias Dümpelmann, Andreas Schulze- Bonhage, Maarten De Vos, Wim Van Paesschen, Borbala Hunyadi Files PDF epi.16990.pdf 1.36 MB Close viewer /islandora/object/uuid:69d523e4-1bb8-463c-823c-2dbd62a13a7f/datastream/OBJ/view