ECG Pattern Recognition and Beat Classification using Internet of Things and Hardware Acceleration on ZynQ (SOC) Platform with High Performance Computational PCIe Protocol

Vijendra V., Meghana Kulkarni, Rajesh Murgan

2019

Abstract

The ECG signals plays an important vital role in Diagnostics Systems. The Real time Hardware Implementation Provides Accuracy, speed, Beat classification, predictivity and diagnostics of the system Interpretation and classification The ECG Signal extraction from sensor and Processing on the Zynq SoC Platform and Imported on to the cloud in involves three Steps: i) Real time data fetch from the Sensor device ii) Pushing on to the Cloud uproot Using TCP/IP Protocol iii) Cloud IDE Processing Using SDAccel openCL language with Amazon FPGA Image (AFI) on Virtual servers with the help of openCL/C++ libraries, Xilinx SDx Environments and virtual JTAG interfaces on Xilinx Virtex Ultrascale Plus Board(VU9P) low profile PCIe accelerated Board.

Paper Citation

in Harvard Style

V. V., Kulkarni M. and Murgan R. (2019). ECG Pattern Recognition and Beat Classification using Internet of Things and Hardware Acceleration on ZynQ (SOC) Platform with High Performance Computational PCIe Protocol. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 1: BIODEVICES; ISBN 978-989-758-353-7, SciTePress, pages 261-268. DOI: 10.5220/0007576902610268

in Bibtex Style

@conference{biodevices19,
author={Vijendra V. and Meghana Kulkarni and Rajesh Murgan},
title={ECG Pattern Recognition and Beat Classification using Internet of Things and Hardware Acceleration on ZynQ (SOC) Platform with High Performance Computational PCIe Protocol},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 1: BIODEVICES},
year={2019},
pages={261-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007576902610268},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 1: BIODEVICES
TI - ECG Pattern Recognition and Beat Classification using Internet of Things and Hardware Acceleration on ZynQ (SOC) Platform with High Performance Computational PCIe Protocol
SN - 978-989-758-353-7
AU - V. V.
AU - Kulkarni M.
AU - Murgan R.
PY - 2019
SP - 261
EP - 268
DO - 10.5220/0007576902610268
PB - SciTePress