Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis

Dongkyu Lee; Seungmin Lee; Daejin Park

doi:10.1109/ISOCC53507.2021.9613894

Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis

Dongkyu Lee, Seungmin Lee, Daejin Park

School of Electronics Engineering

Kyungpook National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

With the development of Internet of things (IoT), where humans and machines interact, healthcare that measures and diagnoses bio-signals is advancing. The electrocardiogram (ECG) signal has different normal beat characteristics for each person, and it requires long-Term data for detecting abnormalities. In this paper, we increased the detection rate of the normal signals by learning the reference signal, which is the standard for diagnosing ECG signals, as individual-specific signals from existing fixed data. In addition, we proposed an OpenCL-based FPGA-GPU hybrid cooperative platform to efficiently diagnose long-Term, large-capacity ECG signals.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference 2021, ISOCC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	349-350
Number of pages	2
ISBN (Electronic)	9781665401746
DOIs	https://doi.org/10.1109/ISOCC53507.2021.9613894
State	Published - 2021
Event	18th International System-on-Chip Design Conference, ISOCC 2021 - Jeju Island, Korea, Republic of Duration: 6 Oct 2021 → 9 Oct 2021

Publication series

Name	Proceedings - International SoC Design Conference 2021, ISOCC 2021

Conference

Conference	18th International System-on-Chip Design Conference, ISOCC 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	6/10/21 → 9/10/21

Keywords

electrocardiogram
FPGA acceleration
GPU parallel programming

Access to Document

10.1109/ISOCC53507.2021.9613894

Cite this

Lee, D., Lee, S., & Park, D. (2021). Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis. In Proceedings - International SoC Design Conference 2021, ISOCC 2021 (pp. 349-350). (Proceedings - International SoC Design Conference 2021, ISOCC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC53507.2021.9613894

Lee, Dongkyu ; Lee, Seungmin ; Park, Daejin. / Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis. Proceedings - International SoC Design Conference 2021, ISOCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 349-350 (Proceedings - International SoC Design Conference 2021, ISOCC 2021).

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title = "Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis",

abstract = "With the development of Internet of things (IoT), where humans and machines interact, healthcare that measures and diagnoses bio-signals is advancing. The electrocardiogram (ECG) signal has different normal beat characteristics for each person, and it requires long-Term data for detecting abnormalities. In this paper, we increased the detection rate of the normal signals by learning the reference signal, which is the standard for diagnosing ECG signals, as individual-specific signals from existing fixed data. In addition, we proposed an OpenCL-based FPGA-GPU hybrid cooperative platform to efficiently diagnose long-Term, large-capacity ECG signals.",

keywords = "electrocardiogram, FPGA acceleration, GPU parallel programming",

author = "Dongkyu Lee and Seungmin Lee and Daejin Park",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th International System-on-Chip Design Conference, ISOCC 2021 ; Conference date: 06-10-2021 Through 09-10-2021",

year = "2021",

doi = "10.1109/ISOCC53507.2021.9613894",

language = "English",

series = "Proceedings - International SoC Design Conference 2021, ISOCC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "349--350",

booktitle = "Proceedings - International SoC Design Conference 2021, ISOCC 2021",

address = "United States",

}

Lee, D, Lee, S & Park, D 2021, Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis. in Proceedings - International SoC Design Conference 2021, ISOCC 2021. Proceedings - International SoC Design Conference 2021, ISOCC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 349-350, 18th International System-on-Chip Design Conference, ISOCC 2021, Jeju Island, Korea, Republic of, 6/10/21. https://doi.org/10.1109/ISOCC53507.2021.9613894

Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis. / Lee, Dongkyu; Lee, Seungmin; Park, Daejin.
Proceedings - International SoC Design Conference 2021, ISOCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 349-350 (Proceedings - International SoC Design Conference 2021, ISOCC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis

AU - Lee, Dongkyu

AU - Lee, Seungmin

AU - Park, Daejin

PY - 2021

Y1 - 2021

N2 - With the development of Internet of things (IoT), where humans and machines interact, healthcare that measures and diagnoses bio-signals is advancing. The electrocardiogram (ECG) signal has different normal beat characteristics for each person, and it requires long-Term data for detecting abnormalities. In this paper, we increased the detection rate of the normal signals by learning the reference signal, which is the standard for diagnosing ECG signals, as individual-specific signals from existing fixed data. In addition, we proposed an OpenCL-based FPGA-GPU hybrid cooperative platform to efficiently diagnose long-Term, large-capacity ECG signals.

AB - With the development of Internet of things (IoT), where humans and machines interact, healthcare that measures and diagnoses bio-signals is advancing. The electrocardiogram (ECG) signal has different normal beat characteristics for each person, and it requires long-Term data for detecting abnormalities. In this paper, we increased the detection rate of the normal signals by learning the reference signal, which is the standard for diagnosing ECG signals, as individual-specific signals from existing fixed data. In addition, we proposed an OpenCL-based FPGA-GPU hybrid cooperative platform to efficiently diagnose long-Term, large-capacity ECG signals.

KW - electrocardiogram

KW - FPGA acceleration

KW - GPU parallel programming

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U2 - 10.1109/ISOCC53507.2021.9613894

DO - 10.1109/ISOCC53507.2021.9613894

M3 - Conference contribution

AN - SCOPUS:85123371779

T3 - Proceedings - International SoC Design Conference 2021, ISOCC 2021

SP - 349

EP - 350

BT - Proceedings - International SoC Design Conference 2021, ISOCC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th International System-on-Chip Design Conference, ISOCC 2021

Y2 - 6 October 2021 through 9 October 2021

ER -

Lee D, Lee S, Park D. Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis. In Proceedings - International SoC Design Conference 2021, ISOCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 349-350. (Proceedings - International SoC Design Conference 2021, ISOCC 2021). doi: 10.1109/ISOCC53507.2021.9613894

Efficient Signal Processing Acceleration using OpenCL-based FPGA-GPU Hybrid Cooperation for Reconfigurable ECG Diagnosis

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