Chia-Ching Chou

Time-frequency analysis of heart sound signals based on Hilbert-Huang Transformation

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.

A real-time processing flow for ICA based EEG acquisition system with eye blink artifact elimination

This paper presents a real-time processing flow for ICA based EEG acquisition system with eye blink artifact elimination. Since EEG signals are one of the feeblest physiological electrical signals, it is easily contaminated by artifacts. Previously, ICA was used to extract artifacts from an EEG data segment in a time period. After processing of ICA, automatic artifact detection and elimination are performed. After that, artifact free EEG signals are reconstructed. Recently, many kinds of EEG applications such as BCIs are proposed to control machines through EEG directly. In order to make BCIs more feasible and reliable, the EEG signals used for BCIs need to be acquired from human without artifacts in real-time. In this work, a real-time ICA algorithm, ORICA, is adopted. Since eye blink artifact dose the most significant harm to EEG signals, this work focus on the automatic eye blink artifact elimination and the algorithm used for eye blink artifact detection is sample entropy. With these algorithms and the real-time processing flow we proposed, processing result of each EEG raw data is finished in 0.25 s after each sample time. In the end of this paper, the method used to evaluate the performance of eye blink artifact elimination is provided. Real EEG signals are also processed and the operation results are shown to remove the eye blink artifacts exactly without misses.

A novel wireless biomedical monitoring system with dedicated FPGA-based ECG processor

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 devices 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.

An effective three way PPG acquiring and signal processing system by using square wave modulation

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 6 MHz 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..

Smart shoes design with embedded monitoring electronics system for healthcare and fitness applications

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.

An Efficient VLSI Implementation of SVD Processor of On-line Recursive ICA for Real-time EEG System

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.

A wireless photoplethysmography signal processing system for long-term monitoring

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 EEG analog front-end design with wireless communication module for a portable EEG monitoring system

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

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.

FPGA implementation of EEG system-on-chip with automatic artifacts removal based on BSS-CCA method

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.