@inproceedings{f4ab2eb7cec04bf1956654c3146cc4b6,
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",
address = "United States",
note = "2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 ; Conference date: 17-10-2018 Through 19-10-2018",
}
