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Bioengineering
Volume 10
Issue 4
10.3390/bioengineering10040491
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Article

The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG

by
Miguel Bhagubai
1,*,
Kaat Vandecasteele
1,
Lauren Swinnen
2,
Jaiver Macea
2,
Christos Chatzichristos
1,
Maarten De Vos
1,3 and
Wim Van Paesschen
2
1
Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
2
Laboratory for Epilepsy Research, University Hospital Leuven, 3000 Leuven, Belgium
3
Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Bioengineering 2023, 10(4), 491; https://doi.org/10.3390/bioengineering10040491
Submission received: 27 February 2023 / Revised: 7 April 2023 / Accepted: 18 April 2023 / Published: 20 April 2023
(This article belongs to the Special Issue Advances in Smart Sensing and Data Computing for Sleep Analysis, Sleep Disorders Detection and Epileptic Seizure Detection and Prediction)
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Abstract

Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm’s detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.
Keywords: epilepsy; seizure detection; multimodal; behind-the-ear EEG; ECG; ictal heart rate epilepsy; seizure detection; multimodal; behind-the-ear EEG; ECG; ictal heart rate
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MDPI and ACS Style

Bhagubai, M.; Vandecasteele, K.; Swinnen, L.; Macea, J.; Chatzichristos, C.; De Vos, M.; Van Paesschen, W. The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG. Bioengineering 2023, 10, 491. https://doi.org/10.3390/bioengineering10040491

AMA Style

Bhagubai M, Vandecasteele K, Swinnen L, Macea J, Chatzichristos C, De Vos M, Van Paesschen W. The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG. Bioengineering. 2023; 10(4):491. https://doi.org/10.3390/bioengineering10040491

Chicago/Turabian Style

Bhagubai, Miguel, Kaat Vandecasteele, Lauren Swinnen, Jaiver Macea, Christos Chatzichristos, Maarten De Vos, and Wim Van Paesschen. 2023. "The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG" Bioengineering 10, no. 4: 491. https://doi.org/10.3390/bioengineering10040491

APA Style

Bhagubai, M., Vandecasteele, K., Swinnen, L., Macea, J., Chatzichristos, C., De Vos, M., & Van Paesschen, W. (2023). The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG. Bioengineering, 10(4), 491. https://doi.org/10.3390/bioengineering10040491

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