Two-stage Hardware-Friendly Epileptic Seizure Detection Method with a Dynamic Feature Selection

Abstract

A novel low-complexity method of detecting epileptic seizures from intracranial encephalography (iEEG) signals is presented. In the proposed algorithm, coastline, energy and nonlinear energy features of iEEG signals are extracted in a patient-specific two-stage seizure detection system. The detection stage of the proposed system, which extracts two times more features than the monitoring stage, is only powered on when the monitoring stage detects a seizure occurrence. A new metric is defined to demonstrate the significance of the two-stage architecture and show the time duration over which the detection stage is activated. The new parameter is called detection stage activation ratio (DAR) and it is equal to 0.272 in this work. In addition, the proposed seizure detector outperforms other algorithms which utilize a single feature or multiple features continuously in terms of sensitivity, specificity and DAR. Therefore, it is highly suitable for seizure detector implants in which reducing the power consumption is a critical factor to increase the lifetime of the implanted battery. The algorithm is implemented on a Cyclone V FPGA and has a low dynamic power of 1 μW when tested on human iEEG signals of six patients from the Bern Inselspital dataset. It reaches a perfect sensitivity of 100% tested on 120 hours of iEEG data containing 24 seizure periods of six patients.

Files

In addition to the paper, software simulation and VHDL implementation codes are also provided. The MATLAB files contain the simulation procedure both in training and test phases. The VHDL codes contain the RTL implementation of the proposed seizure detector.

How to use

First, the Matlab codes must be run in the training mode. It is noteworthy that the publicaly available iEEG signal of the patients from SWEC-ETHZ dataset must be located in the same folder before simulation. After compilation of the MATLAB codes, the obtained thresholds are given to the VHDL codes as the inputs of the feature extraction. Finally, the VHDL codes must be run for each patient seperately.