Modelling and analysis of a modified preamplifier for seizure detection

This paper proposes a modified version of recycling folded cascode amplifier. A comparative study, modelling and analysis of the proposed design along with conventional folded cascode and recycling folded cascode have been presented in this work. The design is intended for use as a preamplifier in an epileptic neurostimulator circuit, which ensures high gain, low noise low power trade-off and smaller bandwidth (lower than 500 Hz) as the primary criteria. An adaptive biasing technique is implemented to enhance the key performance parameters like intrinsic gain, slew rate and gain-bandwidth product. Also, the proposed device works in weak inversion region to ensure ultra-low power consumption. There are various neurostimulators categorized as Deep Brain Stimulation and Vagus Nerve Stimulation reported in the literature so far. However the proposed design considers high-frequency oscillations (HFO) as the biomarker of focal epileptic seizures, owing to its occurrence in higher frequencies, thereby asserting a bandwidth less than 500 Hz. The simulations for the designed structures are carried out in Cadence Virtuoso using SCL 180 nm technology, with an operating voltage 0.6 V. The simulation results illustrate the improvement in several parameters: intrinsic DC gain by 14 dB with a consumed power of 1.782 mu W, and an input-referred noise of 10.97 mu V/root Hz @1 Hz, establishing that the design works with ultra-low power consumption. Analytical modelling and comparative analysis of the proposed as well as conventional designs have been performed. Additionally comparison has been drawn between the pre-layout and post layout simulation results which shows close matching between each other. Furthermore, Monte Carlo analysis has been performed on the proposed design to validate its consistency and dependability.

Automated summary

This paper presents a modified recycling folded cascode amplifier intended for use in an epileptic neurostimulator circuit. The design employs an adaptive biasing technique and operates in weak inversion region for ultra-low power consumption.