A Low-Power High-Linear Time-to-Digital Converter for Measuring R-R Intervals in ECG Signal Optimized by Neural Network and TLBO Algorithm

Abstract

In this paper, a two-stage time interpolation time-to-digital converter (TDC) is proposed to achieve adequate resolution and wide dynamic range for measuring R-R intervals in QRS detection. The architecture is based on a coarse counter and a couple of two-stage interpolator circuit in order to improve the conversion linearity. The proposed TDC is modeled with the neural network, while the teacher–learner-based optimization algorithm (TLBO) is used to optimize the integral nonlinearity (INL) of the proposed TDC. The proposed optimization method shows a characteristic close to the ideal output of the TDC behavior over a wide input range. Using the achieved results of the TLBO algorithm simulation results using CADENCE VIRTUOSO and standard 180[Formula: see text]nm CMOS technology shows 1.2[Formula: see text]s dynamic range, 100[Formula: see text]ns resolution, 0.19[Formula: see text]mW power consumption and area of 0.16[Formula: see text]mm2. The proposed circuit can find application in biomedical engineering systems and other fields where long and accurate time interval measurement is needed.