A 1.83 $\mu$J/Classification, 8-Channel, Patient-Specific Epileptic Seizure Classification SoC Using a Non-Linear Support Vector Machine

Abstract

A non-linear support vector machine (NLSVM) seizure classification SoC with 8-channel EEG data acquisition and storage for epileptic patients is presented. The proposed SoC is the first work in literature that integrates a feature extraction (FE) engine, patient specific hardware-efficient NLSVM classification engine, 96 KB SRAM for EEG data storage and low-noise, high dynamic range readout circuits. To achieve on-chip integration of the NLSVM classification engine with minimum area and energy consumption, the FE engine utilizes time division multiplexing (TDM)-BPF architecture. The implemented log-linear Gaussian basis function (LL-GBF) NLSVM classifier exploits the linearization to achieve energy consumption of 0.39 μ J/operation and reduces the area by 28.2% compared to conventional GBF implementation. The readout circuits incorporate a chopper-stabilized DC servo loop to minimize the noise level elevation and achieve noise RTI of 0.81 μ Vrms for 0.5-100 Hz bandwidth with an NEF of 4.0. The 5 × 5 mm (2) SoC is implemented in a 0.18 μm 1P6M CMOS process consuming 1.83 μ J/classification for 8-channel operation. SoC verification has been done with the Children's Hospital Boston-MIT EEG database, as well as with a specific rapid eye-blink pattern detection test, which results in an average detection rate, average false alarm rate and latency of 95.1%, 0.94% (0.27 false alarms/hour) and 2 s, respectively.