FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm

This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs). Article Highlights First one is data compression which was successfully achieved with Cellular Automata techniques and 85% memory is saved. Second the compressed data set trained to the machine learning algorithm was developed and implemented in the FPGA with very negligible delay and very high accuracy. The seizure affected people will have very less time to take the medical diagnosis. So the proposed work will be helpful to detect the abnormal states so they can react early for damage prevention.