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The Seizure Detection Device [SURE Stories]

From the right: Pavan Jagannathan (BME ’14), Saumita Rajeevan (BME ’14), & Yash Jain (BME ’14)

From the right: Pavan Jagannathan (BME ’14), Saumita Rajeevan (BME ’14), & Yash Jain (BME ’14)

The following post is written by UHP student and SURE Award winner Pavan Jagannathan.

The money from the SURE Award will be used to help build my capstone design project.  What my group and I are attempting to build is a device that can identify seizures before they happen.  This “Seizure Detection Device” is a collection of systems responsible for interpreting a tonic-clonic EEG seizure signal, reporting it to the user, and calling first responders at the discretion of the user.  The system utilizes an electrode “helmet” that transmits the EEG signals via Bluetooth®™ to an Android™ smartphone.  The application on the Android™ analyzes, compares the signals to tonic -clonic seizure signals, and relays the appropriate response to the user on the graphical interface of the application.

If the device detects a seizure, the application starts a 30 second countdown timer displayed by the application.  When the timer hits zero, it will call first responders and pass on the user’s location. However, the user can push the “Override” button to stop the call in case of an error in the detection system or if the user is already at the hospital or with adequate support.  The device will also warn the user about an impending seizure and allowing him/her to escape an otherwise dangerous situation (driving, machine work, etc.). This device will optimize the quality of living for a patient with chronic seizures and revolutionize the diagnosis and response technology.

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