Bioelectric signal detrending using smoothness prior approach

Zhang, Fan; Chen, Shixiong; Zhang, Haoshi; Zhang, Xiufeng; Li, Guanglin

doi:10.1016/j.medengphy.2014.05.009

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…The LF baseline trend was removed using a smoothness prior method. The following time domain parameters for HRV signal were calculated for each patient in the three groups using MATLAB Software (MathWorks Inc., Natick, MA, USA): SDNN, the standard deviation of all normal RR intervals; RMSSD, the square root of the mean of the squared successive differences in R-R intervals; and pNN50, relative number of intervals differing more than 50 ms.[ 16 ] SDNN reflects the overall HRV and correlates with the total power from frequency domain analysis. [ 2 ] The RMSSD and pNN50 are estimates of short-term components of HRV and correlates with the HF component of frequency domain analysis.…”

Section: Methodsmentioning

confidence: 99%

Cardio-autonomic functions and sleep indices before and after coronary artery bypass surgery

et al. 2018

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BACKGROUND:Earlier studies showed a short-term impairment of cardio-autonomic functions following coronary artery bypass grafting (CABG). There is a lack of consistency in the time of recovery from this impairment. Studies have attributed the post-CABG atrial fibrillation to preexisting obstructive sleep apnea (OSA) without an objective sleep assessment. The aim of this study was to evaluate the effect of CABG on cardio-autonomic and hemodynamic functions and on OSA indices in patients with ischemic heart disease (IHD).METHODS:Cardio-autonomic function using heart rate variability indices, hemodynamic parameters, and sleep studies were performed in 26 patients with stable IHD before, on day-6, and day-30 post-CABG surgery.RESULTS:The high-frequency powers of normalized R-R intervals significantly (P = 0.002) increased from the preoperative value of 46.09 to 66.52 on day-6 and remained unchanged on day-30 postsurgery. In contrary, the low-frequency powers of normalized R-R interval decreased from 53.91 to 33.48 during the same period (P = 0.002) and remained unchanged on day 30 postsurgery. Baroreceptor sensitivity, obstructive and central apnea indices, desaturation index, and lowest O2 saturation were not significantly different between preoperative, day-6, and day-30 postsurgery.CONCLUSION:Our study revealed that recovery of autonomic functions following CABG occurs as early as 30 days of postsurgery. CABG does not seem to have short-term effects on sleep study indices. However, long-term effects need further evaluation.

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Section: Methodsmentioning

confidence: 99%

Cardio-autonomic functions and sleep indices before and after coronary artery bypass surgery

et al. 2018

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“…For instance, increased respiratory rates and shortened Q-TE durations are known to be correlated to physical strain [5]. In a preprocessing step, baseline removal was applied and the 50 Hz powerline interference was removed [6]. The signal was then subdivided into windows of 30 s length and an overlap of 29 s between adjacent windows.…”

Section: B Experimental Studymentioning

confidence: 99%

Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study

Schrumpf

Bausch

Stürm

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This paper introduces a new unsupervised method for the clustering of physiological data into health states based on their similarity. We propose an iterative hierarchical clustering approach that combines health states according to a similarity constraint to new arbitrary health states. We applied our method to experimental data in which the physical strain of subjects was systematically varied. We derived health states based on parameters extracted from ECG data. The occurrence of health states shows a high temporal correlation to the experimental phases of the physical exercise. We compared our method to other clustering algorithms and found a significantly higher accuracy with respect to the identification of health states.

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“…Smoothness priors has a broad range of applications in time series analysis [1,2,3,4], global stereo reconstruction [5], edge-preserving image smoothing [6], image restoration [7], transfer function estimation [8], smoothing noisy data and signal detrending [9,10,11,12], smoothing of discontinuous signals [13], spectral estimation [14], parametric time warping [15] and spline smoothing [16,17,18,19,20,21,22,23]. It is closely linked to the ill-posed inverse problems and problems of statistical Tikhonov regularization [10,24,25,26]. Although the notation of "smoothness priors" was first introduced by Shiller [27], its conceptual predecessor can be seen in the problem of estimating a smooth trend embedded in white noise addressed by Whittaker in 1923 [28] which was known as the method of graduating data [29,30,31,32,33].…”

Section: Introductionmentioning

confidence: 99%

Improved smoothness priors using bilinear transform

Roonizi

Jutten

2020

Signal Processing

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Smoothness priors is a well-known and most commonly used method in the analysis of stochastic processes making it very useful in the field of stochastic signal processing. It is particularly suited for smoothing the noisy data and detrending the time-series signals. The method is based on an optimization problem where the n-th order derivative of the signal enters as a constraint. When the method is designed in discrete time domain, the backward difference rule is used to perform differential-to-difference conversion. Moreover, the solution depends on a smoothness trade-off parameter. An efficient algorithm for the trade-off parameter selection remains an important and challenging issue. In this paper, first, we propose a closed-form expression for the tradeoff parameter. The closed-form expression resulted from a frequency domain interpretation of the smoothness priors procedure. The trade-off parameter determines the amount of frequency components that the procedure allows to pass. We show that the trade-off parameter is related to the arbitrary choice of cutoff frequency. Second, we introduce a new way to the design and implementation of smoothness priors using bilinear transformation method. Frequency analysis and experiments on both synthetic and real world signals with different levels of noise demonstrate that bilinear transform is indeed more effective for smoothness priors implementation when compared with the traditional ones, i.e., the backward difference rule.

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Bioelectric signal detrending using smoothness prior approach

Cited by 18 publications

References 12 publications

Cardio-autonomic functions and sleep indices before and after coronary artery bypass surgery

Cardio-autonomic functions and sleep indices before and after coronary artery bypass surgery

Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study

Improved smoothness priors using bilinear transform

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