Study to Validate Novel Seizure-Detection Algorithm

NCT ID: NCT04291716

Last Updated: 2020-03-02

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

15 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-03-31

Study Completion Date

2020-12-31

Brief Summary

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The specificity and sensitivity of a novel seizure-detection mobile software application with a generalized tonic/clonic seizure detection algorithm (Motor Seizure Detection Algorithm \[mSDA\]) installed on a wearable device to be worn by the subject. The software will be tested using subjects from a patient population in an epilepsy monitoring unit (EMU) undergoing video and electroencephalograph (VEEG) observation. The number of generalized major motor seizures detected by the mSDA will be compared with those detected by VEEG.

Detailed Description

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Seizures are paroxysmal, abnormal behaviors which usually are associated with altered awareness and amnesia. The frequency of seizures is not easily documented. The individual who suffers from seizures may be unaware that a seizure is occurring. Many seizures, including generalized major motor seizures, have stereotyped, vigorous motor activity associated with the events.

Currently, accurate seizure detection relies on EEG and video which are limited by time, size and mobility. Seizure detection can also use biomarkers such as movement patterns described by gyroscopes. These devices can monitor patterns of movement which correspond to the activity during seizures and kept in a log of seizures without patient input. The log can be used to notify patients or caregivers of seizures.

This study is to determine the accuracy of a system using a commercial, wearable device linked to a computer algorithm based in the cloud which stores the movement pattern and notifies the patient and others of a generalized major motor seizure. The accuracy will be determined by a comparison of the system detections to simultaneously recorded video electroencephalogram, considered the "gold standard" of seizure detection.

Conditions

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Seizures, Motor Seizures Seizure Disorder Epilepsy Epileptic Seizures Epileptic

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

This is a single cohort of subjects male or female, aged 18 and above who are epilepsy patients who have been admitted to an epilepsy monitoring unit (EMU).
Primary Study Purpose

OTHER

Blinding Strategy

NONE

Study Groups

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Single Arm

This is a single-arm study. All subjects enrolled in the study will wear the device during stay in the EMU.

Group Type OTHER

Motor Seizure Detection Algorithm (mSDA)

Intervention Type DEVICE

A seizure detection algorithm installed on a propriety mobile application to be used on a commercially available watch with a gyroscope to detect movement.

Interventions

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Motor Seizure Detection Algorithm (mSDA)

A seizure detection algorithm installed on a propriety mobile application to be used on a commercially available watch with a gyroscope to detect movement.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

1. Provision of signed and dated informed consent form.
2. Stated willingness to comply with all study procedures and availability for the duration of the study.
3. Meets the standard of care criteria for admission to an epilepsy monitoring unit (EMU).
4. Male or female.
5. Aged 18 and above.
6. The patient has experienced at least one generalized major motor seizure prior to admission.
7. Agreement to wear a wristwatch throughout the duration of the study on the left wrist.
8. Ability to cancel false positive alarms via interaction with the application on the watch.

Exclusion Criteria

1. Concurrent physiological diseases with movement disorders (Parkinson's, tremor, ataxia, Huntington's, paralysis of the upper body, pseudo-seizures).
2. Known allergic reactions to components of the (watch materials).
3. Treatment with another investigational drug or other intervention within the study
4. Children under the age of 18.
5. Women who are pregnant or nursing.
6. Inability to give consent to the study.
7. Active skin infection or rash on the upper extremities
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Bracane Company

INDUSTRY

Sponsor Role collaborator

Overwatch Digital Health

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Haytham Elgammal, MD

Role: PRINCIPAL_INVESTIGATOR

Overwatch Digital Health

Subha Sarcar, PhD

Role: STUDY_DIRECTOR

Bracane Company

Locations

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Covenant Hospital and Covenant Medical Group

Lubbock, Texas, United States

Site Status

Countries

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United States

Central Contacts

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Chis Czura, PhD

Role: CONTACT

214-662-7322

Pamela J Nelson, PhD

Role: CONTACT

469.814.0658

References

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Fisher RS, Cross JH, French JA, Higurashi N, Hirsch E, Jansen FE, Lagae L, Moshe SL, Peltola J, Roulet Perez E, Scheffer IE, Zuberi SM. Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017 Apr;58(4):522-530. doi: 10.1111/epi.13670. Epub 2017 Mar 8.

Reference Type BACKGROUND
PMID: 28276060 (View on PubMed)

Proposal for revised clinical and electroencephalographic classification of epileptic seizures. From the Commission on Classification and Terminology of the International League Against Epilepsy. Epilepsia. 1981 Aug;22(4):489-501. doi: 10.1111/j.1528-1157.1981.tb06159.x. No abstract available.

Reference Type BACKGROUND
PMID: 6790275 (View on PubMed)

Kramer U, Kipervasser S, Shlitner A, Kuzniecky R. A novel portable seizure detection alarm system: preliminary results. J Clin Neurophysiol. 2011 Feb;28(1):36-8. doi: 10.1097/WNP.0b013e3182051320.

Reference Type BACKGROUND
PMID: 21221012 (View on PubMed)

Janse SA, Dumanis SB, Huwig T, Hyman S, Fureman BE, Bridges JFP. Patient and caregiver preferences for the potential benefits and risks of a seizure forecasting device: A best-worst scaling. Epilepsy Behav. 2019 Jul;96:183-191. doi: 10.1016/j.yebeh.2019.04.018. Epub 2019 May 29.

Reference Type BACKGROUND
PMID: 31150998 (View on PubMed)

Jalloul N. Wearable sensors for the monitoring of movement disorders. Biomed J. 2018 Aug;41(4):249-253. doi: 10.1016/j.bj.2018.06.003. Epub 2018 Sep 11.

Reference Type BACKGROUND
PMID: 30348268 (View on PubMed)

Muennig PA, Glied SA. What changes in survival rates tell us about us health care. Health Aff (Millwood). 2010 Nov;29(11):2105-13. doi: 10.1377/hlthaff.2010.0073. Epub 2010 Oct 7.

Reference Type BACKGROUND
PMID: 20930036 (View on PubMed)

Johansson D, Malmgren K, Alt Murphy M. Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review. J Neurol. 2018 Aug;265(8):1740-1752. doi: 10.1007/s00415-018-8786-y. Epub 2018 Feb 9.

Reference Type BACKGROUND
PMID: 29427026 (View on PubMed)

Nijsen TM, Arends JB, Griep PA, Cluitmans PJ. The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy. Epilepsy Behav. 2005 Aug;7(1):74-84. doi: 10.1016/j.yebeh.2005.04.011.

Reference Type BACKGROUND
PMID: 15975855 (View on PubMed)

Horne MK, McGregor S, Bergquist F. An objective fluctuation score for Parkinson's disease. PLoS One. 2015 Apr 30;10(4):e0124522. doi: 10.1371/journal.pone.0124522. eCollection 2015.

Reference Type BACKGROUND
PMID: 25928634 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 23398578 (View on PubMed)

Other Identifiers

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OW012019

Identifier Type: -

Identifier Source: org_study_id

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