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

Topics: Bioelectronics; Biomedical Equipment & Instrumentation; Biomedical Sensors; Diagnostic Devices; Electrocardiography and Heart Monitoring; Health Monitoring Devices

Authors: Vijendra V. 1 ; Meghana Kulkarni 1 and Rajesh Murgan 2

Affiliations: 1 Center for PG Studies, Dept. of VLSI Design & Embedded Systems, Visvesvaraya Technological University, Belgaum, Karnataka, India ; 2 CoreEL Technologies (I) Pvt Ltd , Bangalore, India

ISBN: 978-989-758-353-7

Keyword(s): ZynQ System on Chip (SoC), Internet of Things (IoT), Think Speak Cloud, Object Identification, Dynamic Reconfigure Port (DRP), Transmission Control Protocol/Internet Protocol, Peripheral Component Interconnect Express (PCIe), Direct Memory Access.

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.

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Paper citation in several formats:
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 - Volume 1: BIODEVICES, ISBN 978-989-758-353-7, pages 261-268. DOI: 10.5220/0007576902610268

@conference{biodevices19,
author={V, 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 - Volume 1: BIODEVICES,},
year={2019},
pages={261-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007576902610268},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - 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

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