Browsing by Author "Chou, Chia-Ching"

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A Novel Wireless Biomedical Monitoring System with Dedicated FPGA-based ECG Processor
(2012)
Chou, Chia-Ching
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Fang, Wai-Chi
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Huang, Hsiang-Cheh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
A novel portable and wireless biomedical monitoring system featuring on demand wireless data transmission of ECG signals and time-frequency HRV analysis for personal and home healthcare applications is presented in this work. In order to provide comfort and convenience to patients, the device's size, power consumption and portability are of first priority. The ECG processor based on previous hardware design [1] acquires three-channel ECG biomedical raw data through an analog front-end (AFE) circuit, and it measures the time between successive heart beats on lead II as RR intervals for HRV analysis. Functions such as QRS complex peak detection, RR intervals calculation, and time-frequency analysis of HRV have also been developed in hardware. A real-time HRV analysis processor is realized by employing a Lomb periodogram for time-frequency power spectral density (PSD) analysis of the heart rate. The proposed ECG monitoring system has been implemented in Field-Programmable Gate Arrays (FPGA) and it features high integration density, portability, wireless transmission and low cost.
A Wireless Photoplethysmography Signal Processing System for Long-term Monitoring
(2016)
Wu, Chih-Chin
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Fan, Shu-Han
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Chuang, Shang
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Liao, Jia-Ju
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Chou, Chia-Ching
This paper has presented a photoplethysmography (PPG) signal processing system based on ensemble empirical mode decomposition (EEMD) method. Traditionally, empirical mode decomposition (EMD) suffers from mode mixing problem during the decomposition process. This paper adopts EEMD to solve the mode mixing problem by adding different sets of white noise and decompose signal into meaningful intrinsic mode functions. The system is implemented on self-designed platform combined with front-end circuit, EEMD chip by TSMC 90nm CMOS technology, and commercial display devices. The results showed that the proposed EEMD processor can effectively solve the mode mixing problem of EMD. It was helpful for non-stationary biomedical signal processing and cardiovascular diseases research.
An Advanced Hardware Design Based on Ensemble Empirical Mode Decomposition Algorithm for Heart Sound Signal Processing
(2013)
Chou, Chia-Ching
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Lin, Kuen-Chih
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Fang, Wai-Chi
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Li, Arvin Huang-Te
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Chang, Yu-Ching
In this study, an advanced hardware design for heart sound signal processing based on ensemble empirical mode decomposition (EEMD) is developed and implemented. The EEMD method [1] is developed to alleviate a key drawback in the original empirical mode decomposition (EMD) algorithm. In a previous research, Huang et al. [2] developed an adaptive and efficient EMD method for nonlinear and nonstationary signal analysis. The physical meaning of a single intrinsic mode function (IMF) is obscure, and the original EMD algorithm cannot separate signals with different scales into appropriate IMFs. To overcome this major drawback, a noise-assisted data analysis (NADA) method called EEMD is developed. Heart sound signals are fed into the proposed system to simulate the EEMD-fixed-point performance. A comparison of the floating-point and fixed-point results exhibits satisfactory consistency and demonstrates that our design can accommodate wide variations of dynamic ranges and complicated calculations.
An Advanced Patient Bedside Monitoring System Design Based on WBAN and Telemedicine Technologies
(2015)
Fahier, Nicolas
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Huang, Wei-Chin
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Chou, Chia-Ching
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Fang, Wai-Chi
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電機學院
An advanced patient-centric bedside monitoring system design based on wireless body area sensor networks and telemedicine technologies is proposed in this paper where each patient uses sensors with wireless transmission capabilities and transmit the physiological signals within a larger advanced network, which is connected to a secured section of the public network. This work intends to reduce the number of connections to the patient and proposes a wireless intelligent Intensive Care Unit (ICU) data security system to provide a wireless remote access to a patient\'s information anytime, and increase the information security to protect the patient\'s privacy.
An EEG Analog Front-End Design with Wireless Communication Module for a Portable EEG Monitoring System
(2015)
Chen, Wei-Chen
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Chen, Yi-Chung
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Chou, Chia-Ching
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Fang, Wai-Chi
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電機學院
In this paper, a wireless electroencephalography (EEG) acquisition analog front-end (AFE) circuit design is presented. The wireless EEG acquisition system includes an analog front-end readout chipset (AFERC) with extendable channel, a low-power MSP430 microcontroller from Texas Instruments and a commercial Bluetooth 2.0 module (BTM). The front-end readout chipset not only applies the ability of noise attenuation but also modulates the EEG signal into appropriate amplitude. It has individual 10-bit successive approximation register analog-to-digital converter (SAR-ADC) for each EEG channel, and it transmits the EEG signals which have been serially converted to digital format to MSP430. MSP430 is responsible for rearranging the converted digital bits and controlling the BTM with built-in Universal Asynchronous Receiver/Transmitter (UART) interface. The AFERC maximumly reaches to 64 channels, and single channel consumes 80.268 uW. Total power consumption of AFERC is 2.6 mW at with 32 channels, and its Common-Mode-Reject-Ratio (CMRR) is 75 dB.
