Epilepsy, Epileptic, Epileptic Seizures, Seizure Disorder, Seizures, Seizures, Motor
Conditions
Keywords
Epilepsy, Mobile Health, Seizure, Major motor seizure, Epileptic convulsion, Epilepsy monitoring
Brief summary
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
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.
Interventions
DEVICEMotor 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.
Sponsors
Bracane Company
Overwatch Digital Health
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Intervention model description
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).
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | 1 to 5 days | Number of major motor seizure detections by algorithm with detection by video encephalogram data. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| False positive rate | 1 to 5 days | Total number of false positives and number of false positives per day. |
| Mean detection latency | 1 to 5 days | Time between algorithm detection and application notification |
| Notifications | 1 to 5 days | Total number of seizure notifications received on subject's assigned email |
| Cancellations | 1 to 5 days | Total number of cancellations of false positive alerts made by the subject. |
Countries
United States
Contacts
Primary ContactChis Czura, PhD
Backup ContactPamela J Nelson, PhD
Outcome results
None listed