Szi-Wen Chen

A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising

In this paper, a simple moving average-based computing method for real-time QRS detection is proposed. In addition, for signal preprocessing our detection algorithm also incorporates a wavelet-based denoising procedure to effectively reduce the noise level for electrocardiogram (ECG) data. The overall computational structure of the proposed algorithm allows the QRS detection to be performed and implemented in real-time with high time- and memory-efficiency. Algorithm performance was evaluated against the MIT-BIH Arrhythmia Database. The numerical results indicated that the novel algorithm finally achieved about 99.5% of the detection rate for the standard database, and also, it could function reliably even under the condition of poor signal quality in the measured ECG data.

A moving average based filtering system with its application to real-time QRS detection

This paper presents a novel real-time QRS detection algorithm designed based on a simple moving average filter. The proposed algorithm demands no redundant preprocessing step, thus allowing a simple architecture for its implementation as well as low computational cost. Algorithm performance was validated against a subset of the MIT-BIH arrhythmia database. Consequently, numerical results showed that the proposed algorithm correctly detected over 99.5% of the QRS complexes from the standard ECG database, implying it may be considered as a simple and reliable candidate of QRS detection algorithms.

A two-stage discrimination of cardiac arrhythmias using a total least squares-based Prony modeling algorithm

In this paper, we describe a new approach for the discrimination among ventricular fibrillation (VF), ventricular tachycardia (VT) and superventricular tachycardia (SVT) developed using a total least squares (TLS)-based Prony modeling algorithm. Two features, dubbed energy fractional factor (EFF) and predominant frequency (PF), are both derived from the TLS-based Prony model. In general, EFF is adopted for discriminating SVT from ventricular tachyarrhythmias (i.e., VF and VT) first, and PF is then used for further separation of VF and VT. Overall classification is achieved by performing a two-stage process to the indicators defined by EFF and PF values, respectively. Tests conducted using 91 episodes drawn from the MIT-BIH database produced optimal predictive accuracy of (SVT, VF, VT) = (95.24%, 96.00%, 97.78%). A data decimation process is also introduced in the novel method to enhance the computational efficiency, resulting in a significant reduction in the time required for generating the feature values.

A robust sequential detection algorithm for cardiac arrhythmia classification

In ibid., vol. 37, no. 9, p. 837-43 (1990) and Proc. IEEE 9th Annu. Conf. Eng. Med. Biol. Soc., p. 918-19 (1988) N.V. Thakor et al. describe a sequential probability ratio test (SPRT) based on threshold crossing intervals (TCI) for the discrimination of ventricular fibrillation (VF) from ventricular tachycardia (VT). However, in applying their algorithm to data from the MIT-BIH malignant arrhythmia database, the authors observed some overlap in the distributions of TCI for VF and VT resulting in 16% overall error rate for the discrimination. In this communication, the authors describe a modified SPRT algorithm, using a new feature dubbed blanking variability (BV) as the basis for discrimination. Using the MIT-BIH database, the preliminary results showed that the proposed method decreases the overall error rate to 5%.

A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia

It has been reported that the sympathovagal balance (SB) can be quantified by heart rate (HR) via the low-frequency (LF) to high-frequency (HF) spectral power ratio LF/HF. In this paper, an investigation of the relationship between the autonomic nervous system (ANS) and non-sustained ventricular tachycardia (NSVT) is presented. A wavelet transform (WT)-based approach for short-time heart rate variability (HRV) assessments is proposed for this aspect of analysis. The study was conducted on an RR-interval database consisting of 87 NSVT, 61 ischemic and five normal episodes. First, instantaneous SB estimates were generated by the proposed method. Then, waveforms of the WT-based SB evolutions were quantitatively examined. Numerical results showed that while a majority of SB waveforms (about 71%) derived from the non-NSVT population (i.e., ischemic and normal) appeared to come near oscillating with certain fixed levels, approximate 75% of SB evolutions underwent significantly rapid increases prior to the onset of NSVT, suggesting that an abrupt sympathovagal imbalance might partly account for the occurrence of NSVT.

