Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 43 | EpiCap - a wearable seizure monitoring device |
Ian Xu Yichen Wu Yuanrui Chen |
Jamie Xu | design_document1.pdf design_document6.pdf final_paper1.pdf final_paper2.pdf other1.HEIC other2.HEIC other3.HEIC photo1.HEIC presentation1.pptx proposal1.pdf |
|
| Team Members: - Yichen Wu(yichenw5) - Yiyang Xu(yiyangx6) - Yuanrui Chen(yuanrui3) # Problem Epilepsy accounts for 12% of all neurological issues in the United States. Currently, seizure monitoring is mostly conducted in inpatient setting, which brings inconvenience to the patients at different levels. Appointments are hard to get, work schedule is disturbed and the inpatient experience is often unpleasant. There are outpatient devices, but they are bulky and ugly. Thus, we are in need of an EEG-based seizure monitor device that enables faster diagnosing, allows patients to continue daily activities and supports video recording. # Solution The EpiCap is proposed: a hat with an embedded EEG system. The EpiCap can collect EEG signals, detect eye movement, and monitor seizure activities. An onboard bluetooth/wifi chip can be used to send information for physician interpretation via web API. Physicians can also interpret data from an on-board memory device. The cap will include a miniature power system with minimal noise. ADS1299 will be used for this cap. No development board will be used due to size constraints. # Significance Currently, no outpatient epilepsy monitoring device exists that includes a camera module. From our sponsor’s interactions with the lead EEG technician at Carle, capturing the patient’s arm and leg movements, as well as eye movements are important to correlate with the captured brain waves to determine the type of seizure. For example, if the patient is not moving, but there are some abnormal brain activity, that can be suspecting of an absence seizure, versus for a grand mal seizure, you would see some arm flailing movements. For patients with psychogenic non-epileptic seizures, that is seizures that is psychologic in nature and not an abnormality of the brain, the patient’s eyes would normally be closed, compared with seizures where their eyes may be opened, fluttering, or deviating. Also, no commercially available EEG cap on the market is in the form of a baseball cap that has a vision of being customizable. From our sponsor’s interactions with patients, they share that they would want a EEG device that blends into the surrounding. Current outpatient (i.e. carry outside the hospital) devices look horrendous, and kids can get made fun of. # Solution Components ## Power Subsystem We choose Lithium Rechargeable batteries to power up our system. Battery chemistry such as Lithium-Ion has a longer cycle life and shelf life compared to other batteries. Additionally, we will need linear voltage regulators to adjust the voltage to a desired level and lower the noise impact. ## Logic Subsystem The microcontroller for this project needs to be able to process the data from the sensor and decide whether the patient is experiencing seizure. To be specific, it needs to compare the current data to a reference and send an alarm to the physician when it detects the seizure. To constantly receive and calculate the EEG data, we must ensure the microcontroller we pick has enough RAM. Furthermore, it needs to be able to interact with the camera module. The EEG data and video will be then uploaded to the cloud using Javascript for further evaluation. ## Sensor Subsystem The sensor is one of the most important parts of this project because it needs to measure the EEG data. In order to monitor the patient and record the data for doctors, we need a chip that has enough channels, resolution, and a desired sampling rate. ## Storage Subsystem The difficulty of choosing components for the storage system is that the MicroSD card is far too small for the large data size collected by the system, while larger storage devices like USB may not fit the cap. We need a storage chip that can store the data we produced and also another as backup. # Criterion for Success The final deliverable should be able to capture EEG data and video recording. The data can be sent to computer/phone/web. And we are able to view data on compute/phone/web. The battery should be able sustain for an entire day. The signal integrity of the EEG data should be verified. Apart from these requirements, we need to ensure dry electrodes stay on head. Also the hat should be relatively comfortable to wear. |
|||||