Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals

Khan, Nabeel Ali; Jönsson, Peter; Sandsten, Maria

doi:10.3390/app7030221

Cited by 17 publications

(7 citation statements)

References 45 publications

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“…Mathematical models based on exponential fitting or, more recent dynamical systems models based on a system of coupled ordinary differential equations, have been proposed for the numerical simulation and prediction of the HR response to exercise [9,10]. Several methods may be found in the literature for the estimation of the parameters involved in the HR variability signals [11][12][13]. A review of the most popular techniques for the time-frequency representation of the HR variability signals is presented in [11], whereas Khan et al [12] compare the performance of five kernel-based timefrequency distributions in terms of their ability to estimate the instantaneous frequency of HR signals.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods may be found in the literature for the estimation of the parameters involved in the HR variability signals [11][12][13]. A review of the most popular techniques for the time-frequency representation of the HR variability signals is presented in [11], whereas Khan et al [12] compare the performance of five kernel-based timefrequency distributions in terms of their ability to estimate the instantaneous frequency of HR signals.…”

Section: Introductionmentioning

confidence: 99%

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A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

Zakynthinaki

Kapetanakis

Lampou

et al. 2021

Signals

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Estimating the heart rate (HR) response to exercises of a given intensity without the need of direct measurement is an open problem of great interest. We propose here a model that can estimate the heart rate response to exercise of constant intensity and its subsequent recovery, based on soft computing techniques. Multilayer perceptron artificial neural networks (NN) are implemented and trained using raw HR time series data. Our model’s input and output are the beat-to-beat time intervals and the HR values, respectively. The numerical results are very encouraging, as they indicate a mean relative square error of the estimated HR values of the order of 10−4 and an absolute error as low as 1.19 beats per minute, on average. Our model has also been proven to be superior when compared with existing mathematical models that predict HR values by numerical simulation. Our study concludes that our NN model can efficiently predict the HR response to any constant exercise intensity, a fact that can have many important applications, not only in the area of medicine and cardio-vascular health, but also in the areas of rehabilitation, general fitness, and competitive sport.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

Zakynthinaki

Kapetanakis

Lampou

et al. 2021

Signals

View full text Add to dashboard Cite

show abstract

“…Therefore, in transient conditions, with non-constant speed, the positions of the fault harmonics are no longer constant, which blurs their characteristic signatures (smearing effect) and hinders the application of steady state fault diagnostic techniques. In fact, as the frequency of these harmonics change with time, what Equations ( 1 ), ( 2 ), ( 3 ), and ( 4 ) provide is their instantaneous frequency (IF) [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Puche-Panadero

Martínez-Román

Sapena-Bañó

et al. 2020

Sensors

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Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms—as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform—which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

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“…Respiration based frequency bands are also considered in the analysis of HRV in relation to emotion recognition [9], where the respiration frequency is extracted and used to define a subject-based time-varying frequency band. These studies justify the increased interest in the estimation of the respiratory frequency from the HRV signal when the respiratory information is not present [17]. In [18], the HRV is decomposed into a component that is correlated with the respiratory frequency and one residual component: the latter is found to have more discrimination power than traditional HRV analysis to monitor mental stress.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV

2019

Self Cite

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In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A non-stationary stochastic model, belonging to the class of Locally Stationary Chirp Processes, is used to model the task-related HRV data, and its parameters are estimated with a novel inference method. The corresponding optimal mean-square error (MSE) time-frequency spectrum is derived and evaluated both with the individually estimated model parameters and the common process parameters. The results from the optimal spectrum are compared to the standard spectrogram with different window lengths and the Wigner-Ville spectrum, showing that the MSE optimal spectral estimator may be preferable to the other spectral estimates because of its optimal bias and variance properties. The estimated model parameters are considered as response variables in a regression analysis involving several physiological factors describing the test participants’ state of health, finding a correlation with gender, age, stress, and fitness. The proposed novel approach consisting of measuring HRV during a chirp-breathing task, a corresponding time-varying stochastic model, inference method, and optimal spectral estimator gives a complete framework for the study of task-related HRV in relation to factors describing both mental and physical health and may highlight otherwise overlooked correlations. This approach may be applied in general for the analysis of non-stationary data and especially in the case of task-related HRV, and it may be useful to search for physiological factors that determine individual differences.

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Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals

Cited by 17 publications

References 45 publications

A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV

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