HRV spectral estimation based on constrained Gaussian modeling in the nonstationary case

Daoud, Mohammed; Ravier, Philippe; Harba, Rachid; Jabloun, Meryem; Yagoubi, B.; Buttelli, Olivier

doi:10.1016/j.bspc.2013.04.007

Cited by 5 publications

(8 citation statements)

References 21 publications

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“…It is often used for beat segments extraction for the purpose of classification [4][5][6]. Also, it is required for calculating the R-R interval, which is used in heart-rate variability analysis [7,8]. QRS detection is not a simple peak-finding problem.…”

Section: Introductionmentioning

confidence: 99%

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Sharma

2016

IETE Journal of Research

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The QRS complex is the most distinctive feature in an electrocardiogram (ECG) signal. Therefore, its detection serves as the starting point for various applications, such as detection of other waves and segments, heart-rate calculation, derivation of respiration, etc. In this paper, a novel technique for QRS detection is proposed. The technique is based on the recently proposed synchrosqueezed wavelet transform (SSWT), which is obtained by application of a post-processing technique known as synchrosqueezing to the continuous wavelet transform. Following SSWT, various other processing steps are applied, including a nonlinear mapping technique, which is novel in the context of QRS detection, to finally detect the R-peaks. The proposed algorithm is evaluated on the MIT-BIH arrhythmia database and overall sensitivity, positive predictivity and error rate obtained are 99.92%, 99.93%, and 0.15%, respectively.

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Section: Introductionmentioning

confidence: 99%

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Sharma

2016

IETE Journal of Research

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“…It is assumed that the matrix consists of n r spectra that are derived from the HRV data, where is a vector of the spectrum. Given the relationship between the LF and HF components of the spectrum [18], we assume the LF part can be used as a reference to estimate uncertainties in the HF part, or the HF can be used to estimate the LF part. Specifically, for each s i ∈ S , it can be modelled with Gaussian functions [18], and the entry s i,ℓ can be represented as follows, where f ∈ [ f l,i , f r,i ] indicates the interval of frequency band of interests, i.e., LF or HF spectrum.…”

Section: Methodsmentioning

confidence: 99%

“…The above approximation of missing entries is data-driven and implemented using an iteration process. We note that for the analysis of HRV data, some mathematical techniques can be used to model the spectrum, such as the Gaussian model as pointed out in [18], the model can be used to characterise HRV spectra and investigate the relationship between them. In the next subsection, we will use the Gaussian model to develop a refined matrix completion (RMC) by generating a new matrix with a much lower dimension.…”

Section: B Matrix Approximation With Interested Zonementioning

confidence: 99%

“…Given the relationship between the LF and HF components of the spectrum [18], we assume the LF part can be used as a reference to estimate uncertainties in the HF part, or the HF can be used to estimate the LF part. Specifically, for each s i ∈ S, it can be modelled with Gaussian functions [18], and the entry s i,ℓ can be represented as follows,…”

Section: Model-based Refined MC For Hrv Spectrum Estimationmentioning

confidence: 99%

“…To this end, many computational techniques have been developed to address these challenges in the analysis of HRV data. For example, the Gaussian model was used to estimate frequency components from noisy HRV data [18]; ensemble machine learning was used to explore linear and non-linear correlations between HRV features derived from ECG data [19]; and a hybrid deep learning model was developed to combine different HRV features and produced reliable classification performance [20]. Nevertheless, machine learning methods, and in particular deep neural networks, typically require expertise in model development and hyperparameters tuning.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Refined Matrix Completion for Spectrum Estimation of Heart Rate Variability

Zhu

Tan

et al. 2022

Preprint

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Heart rate variability (HRV) is the reflection of physiological effects modulating heart rhythm. In particular, spectral HRV metrics provide valuable information to investigate activities of the cardiac autonomic nervous system. However, uncertainties and artifacts from measurements can reduce signal quality and therefore affect the evaluation of HRV measures. In this paper, we propose a new method for HRV spectrum estimation with measurement uncertainties using matrix completion (MC). We show that missing values of HRV spectrum can be efficiently estimated using the MC method by leveraging the low rank property of the spectrum matrix. In addition, we proposed a refined matrix completion (RMC) method to improve the estimation accuracy and computational efficiency by introducing model information for the HRV spectrum. Experimental studies on five public benchmark datasets show the effectiveness and robustness of the developed RMC method for estimating missing entries for HRV spectrum with different masking ratios. Furthermore, our developed RMC method is compared with five deep learning models and the traditional MC method; the results of this comparison study demonstrate that our developed RMC method obtains the least estimation error with the minimal computation cost, indicating the advantages of our developed method for HRV spectrum estimation.

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Use of cardiorespiratory coherence to separate spectral bands of the heart rate variability

Daoud

Ravier

Buttelli

2018

Biomedical Signal Processing and Control

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HRV spectral estimation based on constrained Gaussian modeling in the nonstationary case

Cited by 5 publications

References 21 publications

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Refined Matrix Completion for Spectrum Estimation of Heart Rate Variability

Use of cardiorespiratory coherence to separate spectral bands of the heart rate variability

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