{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T06:29:26Z","timestamp":1791872966003,"version":"4.3.4"},"reference-count":49,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T00:00:00Z","timestamp":1625788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self\u2010reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind\u2010the\u2010ear 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\u2010the\u2010ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind\u2010the\u2010ear EEG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896\u00a0seizures. A patient\u2010specific multimodal automated seizure detection algorithm was developed using behind\u2010the\u2010ear\/temporal EEG and single\u2010lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The multimodal algorithm outperformed the EEG\u2010based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>ECG can be of added value to an EEG\u2010based seizure detection algorithm using only behind\u2010the\u2010ear\/temporal lobe electrodes for patients with focal epilepsy.<\/jats:p>\n                  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