A Low-Latency, Low-Power FPGA Implementation of ECG Signal Characterization Using Hermite Polynomials

Abstract

Automatic ECG signal characterization is of critical importance in patient monitoring and diagnosis. This process is computationally intensive, and low-power, online (real-time) solutions to this problem are of great interest. In this paper, we present a novel, dedicated hardware implementation of the ECG signal processing chain based on Hermite functions, aiming for real-time processing. Starting from 12-bit ADC samples of the ECG signal, the hardware implements filtering, peak and QRS detection, and least-squares Hermite polynomial fit on heartbeats. This hardware module can be used to compress ECG data or to perform beat classification. The hardware implementation has been validated on a Field Programmable Gate Array (FPGA). The implementation is generated using an algorithm-to-hardware compiler tool-chain and the resulting hardware is characterized using a low-cost off-the-shelf FPGA card. The single-beat best-fit computation latency when using six Hermite basis polynomials is under 1 s with a throughput of 3 beats/s and with an average power dissipation around 28 mW, demonstrating true real-time applicability.

Harnessing FPGA Technology for Enhanced Biomedical Computation

“…The parallel processing capabilities of FPGAs, coupled with their reconfigurable nature, allow for the design of optimized systems that process ECG signals much more rapidly than conventional computing systems. This rapid analysis is critical in medical emergencies, such as in the detection of life-threatening conditions like arrhythmia or cardiac ischemia [6].…”

Harnessing FPGA Technology for Enhanced Biomedical Computation

“…The parallel processing capabilities of FPGAs, coupled with their reconfigurable nature, allow for the design of optimized systems that process ECG signals much more rapidly than conventional computing systems. This rapid analysis is critical in medical emergencies, such as in the detection of life-threatening conditions like arrhythmia or cardiac ischemia [6].…”

Optimizing Real-Time ECG Data Transmission in Constrained Environments

“…The remaining research efforts target power consumption optimization by implementing customized hardware circuits [11] or deploying classification on the cloud [12] [13]. As a result, there is a significant gap in testing the performance and durability of the ECG monitoring platforms on real ECG systems.…”