Cell proliferation on PVA/sodium alginate and PVA/poly(γ-glutamic acid) electrospun fiber

To overcome the obstacles of easy dissolution of PVA nanofibers without crosslinking treatment and the poor electrospinnability of the PVA cross-linked nanofibers via electrospinning process, the PVA based electrospun hydrogel nanofibers are prepared with post-crosslinking method. To expect the electrospun hydrogel fibers might be a promising scaffold for cell culture and tissue engineering applications, the evaluation of cell proliferation on the post-crosslinking electrospun fibers is conducted in this study. At beginning, poly(vinyl alcohol) (PVA), PVA/sodium alginate (PVASA) and PVA/poly(γ-glutamic acid) (PVAPGA) electrospun fibers were prepared by electrospinning method. The electrospun PVA, PVASA and PVAPGA nanofibers were treated with post-cross-linking method with glutaraldehyde (Glu) as crosslinking agent. These electrospun fibers were characterized with thermogravimetry analysis (TGA) and their morphologies were observed with a scanning electron microscope (SEM). To support the evaluation and explanation of cell growth on the fiber, the study of 3T3 mouse fibroblast cell growth on the surface of pure PVA, SA, and PGA thin films is conducted. The proliferation of 3T3 on the electrospun fiber surface of PVA, PVASA, and PVAPGA was evaluated by seeding 3T3 fibroblast cells on these crosslinked electrospun fibers. The cell viability on electrospun fibers was conducted with water-soluble tetrazolium salt-1 assay (Cell Proliferation Reagent WST-1). The morphology of the cells on the fibers was also observed with SEM. The results of WST-1 assay revealed that 3T3 cells cultured on different electrospun fibers had similar viability, and the cell viability increased with time for all electrospun fibers. From the morphology of the cells on electrospun fibers, it is found that 3T3 cells attached on all electrospun fiber after 1day seeded. Cell-cell communication was noticed on day 3 for all electrospun fibers. Extracellular matrix (ECM) productions were found and cell-ECM adhesion was shown on day 7. The cell number was also increased on all of the crosslinked electrospun fibers. It seems that the PVA based electrospun hydrogel nanofibers prepared with post-crosslinking method can be used as scaffold for tissue engineering.

Wavelet-based ECG compression by bit-field preserving and running length encoding

Efficient electrocardiogram (ECG) compression can reduce the payload of real-time ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In this paper an ECG compression/decompression architecture based on the bit-field preserving (BFP) and running length encoding (RLE)/decoding schemes incorporated with the discrete wavelet transform (DWT) is proposed. Compared to complex and repetitive manipulations in the set partitioning in hierarchical tree (SPIHT) coding and the vector quantization (VQ), the proposed algorithm has advantages of simple manipulations and a feedforward structure that would be suitable to implement on very-large-scale integrated circuits and general microcontrollers.

Complexity-Measure-Based Sequential Hypothesis Testing for Real-Time Detection of Lethal Cardiac Arrhythmias

A novel approach that employs a complexity-based sequential hypothesis testing (SHT) technique for real-time detection of ventricular fibrillation (VF) and ventricular tachycardia (VT) is presented. A dataset consisting of a number of VF and VT electrocardiogram (ECG) recordings drawn from the MIT-BIH database was adopted for such an analysis. It was split into two smaller datasets for algorithm training and testing, respectively. Each ECG recording was measured in a 10-second interval. For each recording, a number of overlapping windowed ECG data segments were obtained by shifting a 5-second window by a step of 1 second. During the windowing process, the complexity measure (CM) value was calculated for each windowed segment and the task of pattern recognition was then sequentially performed by the SHT procedure. A preliminary test conducted using the database produced optimal overall predictive accuracy of. The algorithm was also implemented on a commercial embedded DSP controller, permitting a hardware realization of real-time ventricular arrhythmia detection.

Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing.

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

A computational modeling and analysis in cell biological dynamics using electric cell-substrate impedance sensing (ECIS).

In this paper, a study of computational modeling and multi-scale analysis in cell dynamics is presented. Our study aims at: (1) deriving and validating a mathematical model for cell growth, and (2) quantitatively detecting and analyzing the biological interdependencies across multiple observational scales with a variety of time and frequency resolutions. This research was conducted using the time series data practically measured from a novel on-line cell monitoring technique, referred to as electric cell-substrate impedance sensing (ECIS), which allows continuously tracking the cellular behavior such as adhesion, proliferation, spreading and micromotion. First, comparing our ECIS-based cellular growth modeling analysis results with those determined by hematocytometer measurement using different time intervals, we found that the results obtained from both experimental methods consistently agreed. However, our study demonstrated that it is much easier and more convenient to operate with the ECIS system for on-line cellular growth monitoring. Secondly, for multi-scale analysis our results showed that the proposed wavelet-based methodology can effectively quantify the fluctuations associated with cell micromotions and quantitatively capture the biological interdependencies across multiple observational scales. Note that although the wavelet method is well known, its application into the ECIS time series analysis is novel and unprecedented in computational cell biology. Our analyses indicated that the proposed study on ECIS time series could provide a hopeful start and great potentials in both modeling and elucidating the complex mechanisms of cell biological systems.