Epilepsy is one of the most common neurological disorders and occurs with an incidence of 68.8/100,000 person-years.
The age-adjusted incidence of epilepsy is estimated to be 44/100,000 person-years.
Despite the introduction of new antiepileptic drugs in the last decades, one-third of people with epilepsy
continue to have seizures despite treatment.
However, even when seizures are well controlled, self-reported quality of life is significantly lowered by the
anxiety associated with the unpredictable nature of seizures and the consequences therefrom.
Some of the difficulties in managing treatment-refractory epilepsy can be ameliorated by the ability
to detect clinical seizures. This information might be useful both in developing accurate seizure diaries
and in providing therapies during times of greatest seizure susceptibility. The ability to rapidly and accurately
detect seizures could promote therapies aimed at rapidly treating seizures. The capability to detect seizures early and
anticipate their onset prior to presentation would provide even greater advantages. These early detection and prediction s
ystems might be able to abort seizures through targeted therapies. Such systems would also be able to prevent
accidents and limit injury.
Typically seizures are detected by analyzing the electroencephalogram (EEG), but obtaining it outside the
hospital is too difficult for long-term monitoring. The electrocardiogram (ECG) is however easily obtainable in a
home environment. Earlier studies showed that most tonic-clonic (TC) seizures are accompanied by a specific heart rate (HR) pattern.
This project was started as our 2nd year semester project of the Computer Science and Engineering Department,
the University of Moratuwa in February 2019. The device with EEG was designed by us under
Dr Shantha Fernando (Consultant Neurologist).
Our target is to build a portable 4-channel EEG along with heart rate sensor So the final project will be a
“Low-cost wearable device to identify Tonic-clonic seizure using 4 channel EEG and heartbeat”
There are devices in the market which detects seizure like Nightwatch which is based on non-EEG devices. These competitive devices are based on ECG or heartbeat pattern. One key disadvantage of the Nightwatch device is as it is a non-EEG device, the false positive rate is very high. Even for cold or after a long run your heart rate pattern can be identified as a seizure pattern. The only way to detect tonic-clonic seizure is to use an EEG device. The key advantage of our device is that it is has a portable 4-channel EEG device which is specially designed for tonic-clonic seizure. This device will detect and Inform people nearby and also parents. Electronics, PCB designing and Firmware development. App development. Target is to create a wearable EEG device which connects to owners phone and inform him in case of a seizure forehand. This project started with [RatEEG] - https://github.com/tharaka27/RatEEG design. But we later moved to OpenBCI 8bit design for more accuracy. So credit of initial design goes to OpenBCI - https://github.com/OpenBCI. We have changed the hardware, firmware to match our needs.- schematic designing the EEG module
- PCB print
- Design headset for RatEEG V2
- 3d print RatEEG V2 headset
- Writing firmware
- Desktop application for debugging
- Android/ios app