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| Name | Class |
|---|---|
| Freiburg University | UNKNOWN |
| King's College London | OTHER |
| Oxford University Hospitals NHS Trust | OTHER |
| University of Coimbra |
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Clinically validate a biopotential and motion recording wearable device (Byteflies Sensor Dot) for detection of epileptic seizures in the epilepsy monitoring unit (EMU) and at home.
Subjects with refractory epilepsy who are admitted to the Epilepsy Monitoring Unit (EMU) for clinically-indicated long-term video-EEG assessment will be simultaneously monitored with Sensor Dots to record electroencephalographic (EEG), electrocardiographic (ECG), electromyographic (EMG), and motion signals.
A subset of subjects will continue using Sensor Dot devices at home (Home Phase) after completing the EMU Phase.
The data recorded by Sensor Dots will be used to: 1) annotate epileptic seizures, which will be compared to the annotations made as part of routine EMU monitoring and seizure diaries kept at home, and 2) to develop seizure detection algorithms. The data collected as part of this study will not be used to influence clinical decision making.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| All subjects | Experimental | Single arm study with a device intervention for epileptic seizure monitoring in subjects with refractory focal impaired awareness, tonic-clonic, and/or typical absence seizures. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Sensor Dot | Device | Multimodal (EEG, ECG, EMG and motion) seizure monitoring with Sensor Dot to complement EMU-based video-EEG monitoring (EMU Phase), and optional home-based seizure diary logging (Home Phase). |
| Measure | Description | Time Frame |
|---|---|---|
| Comparison of typical absence seizure annotations derived from Sensor Dot data collected during the EMU Phase against annotations derived from video-EEG equipment during wakefulness | F1-score as determined by expert reviewers | up to two weeks |
| Comparison of typical absence seizure annotations derived from Sensor Dot data collected during the EMU Phase against annotations derived from video-EEG equipment during sleep | F1-score as determined by expert reviewers | up to two weeks |
| Comparison of focal impaired awareness seizure annotations derived from Sensor Dot data collected during the EMU Phase against annotations derived from video-EEG equipment during wakefulness | F1-score as determined by expert reviewers | up to two weeks |
| Comparison of focal impaired awareness seizure annotations derived from Sensor Dot data collected during the EMU Phase against annotations derived from video-EEG equipment during sleep | F1-score as determined by expert reviewers | up to two weeks |
| Comparison of tonic-clonic seizure annotations derived from Sensor Dot data collected during the EMU Phase against annotations derived from video-EEG equipment during wakefulness | F1-score as determined by expert reviewers | up to two weeks |
| Comparison of tonic-clonic seizure annotations derived from Sensor Dot data collected during the EMU Phase against annotations derived from video-EEG equipment during sleep | F1-score as determined by expert reviewers |
| Measure | Description | Time Frame |
|---|---|---|
| Sensor Dot usability | We will assess the usability of the device as perceived by users (patients and healthcare personnel) via surveys | up to two weeks |
| To assess seizure duration | From the Sensor Dot data, we will be able to assess seizure duration |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Wim Van Paesschen, MD, PhD | UZ Leuven and KU Leuven | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospitals Leuven, department of Neurology | Leuven | 3000 | Belgium | |||
| Department of Epileptology and Neurology |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 24730690 | Background | Fisher RS, Acevedo C, Arzimanoglou A, Bogacz A, Cross JH, Elger CE, Engel J Jr, Forsgren L, French JA, Glynn M, Hesdorffer DC, Lee BI, Mathern GW, Moshe SL, Perucca E, Scheffer IE, Tomson T, Watanabe M, Wiebe S. ILAE official report: a practical clinical definition of epilepsy. Epilepsia. 2014 Apr;55(4):475-82. doi: 10.1111/epi.12550. Epub 2014 Apr 14. | |
| 12644744 |
| Label | URL |
|---|---|
| EIT Health website referring to our SeizeIT2 project | View source |
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We plan to share the individual biosignals (EEG, EMG, ECG and movement) and 24-channel seizure-annotated EEG data, de-identified demographic and epilepsy-related data two years after the finish of the study (1-1-2024) upon request to researchers who provide a methodologically sound proposal.
Data will be shared from 1-1-2024. We do not foresee an end-date.
