REAL-TIME PROCESSING OF MULTICHANNEL ECG SIGNALS USING GRAPHIC PROCESSING UNITS

Overview

A novel approach to real-time processing of tens to hundreds of measured ECG signals is proposed. For multichannel ECG signal processing we utilized computing capabilities of current heterogeneous computing systems consisting of CPUs and GPUs. Specifically we analyzed the potential of parallel hardware and software platform named CUDA that supports general purpose computation on GPUs. Three typical tasks were selected from the real-time ECG signal processing chain and distributed between the CPU and GPU according to their suitability and computational demands. Computationally less intensive task –⁠ data formatting and typical sequential task –⁠ data saving were executed on CPU and computationally more intensive task –⁠ data filtration was executed on the GPU using thousands of CUDA threads running in parallel. Furthermore, parallel execution on the GPU was also supported by parallel execution between the CPU and GPU using asynchronous function calls. Special attention was paid exactly to the parallelization of data filtration. A digital high-pass FIR filter for continual parallel filtration of tens of measured ECG signals was designed. The filter was realized in frequency domain using fast convolution and the overlap-save method. The CUDA platform enabled a 5.3-fold speedup of the application in comparison to its serial implementation and represents promising alternative for data-parallel signal processing algorithms.

Keywords:
Real-time signal processing, multichannel ECG, heterogeneous computing systems, CUDA platform, general purpose computing on GPU, data-parallelism, parallel digital filtration, fast convolution