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| Name | Class |
|---|---|
| Hamilton Health Sciences Corporation | OTHER |
| Toronto Metropolitan University | OTHER |
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This study aims to collect the motor movement data using sensors to detect Generalized Tonic Clonic Seizures . Wearable sensors similar to smart watches will be used to detect seizures. The proposed system will consist of 3-4 wearable wireless sensor worn on the hands and legs. The data from these sensors will be send to the clod and collected to a central hub for analysis and detection of GTC Seizures.
The key of the project is digital signal processing of the movement data collected. The medical application sensors from Analog Devices will be worn around the wrist and legs of the participants. These sensors will continuously collect the motor movement data. The data will be send to the cloud from where it will be collected at a central hub for digital signal processing and detection. The data analysis results will be compared with the standard EEG results for Tonic Clonic seizures and results will be verified.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Measurement of Motor Movements | Experimental | Wearable Accelerometer Sensors manufactured by leading manufacturers will be given to the participants to be worn around hands and legs. These sensors will be used to measure accelerations which will be impacted by the motor movements. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Wearable sensors | Device | Wearable sensors will be given to the participants to be worn and movement data will be collected |
|
| Measure | Description | Time Frame |
|---|---|---|
| Measurement of Acceleration | Motor movements will cause changes in acceleration. Accelerometer sensors will be used to measure the acceleration as a first step towards detection of Tonic Clonic Seizures | 3 months |
| Measure | Description | Time Frame |
|---|---|---|
| Digital Signal processing of the data | The raw acceleration data collected will be processed in a computer with MATLAB software using Digital Signal processing techniques. These techniques will help identify the data and differentiate the seizure movements from normal movements thereby helping to detect seizures data | 3 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Mini Thomas, M.Eng. | Mohawk College | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Mini Thomas | Hamilton | Ontario | L9C 0E5 | Canada |
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| Label | URL |
|---|---|
| Non-EEG based ambulatory seizure detection designed for home use: What is available and how will it influence epilepsy care? | View source |
| Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings | View source |
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| ID | Term |
|---|---|
| D012640 | Seizures |
| ID | Term |
|---|---|
| D009461 | Neurologic Manifestations |
| D009422 | Nervous System Diseases |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| Safe and sound? A systematic literature review of seizure detection methods for personal useSafe and sound? A systematic literature review of seizure detection methods for personal use | View source |