Clinical Scenarios for Long-term Monitoring of Epileptic Seizures With a Wearable Biopotential Technology

Epilepsy Clinical Trial

— SeizeIT2  

Official title:

A Multicenter Study to Examine Clinical Scenarios for Long-term Monitoring of Epileptic Seizures With a Wearable Biopotential Technology

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04284072
• Other study ID #: S63631
• Status: Completed
• Phase: N/A
• Start date: June 22, 2020
• Est. completion date: June 30, 2022

Study information

• Verified date: May 2022
• Source: Universitaire Ziekenhuizen KU Leuven
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 496
• Est. completion date: June 30, 2022
• Est. primary completion date: June 30, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 4 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Epilepsy
• Seizures

Intervention

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).

Locations

Country	Name	City	State
Belgium	University Hospitals Leuven, department of Neurology	Leuven
Germany	Department of Epileptology and Neurology	Aachen
Germany	Epilepsy Center, University Medical Center, Freiburg University	Freiburg
Portugal	Division of Neurology, Coimbra University Hospital	Coimbra
Sweden	Department of Clinical Neuroscience, Karolinska Institute	Stockholm
United Kingdom	Division of Neuroscience, King's College London	London
United Kingdom	Nuffield Department of Clinical Neurosciences, Oxford University Hospital	Oxford

Sponsors (10)

Lead Sponsor	Collaborator
Universitaire Ziekenhuizen KU Leuven	Byteflies, Freiburg University, Helpilepsy, Karolinska Institutet, King's College London, Oxford University Hospital, RWTH Aachen University, UCB Pharma, University of Coimbra

Countries where clinical trial is conducted

Belgium,  Germany,  Portugal,  Sweden,  United Kingdom, 

References & Publications (16)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Hoppe C, Poepel A, Elger CE. Epilepsy: accuracy of patient seizure counts. Arch Neurol. 2007 Nov;64(11):1595-9. — View Citation

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. — View Citation

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. — View Citation

Kwan P, Brodie MJ. Early identification of refractory epilepsy. N Engl J Med. 2000 Feb 3;342(5):314-9. — View Citation

Sander JW. The epidemiology of epilepsy revisited. Curr Opin Neurol. 2003 Apr;16(2):165-70. Review. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

* Note: There are 16 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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
Primary	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
Primary	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
Primary	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
Primary	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	up to two weeks
Secondary	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
Secondary	To assess seizure duration	From the Sensor Dot data, we will be able to assess seizure duration	up to two weeks
Secondary	To assess the usability of the seizure e-diary	We will asses usability of the electronic seizure diary	up to two weeks
Secondary	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
Secondary	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
Secondary	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

External Links

•   EIT Health website referring to our SeizeIT2 project