An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal

I. Wei Chen; Shang Yi Chuang; Wen Lun Wu; Wai-Chi Fang

doi:10.1109/BIOCAS.2018.8584764

An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal

I. Wei Chen, Shang Yi Chuang, Wen Lun Wu, Wai-Chi Fang

Institute of Electronics

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

2 Scopus citations

Abstract

This study proposed an efficient hardware architecture design of Ensemble Empirical Mode Decomposition (EEMD) processor for the signal analysis of Electrocardiography (ECG). The proposed processor is implemented in an on-board Xilinx FPGA for on-line signal processing of the non-linear and non-stationary signal. The EEMD method is appropriate to analyze the non-linear ECG signal with assisting white noise and decompose the signal into 8 sets of Intrinsic Mode Functions (IMFs). The experimental result shows that the mode mixing problem, which exists in the Empirical Mode Decomposition (EMD) method, solved by the proposed EEMD processor. The study solves the obstacle of mode mixing and achieves high accuracy with data error < 4.7×10-5. This approach can effectively analyze the non-linear and non-stationary biomedical signal and facilitate cardiovascular diseases diagnosis and long-term monitoring.

Original language	English
Title of host publication	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538636039
DOIs	https://doi.org/10.1109/BIOCAS.2018.8584764
State	Published - 20 Dec 2018
Event	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Cleveland, United States Duration: 17 Oct 2018 → 19 Oct 2018

Publication series

Name	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings

Conference

Conference	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Country/Territory	United States
City	Cleveland
Period	17/10/18 → 19/10/18

Keywords

Electrocardiography
Ensemble Empirical Mode Decomposition (EEMD)
Field Programmable Gate Array (FPGA)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/BIOCAS.2018.8584764

Cite this

Chen, I. W., Chuang, S. Y., Wu, W. L., & Fang, W-C. (2018). An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal. In 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings Article 8584764 (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2018.8584764

Chen, I. Wei ; Chuang, Shang Yi ; Wu, Wen Lun et al. / An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal. 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings).

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title = "An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal",

abstract = "This study proposed an efficient hardware architecture design of Ensemble Empirical Mode Decomposition (EEMD) processor for the signal analysis of Electrocardiography (ECG). The proposed processor is implemented in an on-board Xilinx FPGA for on-line signal processing of the non-linear and non-stationary signal. The EEMD method is appropriate to analyze the non-linear ECG signal with assisting white noise and decompose the signal into 8 sets of Intrinsic Mode Functions (IMFs). The experimental result shows that the mode mixing problem, which exists in the Empirical Mode Decomposition (EMD) method, solved by the proposed EEMD processor. The study solves the obstacle of mode mixing and achieves high accuracy with data error < 4.7×10-5. This approach can effectively analyze the non-linear and non-stationary biomedical signal and facilitate cardiovascular diseases diagnosis and long-term monitoring.",

keywords = "Electrocardiography, Ensemble Empirical Mode Decomposition (EEMD), Field Programmable Gate Array (FPGA)",

author = "Chen, {I. Wei} and Chuang, {Shang Yi} and Wu, {Wen Lun} and Wai-Chi Fang",

year = "2018",

month = dec,

day = "20",

doi = "10.1109/BIOCAS.2018.8584764",

language = "English",

series = "2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings",

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

booktitle = "2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings",

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note = "2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 ; Conference date: 17-10-2018 Through 19-10-2018",

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Chen, IW, Chuang, SY, Wu, WL & Fang, W-C 2018, An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal. in 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings., 8584764, 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018, Cleveland, United States, 17/10/18. https://doi.org/10.1109/BIOCAS.2018.8584764

An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal. / Chen, I. Wei; Chuang, Shang Yi; Wu, Wen Lun et al.
2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8584764 (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings).

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

TY - GEN

T1 - An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal

AU - Chen, I. Wei

AU - Chuang, Shang Yi

AU - Wu, Wen Lun

AU - Fang, Wai-Chi

PY - 2018/12/20

Y1 - 2018/12/20

N2 - This study proposed an efficient hardware architecture design of Ensemble Empirical Mode Decomposition (EEMD) processor for the signal analysis of Electrocardiography (ECG). The proposed processor is implemented in an on-board Xilinx FPGA for on-line signal processing of the non-linear and non-stationary signal. The EEMD method is appropriate to analyze the non-linear ECG signal with assisting white noise and decompose the signal into 8 sets of Intrinsic Mode Functions (IMFs). The experimental result shows that the mode mixing problem, which exists in the Empirical Mode Decomposition (EMD) method, solved by the proposed EEMD processor. The study solves the obstacle of mode mixing and achieves high accuracy with data error < 4.7×10-5. This approach can effectively analyze the non-linear and non-stationary biomedical signal and facilitate cardiovascular diseases diagnosis and long-term monitoring.

AB - This study proposed an efficient hardware architecture design of Ensemble Empirical Mode Decomposition (EEMD) processor for the signal analysis of Electrocardiography (ECG). The proposed processor is implemented in an on-board Xilinx FPGA for on-line signal processing of the non-linear and non-stationary signal. The EEMD method is appropriate to analyze the non-linear ECG signal with assisting white noise and decompose the signal into 8 sets of Intrinsic Mode Functions (IMFs). The experimental result shows that the mode mixing problem, which exists in the Empirical Mode Decomposition (EMD) method, solved by the proposed EEMD processor. The study solves the obstacle of mode mixing and achieves high accuracy with data error < 4.7×10-5. This approach can effectively analyze the non-linear and non-stationary biomedical signal and facilitate cardiovascular diseases diagnosis and long-term monitoring.

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KW - Ensemble Empirical Mode Decomposition (EEMD)

KW - Field Programmable Gate Array (FPGA)

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ER -

Chen IW, Chuang SY, Wu WL, Fang W-C. An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal. In 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8584764. (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings). doi: 10.1109/BIOCAS.2018.8584764

An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal

Abstract

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Keywords

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