Browsing by Author "Chuang, Shang-Yi"

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An Effective Arterial Blood Pressure Signal Processing System Based on EEMD Method
(2014-01-01)
Chuang, Shang-Yi
;
Liao, Jia-Ju
;
Chou, Chia-Ching
;
Chang, Chia-Chi
;
Fang, Wai-Chi
This study proposed an effective signal processing system based on Ensemble Empirical Mode Decomposition (EEMD) method for the analysis of arterial blood pressure (ABP). The whole system was implemented on an ARM-based SoC development platform to attain the on-line non-stationary signal processing. A non-invasive blood pressure acquisition device (NIBP100D) was used to record the continuous ABP as the input signal. According to the non-stationary characteristics of ABP, EEMD is useful to achieve accurate decomposition for ABP spectral analysis. The signal was decomposed into several Intrinsic Mode Functions (IMFs) by EEMD, and quantitatively assessed by fast Fourier transform (FFT). The results showed that the proposed EEMD processor can effectively solve the mode mixing problem of Empirical Mode Decomposition (EMD) and the FFT spectrum of IMF5, IMF6, and IMF7 to reveal heart rate and respiration.
An Effective Photoplethysmography Signal Processing System Based on EEMD Method
(2015)
Liao, Jia-Ju
;
Chuang, Shang-Yi
;
Chou, Chia-Ching
;
Chang, Chia-Chi
;
Fang, Wai-Chi
This study proposed an effective signal processing system based on Ensemble Empirical Mode Decomposition (EEMD) method for the analysis of Photoplethysmography (PPG). The whole system was implemented on an ARM-based SoC development platform to attain the on-line non-stationary signal processing. A non-invasive near-infrared light sensing device was used to record the continuous PPG as the input signal. According to the non-stationary characteristics of PPG, EEMD is useful to achieve accurate analysis for PPG. The signal was decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. The results showed that the proposed EEMD processor can effectively solve the mode mixing problem of Empirical Mode Decomposition (EMD). This study examined its possibility based on specific architecture with an on-board Xilinx FPGA. It was helpful for non-stationary biomedical signal processing and cardiovascular diseases research.
An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal
(2018-01-01)
Chen, I-Wei
;
Chuang, Shang-Yi
;
Wu, Wen-Jun
;
Fang, Wai-Chi
;
電子工程學系及電子研究所
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 < 4.7x10(-5). This approach can effectively analyze the non-linear and non-stationary biomedical signal and facilitate cardiovascular diseases diagnosis and long-term monitoring.
Spectral Analysis of Photoplethysmography Based on EEMD Method
(2015)
Chuang, Shang-Yi
;
Liao, Jia-Ju
;
Chou, Chia-Ching
;
Chang, Chia-Chi
;
Fang, Wai-Chi
In this paper, the proposed algorithm based on Hilbert-Huang Transform (HHT) is used to analyze the photoplethysmography (PPG) signal. In fact, PPG is nonlinear and nonstationary signal which is suitable for HHT method to achieve the capability of precise decomposition. The time series data are decomposed into Intrinsic Mode Function (IMF) with Ensemble Empirical Mode Decomposition (EEMD) algorithm. This study proposed a novel instantaneous pulse rate variability (iPRV) measurement based on EEMD method to analyze the very high frequency band (0.4-0.9Hz) of IMF5 which contains heart rhythm component.