SeizeIT2: Clinical Scenarios for Long-term Monitoring of Epileptic Seizures With a Wearable Biopotential Technology
Study Details
Study Description
Brief Summary
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.
Condition or Disease | Intervention/Treatment | Phase |
---|---|---|
|
N/A |
Detailed Description
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.
Study Design
Arms and Interventions
Arm | Intervention/Treatment |
---|---|
Experimental: All subjects 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. |
Device: Sensor Dot
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).
|
Outcome Measures
Primary Outcome Measures
- 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 [up to two weeks]
F1-score as determined by expert reviewers
- 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 [up to two weeks]
F1-score as determined by expert reviewers
- 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 [up to two weeks]
F1-score as determined by expert reviewers
- 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 [up to two weeks]
F1-score as determined by expert reviewers
- 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 [up to two weeks]
F1-score as determined by expert reviewers
- 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 [up to two weeks]
F1-score as determined by expert reviewers
Secondary Outcome Measures
- Sensor Dot usability [up to two weeks]
We will assess the usability of the device as perceived by users (patients and healthcare personnel) via surveys
- To assess seizure duration [up to two weeks]
From the Sensor Dot data, we will be able to assess seizure duration
- To assess the usability of the seizure e-diary [up to two weeks]
We will asses usability of the electronic seizure diary
- To evaluate the accuracy of automated seizure detection algorithms [2 years]
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.
- Comparison of seizure annotations derived from Sensor Dot data collected during the Home Phase against seizure diary annotations [up to 2 weeks]
Accuracy as determined by expert reviewers
- Sensor Dot Performance [up to 2 weeks]
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.
Eligibility Criteria
Criteria
Inclusion Criteria:
-
Subjects (4+ years old) with refractory epilepsy who are admitted to the hospital for clinically-indicated long-term video-EEG assessment or presurgical evaluation, and a high likelihood of experiencing seizures during the EMU Phase
-
For subjects continuing into the Home Phase: successful recording of their habitual seizures with Sensor Dot during the EMU Phase
-
For subjects continuing into the Home Phase: the ability to keep an e-diary
Exclusion Criteria:
-
Known allergies to any of the biopotential electrodes or adhesives used as part of the study protocol
-
Having an implanted device, such as (but not limited to) a pacemaker, cardioverter defibrillator (ICD), and/or neural stimulation device because Sensor Dot contains magnets that could interfere with the operation of these devices
-
Women who are pregnant
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
---|---|---|---|---|---|
1 | University Hospitals Leuven, department of Neurology | Leuven | Belgium | 3000 | |
2 | Department of Epileptology and Neurology | Aachen | Germany | ||
3 | Epilepsy Center, University Medical Center, Freiburg University | Freiburg | Germany | ||
4 | Department of Clinical Neuroscience, Karolinska Institute | Stockholm | Sweden |
Sponsors and Collaborators
- Universitaire Ziekenhuizen Leuven
- Freiburg University
- King's College London
- Oxford University Hospital
- University of Coimbra
- Karolinska Institutet
- RWTH Aachen University
- UCB Pharma
- Byteflies
- Helpilepsy
Investigators
- Principal Investigator: Wim Van Paesschen, MD, PhD, UZ Leuven and KU Leuven
Study Documents (Full-Text)
None provided.More Information
Additional Information:
Publications
- 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. Review.
- 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.
- 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.
- 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. Review.
- 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.
- 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. Review.
- 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, Moshé 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. Review.
- 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). pii: E29. doi: 10.3390/s18010029.
- Hoppe C, Poepel A, Elger CE. Epilepsy: accuracy of patient seizure counts. Arch Neurol. 2007 Nov;64(11):1595-9.
- 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.
- 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.
- Kwan P, Brodie MJ. Early identification of refractory epilepsy. N Engl J Med. 2000 Feb 3;342(5):314-9.
- Sander JW. The epidemiology of epilepsy revisited. Curr Opin Neurol. 2003 Apr;16(2):165-70. Review.
- 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. Review.
- Szabó CÁ, 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.
- 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.
- S63631