A low complexity patient-specific threshold based accelerator for the Grand-mal seizure disorder

This paper presents a 2-channel electroencephalograph (EEG) based seizure detection accelerator suitable for long-term continuous monitoring of patients suffering from the Grand-mal seizure disorder. The implementation is based on the novel slope based detection (SBD) algorithm to achieve start and end of seizure detection. The proposed SBD algorithm is verified experimentally using a full FPGA implementation with patients' recordings from Physionet Children Hospital Boston (CHB)-MIT EEG database with real-time seizure, information display on the Android phone through a low-power Bluetooth link. The patient-specific detection with specific threshold results in sensitivity, specificity, system latency, and detection latency of 91.2%, 93.6%, 0.5s, and 29.25 s, respectively, using the CHB-MIT EEG database.