1. The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels
- Author
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Matthias Dümpelmann, Evy Cleeren, Borbála Hunyadi, Jaiver Macea Ortiz, Lauren Swinnen, Wim Van Paesschen, Andreas Schulze-Bonhage, Thomas De Cooman, Christos Chatzichristos, Maarten De Vos, Sabine Van Huffel, and Kaat Vandecasteele
- Subjects
behind‐the‐ear EEG ,Computer science ,ACCURACY ,Clinical Neurology ,reduced electrode montage ,seizure detection ,Electroencephalography ,Temporal lobe ,Constant false alarm rate ,Electrocardiography ,Wearable Electronic Devices ,Epilepsy ,Seizures ,medicine ,Humans ,ALGORITHM ,multimodal algorithms ,EPILEPTIC SEIZURES ,Science & Technology ,HEART BEAT DETECTION ,medicine.diagnostic_test ,business.industry ,ECG ,wearable sensors ,Detector ,Pattern recognition ,medicine.disease ,Power (physics) ,Neurology ,Seizure detection ,RELIABILITY ,Full‐length Original Research ,epilepsy ,EPILEPSIAE ,Epilepsies, Partial ,Neurology (clinical) ,Artificial intelligence ,Neurosciences & Neurology ,business ,behind-the-ear EEG ,Life Sciences & Biomedicine ,Algorithms - 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. ispartof: EPILEPSIA vol:62 issue:10 pages:2333-2343 ispartof: location:United States status: published
- Published
- 2021