A custom reduced instruction set computer-V based architecture for real-time electrocardiogram feature extraction - Shinde, Vinayak Vikram; Bhandari, Sheetal Umesh; Khurge, Deepti Snehal; Nagarale, Satyashil Dasharath; Shirode, Ujwal Ramesh (Institute of Advanced Engineering and Science, 2025)
The growing demand for energy-efficient and real-time biomedical signal processing in wearable devices has necessitated the development of application-specific and reconfigurable embedded hardware architectures. This paper presents the register transfer level (RTL) design and simulation of a custom reduced instruction set computer-V (RISC-V) based hardware architecture tailored for real-time electrocardiogram (ECG) feature extraction, focusing on R-peak detection and heart rate (HR) calculation. The proposed system combines ECG-specific functional blocks including a specialized ECG arithmetic logic unit and a finite state machine-based ECG control unit with a compact 16-bit RISC-V control core. Hardware optimized algorithms are used to carry out pre-processing activities such high-pass and low-pass filtering as well as feature extraction processes including moving average filtering, derivative calculation, and threshold based peak identification. Designed to reduce memory footprint and control complexity, a custom instruction set architecture supports modular reconfigurability. Functional validation is carried out by Xilinx Vivado simulating RTL components described in very high speed integrated circuit (VHSIC) hardware description language (VHDL). The present work shows successful simulation of important architectural components, complete system-level integration and custom ECG data validation. This work provides the basis for an application-specific, reconfigurable, power efficient hardware solution for embedded health-monitoring devices.