Hardware Accelerators for Cardiovascular Signal Processing: A System-on-Chip Perspective

TL;DR

This paper reviews hardware solutions for real-time cardiovascular signal processing, highlighting promising technologies for improving heart disease diagnostics.

Contribution

A systematic review and performance benchmarking of hardware accelerators for cardiovascular signal processing, identifying hybrid FPGA-ASIC and AI on Edge architectures as most promising.

Findings

Hybrid FPGA-ASIC architectures and AI on Edge accelerators show superior performance in energy efficiency and processing speed.

Signal denoising, feature extraction, and ML/DL-based decision support are the three main application categories explored.

The study identifies key gaps in clinical robustness and scalability of current cardiovascular signal processing systems.

Abstract

This study presents a comprehensive systematic analysis, investigating hardware accelerators specifically designed for real-time cardiovascular signal processing, focusing mainly on Electrocardiogram (ECG), Photoplethysmogram (PPG), and blood pressure monitoring systems. Cardiovascular Diseases (CVDs) represent the world’s leading cause of morbidity and mortality, creating an urgent demand for efficient and accurate diagnostic technologies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically analysed 59 research papers on this topic, published from 2014 to 2024, categorising them into three main categories: signal denoising, feature extraction, and decision support with Machine Learning (ML) or Deep Learning (DL). A comprehensive performance benchmarking across energy efficiency, processing speed, and clinical accuracy demonstrates that hybrid Field Programmable Gate Array (FPGA)-Application Specific Integrated Circuit (ASIC) architectures and specialised Artificial Intelligence (AI) on Edge accelerators represent the most promising solutions for next-generation CVD monitoring systems. The analysis identifies key technological gaps and proposes future research directions focused on developing ultra-low-power, clinically robust, and highly scalable physiological signal processing systems. The findings provide guidance for advancing hardware-accelerated cardiovascular diagnostics toward practical clinical deployment.

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