An Effective Arterial Blood Pressure Signal Processing System Based on EEMD Method
(2014-01-01)
Chuang, Shang-Yi
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Liao, Jia-Ju
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Chou, Chia-Ching
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Chang, Chia-Chi
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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
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Chuang, Shang-Yi
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Chou, Chia-Ching
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Chang, Chia-Chi
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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 Effective Three Way PPG Acquiring and Signal Processing System by Using Square Wave Modulation
(2015)
Chou, Chia-Ching
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Huang, Wei-Chin
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Fang, Wai-Chi
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電機學院
;
電子工程學系及電子研究所
In this paper, an effective photoplethysmograph (PPG) acquiring and signal processing system based on square wave modulation is proposed. Through modulating the acquired signals on different frequency square waves, it is effective to get PPG signals with high SNR by using simple carrier wave generators and reduce the computation load and power. The proposed system includes an exquisite and wearable three way PPG front-end sensor for acquiring PPG signals, an FPGA-based DSP for signal modulation and an analog circuit with an efficient anti-aliasing filter for eliminating serious harmonic waves of the carriers. Finally, the proposed DSP is scheduled for chip fabrication using TSMC 0.18 um CMOS technology to accelerate the performance and reduce the cost. The computation load is reduced by 98.8% compared to conventional system without effective signal modulation and filtering. The proposed DSP operates at 6MHz and reduce 35.2% of power consumption with only 0.58 mW of power compared with the FPGA-based DSP. The proposed system is able to acquire stable PPG signals to accurately derive the physiological indexes of cardiovascular system such as heart rate, respiratory rate, oxygen concentration, etc..
An Efficient and Accurate Empirical Mode Decomposition of the Technical Design and Methods for Biological Sound
(2012)
Fang, Wai-Chi
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Chou, Chia-Ching
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Hung, Tzu-Hsun
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Lin, Kuen-Chih
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Li, Arvin Huang-Te
An EMD-based algorithm for bio-sound signal processing was proposed and implemented on VEX330 Xilinx FPGA. A free-format fixed point library was development to deal with overflow and underflow problems. Downsampled phonocardiogram signals were fed into our EMD-processor. Comparison of floating point and fixed point results exhibited satisfactory consistency and demonstrated that our novel fixed point library are able to accommodate with wild variations of dynamic ranges.
An Efficient ASIC Implementation of 16-channel On-line Recursive ICA Processor for Real-time EEG System
(2014-01-01)
Fang, Wai-Chi
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Huang, Kuan-Ju
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Chou, Chia-Ching
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Chang, Jui-Chung
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Cauwenberghs, Gert
This is a proposal for an efficient very-large-scale integration (VLSI) design, 16-channel on-line recursive independent component analysis (ORICA) processor ASIC for real-time EEG system, implemented with TSMC 40 nm CMOS technology. ORICA is appropriate to be used in real-time EEG system to separate artifacts because of its highly efficient and real-time process features. The proposed ORICA processor is composed of an ORICA processing unit and a singular value decomposition (SVD) processing unit. Compared with previous work [1], this proposed ORICA processor has enhanced effectiveness and reduced hardware complexity by utilizing a deeper pipeline architecture, shared arithmetic processing unit, and shared registers. The 16-channel random signals which contain 8-channel super-Gaussian and 8-channel sub-Gaussian components are used to analyze the dependence of the source components, and the average correlation coefficient is 0.95452 between the original source signals and extracted ORICA signals. Finally, the proposed ORICA processor ASIC is implemented with TSMC 40 nm CMOS technology, and it consumes 15.72 mW at 100 MHz operating frequency.
An Efficient VLSI Implementation of SVD Processor of On-line Recursive ICA for Real-time EEG System
(2014-01-01)
Fang, Wai-Chi
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Chang, Jui-Chung
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Huang, Kuan-Ju
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Feng, Chih-Wei
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Chou, Chia-Ching
This paper presents an efficient VLSI implementation of a singular value decomposition (SVD) processor of on-line recursive independent component analysis (ORICA) for use in a real-time electroencephalography (EEG) system. ICA is a well-known method for blind source separation (BBS), which helps to obtain clear EEG signals without artifacts. In general, computations of ORICA are complicated and the critical computational latency is associated with the SVD process. Accordingly, the performance of the SVD processor should be prioritized. Going beyond previous research [1], this work presents a novel design of coordinate rotation digital computer (CORDIC) engine which is optimized and speeded up to avoid structural hazards. Finally, the processor is fabricated using TSMC 40nm CMOS technology in a 16-channel EEG system. The computation time of the SVD processor is reduced by 24.7% and the average correlation coefficient between original source signals and extracted ORICA signals is 0.95452.
