GPU-Based Acceleration of ECG Characterization Using High-Order Hermite Polynomials

Title:GPU-Based Acceleration of ECG Characterization Using High-Order Hermite Polynomials

Volume: 11 Issue: 4

Author(s): Alberto Gil, David G. Márquez, Gabriel Caffarena, Ana Iriarte and Abraham Otero

Affiliation:

Keywords: Electrocardiogram, Hermite polynomials, graphics processing unit, CUDA, arrhythmia, clustering.

Abstract: In this paper we address the acceleration of the Hermite function characterization of the heartbeat by means of massively parallel Graphics Processing Units. This characterization can be used to develop tools to help the cardiologist to study and diagnose heart disease. However, obtaining this characterization, especially when a large number of functions is used to achieve a high accuracy in heartbeat representation, is very resource intensive. This paper addresses off-line and on-line heartbeat characterization, assessing the acceleration capabilities of Graphics Processing Units for these tasks. Polynomials up to the 30^th order are used in the study. The results yield that the off-line processing of long electrocardiogram recordings with a GPU can be computed up to 186 faster than a standard CPU, while real-time processing can be up to 110x faster.

Cite this article as:

Gil Alberto, Márquez G. David, Caffarena Gabriel, Iriarte Ana and Otero Abraham, GPU-Based Acceleration of ECG Characterization Using High-Order Hermite Polynomials, Current Bioinformatics 2016; 11 (4) . https://dx.doi.org/10.2174/1574893611666160212235711