Machine-based patient-specific seizure classification system

Abstract

This disclosure is directed to a machine-based patient-specific seizure classification system. In general, an example system may comprise a non-linear SVM seizure classification system-on-chip (SoC) with multichannel EEG data acquisition and storage for epileptic patients is presented. The SoC may integrate a hardware-efficient log-linear Gaussian Basis Function engine, floating point piecewise linear natural log, and low-noise, high dynamic range readout circuits. In at least one example implementation, the SoC may consume 1.83W/classification while classifying 8 channel results with an average detection rate, average false alarm rate and latency of 95.1%, 0.94% and <2 s, respectively.

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