Comparing stress ECG enhancement algorithms

Afonso, V.X.; Tompkins, W.J.; Nguyen, Truong Q.; Michler, K.; Luo, Shen

doi:10.1109/51.499756

Cited by 93 publications

(26 citation statements)

References 9 publications

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“…The global method of outliers reduction may be used to identify abnormalities (nondominant signal cycles) in a biomedical signal-for example, in cardiac monitoring. It must also be stated that local methods of outliers reduction are known from literature [13], [14]. Median and alpha-trimmed means are good examples.…”

mentioning

confidence: 99%

Assessing Blood Flow Control Through a Bootstrap Method

Simpson

Panerai

Ramos

et al. 2004

IEEE Trans. Biomed. Eng.

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Abstract-In order to assess blood flow control, the relationship between blood pressure and blood flow can be modeled by linear filters. We present a bootstrap method, which allows the statistical analysis of an index of blood flow control that is obtained from constrained system identification using an established set of pre-defined filters.Index Terms-Bootstrap method, cerebral blood flow control, system identification.

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mentioning

confidence: 99%

Assessing Blood Flow Control Through a Bootstrap Method

Simpson

Panerai

Ramos

et al. 2004

IEEE Trans. Biomed. Eng.

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“…First, we decomposed the simulated noisy signal into high-frequency components, called the detailed signals, and low-frequency components, called the approximated signals. Motion artifacts are predominantly found at higher frequencies, caused by increased muscle activity and mechanical forces acting on the electrodes (AFONSO et al, 1996). Among several frequency components decomposed by the wavelet transform, most of the motion artifacts are included in the high-frequency components, such as D1, De and D3 that are the detailed signals at the first, second and third levels.…”

Section: Wavelet Interpolation Filtermentioning

confidence: 99%

“…There are many methods for de-noising, such as averaging (mean and median) techniques, singular value decomposition (SVD), wavelet transform (or wavelet packet) and filter bank (EVANICH et at., 1972;ACAR and KOYMEN, 1999;TIKKANEN, 1999;AFONSO et al, 1996). The mean technique is frequently used for denoising, but it removes not only the unwanted noise but also the original high-frequency components of the stress ECG.…”

Section: Introductionmentioning

confidence: 97%

Design of a wavelet interpolation filter for enhancement of the ST-segment

Park

Khil

Lee

et al. 2001

Med. Biol. Eng. Comput.

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A wavelet interpolation filter (WIF) is designed for the removal of motion artifacts in the ST-segment of stress ECGs. The WIF consists of two parts. One part is a wavelet transform that decomposes the stress ECG signal into several frequency bands using a Haar wavelet. The other part is an interpolation method, such as the spline technique, that is used to enhance the reconstruction performance of the signal decomposed by the wavelet transform. To evaluate the performance of the WIF, three indices are used: signal-to-noise ratio (SNR), reconstruction square error (RSE) and standard deviation (SD). The MIT/BIH arrhythmia database, the European ST-T database and the triangular wave are used for evaluation. A noisy ECG signal, corrupted by motion artifacts, is simulated by the addition of two types of random noise to the original ECG signal. For comparison, three indices for the other methods are also computed: mean, median and hard thresholding. The performance of the WIF shows that RSE, SNR and SD are 392.7, 18.3dB and 2.6, respectively, in the case of a noisy signal with an SNR of 7.1 dB. This result is much better than those for the other methods.

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“…To date, phase modulation, differential signal detection, or rotation angle optimization can be employed for weak coupling measurement [7−9] ; however, the complexity of the required system has also increased. In order to increase the detection sensitivity, some signal analysis methods, such as band-pass filtering [10] and the wavelet transforms [11] , have been reported for noise reduction. The main drawback, however, is that the base functions are fixed, and no such function has been proposed to correspond to the features of the acquired signals.…”

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confidence: 99%

Sensitivity enhancement of distributed polarization coupling detection in Hi-Bi fibers

Zhang¹,

Ye²,

Jia³

et al. 2012

中国光学快报

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The intensity and position of the coupling points in high birefringence (Hi-Bi) fibers can be detected effectively using distributed polarization coupling (DPC) detection. The detection sensitivity can decrease due to mechanical vibration disturbance and environment noise. Thus, a method based on empirical mode decomposition is proposed to detect weak mode coupling points. The simulation and experimental results illustrate that the proposed method can suppress the noise effectively and improve sensitivity significantly. The method can identify coupling points as weak as -60 dB embedded in noise automatically and effectively. The algorithm is applicable for DPC, and the experimental sensitivity is improved by 10 dB.OCIS codes: 060.2420060. , 070.2025060. , 060.2300 High birefringence (Hi-Bi) fibers are used in many areas related to fiber optics, including coherent optical communications, integrated-optic devices, and optical sensors based on interferometric techniques. Distributed polarization coupling (DPC) detection employs white-light interferometry (WLI) in Hi-Bi fibers, and is widely used in the measurements of strain, twist, temperature, and many other physical parameters [1−3] . Due to high spatial resolution and wide dynamic range brought about by the adoption of WLI [4] , the intensity and position of the coupling points can be detected effectively.Mechanical vibration disturbance and environment noise can lead to the degradation of detection sensitivity of DPC [5,6] . Therefore, the weak coupling points are submerged in noise, leading to a wrong diagnosis. To date, phase modulation, differential signal detection, or rotation angle optimization can be employed for weak coupling measurement [7−9] ; however, the complexity of the required system has also increased. In order to increase the detection sensitivity, some signal analysis methods, such as band-pass filtering [10] and the wavelet transforms [11] , have been reported for noise reduction. The main drawback, however, is that the base functions are fixed, and no such function has been proposed to correspond to the features of the acquired signals. In comparison, the empirical mode decomposition (EMD) is a highly efficient technique for processing nonlinear and non-stationary signals, because the procedure is datadriven, adaptive, and not restricted by linearity or priori conception [12] . Gong et al. applied EMD and the threshold method to reduce the noise in the lidar signals [13] . Deng et al. used EMD and local entropy to detect small targets [14] . Here EMD combined with DPC is proposed to enhance the detection sensitivity and realize the distributed weak mode coupling measurement.The scheme of DPC detection is shown in Fig. 1. A superluminescent diode (SLD) emitting at 1 328 nm was used as the light source. An in-line polarizer was fusion spliced in front of the Hi-Bi fiber. Its polarization orientation was aligned to the slow axis of the polarization maintaining fiber (PMF), so that only the slow axis was excited. The output light from the fi...

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Comparing stress ECG enhancement algorithms

Cited by 93 publications

References 9 publications

Assessing Blood Flow Control Through a Bootstrap Method

Assessing Blood Flow Control Through a Bootstrap Method

Design of a wavelet interpolation filter for enhancement of the ST-segment

Sensitivity enhancement of distributed polarization coupling detection in Hi-Bi fibers

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