TY - JOUR
T1 - The power of ECG in multimodal patient-specific seizure monitoring
T2 - Added value to an EEG-based detector using limited channels
AU - Vandecasteele, Kaat
AU - De Cooman, Thomas
AU - Chatzichristos, Christos
AU - Cleeren, Evy
AU - Swinnen, Lauren
AU - Ortiz, Jaiver Macea
AU - Van Huffel, Sabine
AU - Dümpelmann, Matthias
AU - Schulze- Bonhage, Andreas
AU - De Vos, Maarten
AU - Van Paesschen , Wim
AU - Hunyadi, Borbála
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - behind-the-ear EEG
KW - ECG
KW - epilepsy
KW - multimodal algorithms
KW - reduced electrode montage
KW - seizure detection
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85109411971&partnerID=8YFLogxK
U2 - 10.1111/epi.16990
DO - 10.1111/epi.16990
M3 - Article
AN - SCOPUS:85109411971
SN - 0013-9580
VL - 62
SP - 2333
EP - 2343
JO - Epilepsia
JF - Epilepsia
IS - 10
ER -