Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment
Open accessAbstract
Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.
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One study reports that more than half of epileptic seizures in a data set could not be detected from Empatica E4 PPG data due to motion artifacts.
“One study, for example, reports that more than half of epileptic seizures in a data set could not be detected from Empatica E4 PPG data due to motion artifacts68.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [68]
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- Authors
- Vandecasteele, K., De Cooman, T., Gu, Y., Cleeren, E., Claes, K., Paesschen, W., Huffel, S., & Hunyadi, B.
- Affiliations
- IMEC (BE)
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven 3001, Belgium
- KU Leuven (BE)
- UCB Pharma (Belgium) (BE)
- Corresponding author
- Vandecasteele, Kaat
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-10-13
- Volume
- 17
- Issue
- 10
- Pages
- 2338
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5676949
- OpenAlex
- W2760882705
- Cited by
- 174
- References
- 25
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 22 · 2023: 15 · 2022: 26 · 2021: 35 · 2020: 23 · 2019: 21 · 2018: 15
- FWCI
- 7.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agentschap Innoveren en Ondernemen (150466:, O&O HBC 2016 0184 eWatch)
- European Commission (2007–2013, 339804, FP7 2007-2013, FP7/2007)
- iMinds (HBC.2016.0167)
- KU Leuven
- Innoviris
- Vlaamse regering
- UCB Pharma
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Keywords
- seizure detection, temporal lobe epilepsy, wearable ECG, photoplethysmography, wrist-worn sensors
- MeSH terms
- Wearable Electronic Devices, Algorithms, Electrocardiography, Electroencephalography, Epilepsy, Heart Rate, Hospitals, Humans, Seizures, Photoplethysmography
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