Continuous wavelet transform based processing for estimating the power spectrum content of heart rate variability during hemodiafiltration

Cartas-Rosado, Raúl; Becerra-Luna, Brayans; Martínez-Memije, Raúl; Infante, Oscar; Lerma, Claudia; Pérez-Grovas, Héctor; Rodríguez-Chagolla, José Manuel

doi:10.1016/j.bspc.2020.102031

Cited by 18 publications

(26 citation statements)

References 42 publications

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“…The estimation of BRS is considered to be valuable for the clinical prognosis and diagnosis of various cardiovascular conditions, including heart failure and myocardial infarction [10]. It has been confirmed that BRS is a more reliable marker for the evaluation of functional status during the performance of physical and psychological tasks [11] compared to commonly used indices such as heart rate variability (HRV) [12], cardiac output (CO) [13] and blood pressure variability (BPV) [14]. BPV is a phenomenon characterizing the changes in blood pressure levels.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter

Zida

Harfiya

et al. 2022

Sensors

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The evaluation of baroreflex sensitivity (BRS) has proven to be critical for medical applications. The use of α indices by spectral methods has been the most popular approach to BRS estimation. Recently, an algorithm termed Gaussian average filtering decomposition (GAFD) has been proposed to serve the same purpose. GAFD adopts a three-layer tree structure similar to wavelet decomposition but is only constructed by Gaussian windows in different cutoff frequency. Its computation is more efficient than that of conventional spectral methods, and there is no need to specify any parameter. This research presents a novel approach, referred to as modulated Gaussian filter (modGauss) for BRS estimation. It has a more simplified structure than GAFD using only two bandpass filters of dedicated passbands, so that the three-level structure in GAFD is avoided. This strategy makes modGauss more efficient than GAFD in computation, while the advantages of GAFD are preserved. Both GAFD and modGauss are conducted extensively in the time domain, yet can achieve similar results to conventional spectral methods. In computational simulations, the EuroBavar dataset was used to assess the performance of the novel algorithm. The BRS values were calculated by four other methods (three spectral approaches and GAFD) for performance comparison. From a comparison using the Wilcoxon rank sum test, it was found that there was no statistically significant dissimilarity; instead, very good agreement using the intraclass correlation coefficient (ICC) was observed. The modGauss algorithm was also found to be the fastest in computation time and suitable for the long-term estimation of BRS. The novel algorithm, as described in this report, can be applied in medical equipment for real-time estimation of BRS in clinical settings.

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Section: Introductionmentioning

confidence: 99%

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter

Zida

Harfiya

et al. 2022

Sensors

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“…Several parametric Bayesian methods are useful for the parameter estimation of Gaussian and non-Gaussian systems in which states are Markov process. Different parametric Bayesian estimation methods are available in the literature [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] are described in Table 1 . For the non-Gaussian systems, Bayesian computation of conditional probabilities has been used for updating the weights involved in the state estimation.…”

Section: Introductionmentioning

confidence: 99%

“…EKF gives the joint evaluation of conditional mean and conditional error covariance; therefore, it provides better estimates than the conventional SEIR model. Recently, the wavelet transform (WT) method has been used in state and parameter estimation of various systems [21] [27] , [28] , [29] . In this method, different minimum and maximum frequencies are used for each time slot.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic filtering based transmissibility estimation of novel coronavirus

Bansal

Kumar

Singh

et al. 2021

Digital Signal Processing

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In this study, the transmissibility estimation of novel coronavirus (COVID-19) has been presented using the generalized fractional-order calculus (FOC) based extended Kalman filter (EKF) and wavelet transform (WT) methods. Initially, the state-space representation for the bats-hosts-reservoir-people (BHRP) model is obtained using a set of fractional order differential equations for the susceptible-exposed-infectious-recovered (SEIR) model. Afterward, the EKF and Kronecker product based WT methods have been applied to the discrete vector representation of the BHRP model. The main advantage of using EKF in this system is that it considers both the process and the measurement noise, which gives better accuracy and probable states, which are the Markovian (processes). The importance of proposed models lies in the fact that these models can accommodate conventional EKF and WT methods as their special cases. Further, we have compared the estimated number of contagious people and recovered people with the actual number of infectious people and recovered people in India and China.

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“…Previous studies have shown that using 10 min epochs improves the estimation of the VLF spectral power [21,35]. Here, we applied the STFT using this modification in the analysis epoch to study the changes in the power spectral distribution along the intradialysis time, comparing hypotensive and non-hypotensive patients.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Assessment Using Time–Frequency Analysis in Hypotensive and Non-Hypotensive Patients in Hemodialysis

et al. 2020

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Intradialytic hypotension occurs in 10–30% of hemodialysis (HD) sessions. This phenomenon affects the cardiovascular system’s functions, which are reflected in the activity of the autonomic nervous system (ANS). To indirectly assess the ANS during HD, we analyzed the mean R–R intervals and the spectral power of heart rate variability (HRV) from 20 end-stage renal disease patients divided into hypotensive and non-hypotensive groups. The spectrotemporal analysis was accomplished using short-time Fourier transform with 10 min epochs of HRV overlapping by 40%. The spectral power was divided into three segments according to high frequency, low frequency, and very low frequency bandwidths and averaged to fit quadratic regression models. The analysis of the mean R–R intervals showed significant differences between the groups (p = 0.029). The power variation over time was significant in each spectral band (p ≪ 0.05). The average power, maximum power, and time when the peak was reached differed for each band and between groups, showing the ability to correctly identify the decompensation of the ANS and discriminate between hypotensive and non-hypotensive patients. Additionally, the changes in the sympathovagal ratio were not significant and very scattered for the hypotensive group (p = 0.23) compared to the non-hypotensive group, where the changes were significant (p ≪ 0.05) and much less scattered.

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Continuous wavelet transform based processing for estimating the power spectrum content of heart rate variability during hemodiafiltration

Cited by 18 publications

References 42 publications

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter

Stochastic filtering based transmissibility estimation of novel coronavirus

Heart Rate Variability Assessment Using Time–Frequency Analysis in Hypotensive and Non-Hypotensive Patients in Hemodialysis

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