Integrating Markov Model, Bivariate Gaussian Distribution and GPU Based Parallelization for Accurate Real-Time Diagnosis of Arrhythmia Subclasses

Title

Integrating Markov Model, Bivariate Gaussian Distribution and GPU Based Parallelization for Accurate Real-Time Diagnosis of Arrhythmia Subclasses

Creator

Gawde Purva R; Bansal Arvind K; Nielson Jeffery A

Publisher

Proceedings of the Future Technologies Conference (ftc) 2018, Vol 1

Date

2019
1905-07

Description

In this paper, we present the integration of SIMT (Single Instruction Multiple Threads), Markov model and bivariate Gaussian distribution as a general-purpose technique for real-time accurate diagnosis of subclasses of arrhythmia. The model improves the accuracy by integrating both morphological and temporal features of ECG. GPU based implementation exploits concurrent execution of multiple threads at the heart-beat level to improve the execution efficiency. The approach builds a bivariate Gaussian Markov model (BGMM) for each subclass of arrhythmia where each state includes bivariate distribution of temporal and morphological features of each waveform and ISO-lines using ECG records for each subclass from standard databases, and the edge-weights represent the transition probabilities between states. Limited 30-second subsequences of a patient's beats are used to develop bivariate Gaussian transition graphs (BGTG). BGTGs are matched with each of the BGMMs to derive the exact classification of BGTGs. Our approach exploits data-parallelism at the beat level for ECG preprocessing, building BGTGs and matching multiple BGTG-BGMM pairs. SIMT (Single Instruction Multiple Thread) available on CUDA resources in GPU has been utilized to exploit data-parallelism. Algorithms have been presented. The system has been implemented on a machine with NVIDIA CUDA based GPU. Test results on standard MIT- BIH database show that GPU based SIMT improves execution time further by 78% with an overall speedup of 4.5 while retaining the accuracy achieved by the sequential execution of the approach around 98%.

Rights

Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).

Format

Book Section

Search for Full-text

Users with a NEOMED Library login can search for full-text journal articles at the following url: https://libraryguides.neomed.edu/home

Pages

569-588

Volume

880

NEOMED College

NEOMED College of Medicine

NEOMED Department

Department of Emergency Medicine

Update Year & Number

March 2020 Update

Affiliated Hospital

Summa Health Akron

Citation

Gawde Purva R; Bansal Arvind K; Nielson Jeffery A, “Integrating Markov Model, Bivariate Gaussian Distribution and GPU Based Parallelization for Accurate Real-Time Diagnosis of Arrhythmia Subclasses,” NEOMED Bibliography Database, accessed July 24, 2021, https://neomed.omeka.net/items/show/11002.

Social Bookmarking