Analysis of the Impact of Hardware Front-End Limitations on Vagus Nerve Electroneurogram-Based Seizure Detection

The detection of seizures from the vagus nerve electroneurogram (VENG) is an emerging application allowing minimally invasive closed-loop vagus nerve stimulation for the treatment of epilepsy. Several VENG-specific front-end designs were presented earlier, but their specifications were approximate or arbitrary, leading to inefficient or sub-optimal designs in an applicative context. In this work, we analyze how front-end design choices and non-idealities impact seizure detection performance. The sensitivity analysis leads to the presentation of potentially optimal front-end designs with low power consumption.

This work uses experimental VENG data from 8 rats, a behavioral model of the front-end circuits, and a seizure detection algorithm based on template matching. The studied front-end limitations are intrinsic noise, common-mode rejection, dynamic range, quantization resolution, and oversampling rate, studied individually in a sensitivity analysis. Optimal designs are then proposed based on sensitivity results. The performance of the seizure detection algorithm is characterized by metrics that are least affected by the small dataset size.

Noise and dynamic range are the main factors impacting algorithm performance. An optimal front-end design with 3-μV_RMS noise, 6-bit quantization, and 2.1-μW power consumption is presented and achieves perfect seizure classification on the dataset.

The presented analysis enables the optimization of front-end specifications and a significant reduction in power consumption.

This work bridges the gap between the performance of a seizure detection algorithm and the circuit-level specifications for ultra-low-power integrated bio-interface design.