Diffuse Optical Tomography Image Reconstruction Based on Sparse Recovery Methods
(2015-01-01)
Sun, Hao-Jan
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Hou, Hsiang-Wen
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Chou, Chia-Ching
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Fang, Wai-Chi
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電機工程學系
Diffuse Optical Tomography ( DOT) is a non-invasive image detecting technique. It is used to assess spatial variation with absorption and scattering coefficients for tumor detection, distribution of oxygen concentration analysis, oxygenated hemoglobin concentration measurement or deoxygenated hemoglobin concentration measurement. In this paper, we use sparse recovery methods for DOT image reconstruction. Using the non-linear iterative method to reconstruct the DOT image can increase the resolution of each reconstructed layer. Sparse recovery methods use the p-norm regularization in the estimation problem with 0 < p <= 1. When the number of independent measurements is limited by nature, which is a typical case for diffuse optical tomographic image reconstruction, sparse recovery methods present good performance. According to the simulation results the reconstruction algorithm can parse the tumor clearly with p=0.6. The projection errors are 0.45357, 0.44588, 0.46781, 0.44109, and 0.47174( tumor / background) in each tumor position case using p=0.6. Since that the projection errors with p=0.6 are smaller than projection errors using p=1 or p=2, p=0.6 is selected to be the reconstructed parameter to have the best reconstructed results for the same iteration numbers. Simulation results show that the sparse recovery methods are good to improve the reconstructed image quality.
FPGA Implementation of EEG System-on-Chip with Automatic Artifacts Removal based on BSS-CCA Method
(2016-01-01)
Chou, Chia-Ching
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Chen, Tsan-Yu
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Fang, Wai-Chi
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電機工程學系
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Department of Electrical and Computer Engineering
This paper presents an automatic muscle artifacts removal system for multi-channel electroencephalogram (EEG) applications. Since EEG signals are very weak and highly sensitive to the environment, they are easily contaminated by noises and artifacts. To get clean and usable EEG signals for brain-computer interface (BCI) applications, we should acquire these signals from the human brain without artifacts. Recently, Blind Source Separation (BSS) technique based on Canonical Correlation Analysis (CCA) was proposed to reconstruct clean EEG signals from recordings by removing muscle artifacts components. To enhance the feasibility and reliability of BCIs, EEG processing systems used for BCIs should be more portable and signals should be acquired in real-time without artifacts. To match with these requirements, a hardware design of the artifacts removal system is adopted for artifacts extraction. The performance of eye-blink and muscle artifacts elimination is evaluated through the correlation coefficients between processed and pure EEG signals. The experimental results show that the average correlation coefficients for eye-blink and muscle elimination are 0.9341 and 0.8927 respectively.
Real-Time Obstructive Sleep Apnea Detection from Frequency Analysis of EDR and HRV using Lomb Periodogram
(2015)
Fan, Shu-Han
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Chou, Chia-Ching
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Chen, Wei-Chen
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Fang, Wai-Chi
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電機工程學系
In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.
Smart Shoes Design with Embedded Monitoring Electronics System for Healthcare and Fitness Applications
(2016)
Hwang, Po-Yu
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Chou, Chia-Ching
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Fang, Wai-Chi
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Hwang, Ching-Ming
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電子工程學系及電子研究所
In this paper, the system architecture and adherence method for a wearable medicare health and fitness monitoring system mounted shoe is presented. This system includes an integrated modular monitoring circuit that provides for fitness and biomedical information including coordinate tracker, step counter, calorie counter, as well as for biomedical information such as foot oxygen concentration. A commercial BLE module is used in communicating data wireless between the module system and a display platform. It is purposed with load cells and pressure sensors that will shut down the system to avoid unnecessary battery usage when no pressure feedback is received.
Spectral Analysis of Photoplethysmography Based on EEMD Method
(2015)
Chuang, Shang-Yi
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Liao, Jia-Ju
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Chou, Chia-Ching
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Chang, Chia-Chi
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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.
Time-Frequency Analysis of Heart Sound Signals Based on Hilbert-Huang Transformation
(2012)
Hung, Tzu-Hsun
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Chou, Chia-Ching
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Fang, Wai-Chi
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Li, Arvin Huang-Te
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Chang, Yu-Ching
In this study, a method based on Hilbert-Huang Transformation (HHT) for time-frequency analysis of heart sound signals is presented. HHT is employed because most biomedical signals such as Electroencephalogram (EEG), Electrocardiogram (ECG) and heart sound signals are non-stationary signals. Heart sound signals recordings are often contaminated with the spike noise caused by the front-end circuits or measurement instruments in the real situations. A digital median filter is firstly employed to remove the spike noise of the heart sound signals. Then, the time series data are decomposed into several IMFs (Intrinsic Mode Function) using Empirical Mode Decomposition (EMD) algorithm. Hilbert transformation algorithm is utilized to acquire the instantaneous frequency for every IMF. Simulation results show that time-frequency domain analysis of heart sounds signals based on HHT algorithm is able to offer higher frequency resolution.