Approximate Feature Extraction for Low Power Epileptic Seizure Prediction in Wearable Devices

Epilepsy is a pervasive disorder that causes abrupt seizure attacks. This paper presents an FPGA-based logic implementation that detects impending seizure attacks using the Electroencephalogram (EEG) data-set of epileptic patients. The feature extraction is done using a 2-dimensional Fast Fourier Transform hardware architecture, and the classification is done using a software-based Artificial Neural Network (ANN) classifier. This implementation is presented in two different models, i.e., an accurate model and an approximate model. The accurate model requires more operating power but provides highly accurate results. In comparison, the approximate model provides slightly lesser accurate results but consumes significantly lesser electrical power. The Application- and scenario-based trade-offs between these models are compared against the available energy resources in the device battery. The proposed solution achieved 80.83% and 97.96% sensitivity and specificity, respectively, against 218.95mW power using the accurate feature extraction. In contrast, 77.95% and 95% sensitivity and specificity were achieved at 173.32mW power requirements for the approximate model. There is a 21% power saving in the approximate model with nearly 3% performance loss. The overall design was synthesized at 20MHz operating frequency and provided a complete 256-point FFT result in 650 mu s.