Fast pattern classification of ventricular arrhythmias using graphics processing units



Graphics Processing Units (GPUs) can provide remarkable performance gains when compared to CPUs for computationally-intensive applications. In the biomedical area, most of the previous studies are focused on using Neural Networks (NNs) for pattern recognition of biomedical signals. However, the long training times prevent them to be used in real-time. This is critical for the fast detection of Ventricular Arrhythmias (VAs) which may cause cardiac arrest and sudden death. In this paper, we present a parallel implementation of the Back-Propagation (BP) and the Multiple Back-Propagation (MBP) algorithm which allowed significant training speedups. In our proposal, we explicitly specify data parallel computations by defining special functions (kernels); therefore, we can use a fast evaluation strategy for reducing the computational cost without wasting memory resources. The performance of the pattern classification implementation is compared against other reported algorithms.


GPU Computing, Parallel Programming, Neural Networks


GPU Computing, Neural networks


14th Iberoamerican Congress on Pattern Recognition (CIARP 2009), November 2009


Cited by

Year 2011 : 1 citations

 Laurentiu Bucur, Sparse Kernel Machines and High Performance Computing, PhD thesis, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 2011.