Data will be made available upon request to researchers who provide a methodologically sound proposal. Proposals should be directed to Wim.vanpaesschen@uzleuven.be
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| ID | Term |
|---|---|
| D004827 | Epilepsy |
| D012640 | Seizures |
| ID | Term |
|---|---|
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D009461 | Neurologic Manifestations |
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| OTHER |
| Karolinska Institutet | OTHER |
| RWTH Aachen University | OTHER |
| UCB Pharma | INDUSTRY |
| Byteflies | INDUSTRY |
| Helpilepsy | UNKNOWN |
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| up to two weeks |
| up to two weeks |
| To assess the usability of the seizure e-diary | We will asses usability of the electronic seizure diary | up to two weeks |
| To evaluate the accuracy of automated seizure detection algorithms | We will use the collected data and seizure annotations to develop algorithms to automatically detect epileptic seizures. We plan to evaluate how accurate these new automated seizure detection algorithms are. | 2 years |
| Comparison of seizure annotations derived from Sensor Dot data collected during the Home Phase against seizure diary annotations | Accuracy as determined by expert reviewers | up to 2 weeks |
| Sensor Dot Performance | We will assess the technical performance of the device by comparing the actual length of recorded data against the expected recording length, and what percentage of the data is high quality enough to make seizure annotations. | up to 2 weeks |
| Aachen |
| Germany |
| Epilepsy Center, University Medical Center, Freiburg University | Freiburg im Breisgau | Germany |
| Division of Neurology, Coimbra University Hospital | Coimbra | Portugal |
| Department of Clinical Neuroscience, Karolinska Institute | Stockholm | Sweden |
| Division of Neuroscience, King's College London | London | United Kingdom |
| Nuffield Department of Clinical Neurosciences, Oxford University Hospital | Oxford | United Kingdom |
| Sander JW. The epidemiology of epilepsy revisited. Curr Opin Neurol. 2003 Apr;16(2):165-70. doi: 10.1097/01.wco.0000063766.15877.8e. |
| 10660394 | Background | Kwan P, Brodie MJ. Early identification of refractory epilepsy. N Engl J Med. 2000 Feb 3;342(5):314-9. doi: 10.1056/NEJM200002033420503. |
| 29452687 | Background | Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 2018 Mar;17(3):279-288. doi: 10.1016/S1474-4422(18)30038-3. |
| 17998441 | Background | Hoppe C, Poepel A, Elger CE. Epilepsy: accuracy of patient seizure counts. Arch Neurol. 2007 Nov;64(11):1595-9. doi: 10.1001/archneur.64.11.1595. |
| 30802844 | Background | Kurada AV, Srinivasan T, Hammond S, Ulate-Campos A, Bidwell J. Seizure detection devices for use in antiseizure medication clinical trials: A systematic review. Seizure. 2019 Mar;66:61-69. doi: 10.1016/j.seizure.2019.02.007. Epub 2019 Feb 13. |
| 26552573 | Background | Bidwell J, Khuwatsamrit T, Askew B, Ehrenberg JA, Helmers S. Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies. Seizure. 2015 Nov;32:109-17. doi: 10.1016/j.seizure.2015.09.006. Epub 2015 Sep 18. |
| 29873827 | Background | Beniczky S, Ryvlin P. Standards for testing and clinical validation of seizure detection devices. Epilepsia. 2018 Jun;59 Suppl 1:9-13. doi: 10.1111/epi.14049. |
| 26190150 | Background | Szabo CA, Morgan LC, Karkar KM, Leary LD, Lie OV, Girouard M, Cavazos JE. Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings. Epilepsia. 2015 Sep;56(9):1432-7. doi: 10.1111/epi.13083. Epub 2015 Jul 20. |
| 29873829 | Background | Beniczky S, Conradsen I, Wolf P. Detection of convulsive seizures using surface electromyography. Epilepsia. 2018 Jun;59 Suppl 1:23-29. doi: 10.1111/epi.14048. |
| 23398578 | Background | Beniczky S, Polster T, Kjaer TW, Hjalgrim H. Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia. 2013 Apr;54(4):e58-61. doi: 10.1111/epi.12120. Epub 2013 Feb 8. |
| 29018634 | Background | Kjaer TW, Sorensen HBD, Groenborg S, Pedersen CR, Duun-Henriksen J. Detection of Paroxysms in Long-Term, Single-Channel EEG-Monitoring of Patients with Typical Absence Seizures. IEEE J Transl Eng Health Med. 2017 Jan 9;5:2000108. doi: 10.1109/JTEHM.2017.2649491. eCollection 2017. |
| 29096220 | Background | Zibrandtsen IC, Kidmose P, Christensen CB, Kjaer TW. Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring. Clin Neurophysiol. 2017 Dec;128(12):2454-2461. doi: 10.1016/j.clinph.2017.09.115. Epub 2017 Oct 12. |
| 29295522 | Background | Gu Y, Cleeren E, Dan J, Claes K, Van Paesschen W, Van Huffel S, Hunyadi B. Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy. Sensors (Basel). 2017 Dec 23;18(1):29. doi: 10.3390/s18010029. |
| Background | Dan J, Weckhuysen D, Cleeren E, Van Paesschen W, Vandendriessche B. Technical validation of Sensor Dot: a wearable for ambulatory monitoring of epileptic seizures. 2nd International Congress on mobile devices and seizure detection in epilepsy; Lausanne, Switzerland, 2019. |
| 28778476 | Background | Seeck M, Koessler L, Bast T, Leijten F, Michel C, Baumgartner C, He B, Beniczky S. The standardized EEG electrode array of the IFCN. Clin Neurophysiol. 2017 Oct;128(10):2070-2077. doi: 10.1016/j.clinph.2017.06.254. Epub 2017 Jul 17. |
| 40176726 | Derived | Macea J, Heremans ERM, Proost R, De Vos M, Van Paesschen W. Automated Sleep Staging in Epilepsy Using Deep Learning on Standard Electroencephalogram and Wearable Data. J Sleep Res. 2025 Oct;34(5):e70061. doi: 10.1111/jsr.70061. Epub 2025 Apr 3. |
| 38772401 | Derived | Zhang J, Swinnen L, Chatzichristos C, Broux V, Proost R, Jansen K, Mahler B, Zabler N, Epitashvilli N, Dumpelmann M, Schulze-Bonhage A, Schriewer E, Ermis U, Wolking S, Linke F, Weber Y, Symmonds M, Sen A, Biondi A, Richardson MP, I AS, Silva AI, Sales F, Vertes G, Paesschen WV, Vos M. Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection. Physiol Meas. 2024 Jun 7;45(6). doi: 10.1088/1361-6579/ad4e94. |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |