Estimation of the power spectral density in nonstationary cardiovascular time series: assessing the role of the time-frequency representations (TFR)

Pola, S.; Macerata, A.; Emdin, Michele; Marchesi, Carlo

doi:10.1109/10.477700

Cited by 119 publications

(64 citation statements)

References 18 publications

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“…during changes in autonomic modulation, do not have a major effect. TFRD can be presented as a three-dimensional graph (Figure 3), in which the x-axis is the time axis, the yaxis is the frequency axis, and the z-axis is the power axis (Oppenheim & Schafer, 1999;Pola et al 1996;Saalasti, 2003).…”

Section: Time-frequency Analysismentioning

confidence: 99%

Heart rate variability is related to training load variables in interval running exercises

Kaikkonen¹,

Hynynen

Mann

et al. 2011

Eur J Appl Physiol

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Section: Time-frequency Analysismentioning

confidence: 99%

Heart rate variability is related to training load variables in interval running exercises

Kaikkonen¹,

Hynynen

Mann

et al. 2011

Eur J Appl Physiol

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“…-4 , an order of magnitude smaller. This means that the Q values of different MCG QRS complexes are more time-variable than ECG, and the MCG is more sensitive to change in the cardiac system electromagnetic field states than ECG and indicates that the electromagnetic field of the living matter in human myocardial tissue is time-dependent [5,6].…”

Section: Time and Spatial Differences In Time-frequency Domain Characmentioning

confidence: 99%

“…Electrocardiography (ECG) and magnetocardiography (MCG), which are the carriers of electromagnetic information about the human cardiac system, can be acquired by non-invasive detection devices [1][2][3][4]. ECG and MCG signals, which are both NSS [5], have similar shapes. They both consist of a P-wave (atrial depolarization), QRS complex (ventricular depolarization) and T-wave (ventricular repolarization): the S-T segment reflects the repolarization status of myocardial cells.…”

mentioning

confidence: 99%

Characteristics of magnetocardiography and electrocardiography in the time-frequency domain

et al. 2011

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As the carriers of electromagnetic information about the cardiac system, both magnetocardiography (MCG) and electrocardiography (ECG) are non-stationary signals (NSS). The essential and crucial features of NSS are the local time-frequency characteristics (LTFCH). Under the constraint of the uncertainty principle in the time-frequency domain (TFD), the LTFCH of those non-stationary signals can be determined by evolutionary spectrum analysis (ESA). In this paper, by analyzing the QRS complexes of both MCG and ECG data from healthy volunteers, we have derived some characteristic parameters (CHPs) in the TFD, such as the quality factor (Q) and center frequency (f z ). According to the experiment results, we believe that there are threshold ranges of Q and f z values in the dynamic behavior of the cardiac system for healthy projects. If the CHPs are in the proper ranges, the cardiac system is in a normal condition and homeostasis can be self-correcting. In contrast, if the values of the CHPs are less than the proper ranges, the homeostasis of the cardiac system is lost and some cardiac diseases may result. The assumption of the threshold ranges is verified by sample data from different cardiac diseases, and the results support the assumption. The CHPs are not only a kind of new reference criterion for ECG in clinical diagnosis, but also afford a technological route for the application of MCGs in cardiology. The heart is the dynamic organ of the blood circulation system. The transmembrane potential changes during myocardial cells' vital processes are a biological electromagnetic signal source within living matter. Electrocardiography (ECG) and magnetocardiography (MCG), which are the carriers of electromagnetic information about the human cardiac system, can be acquired by non-invasive detection devices [1][2][3][4]. ECG and MCG signals, which are both NSS [5], have similar shapes. They both consist of a P-wave (atrial depolarization), QRS complex (ventricular depolarization) and T-wave (ventricular repolarization): the S-T segment reflects the repolarization status of myocardial cells. Transient information contained in the QRS complex *Corresponding author (email: xinyuanliu@pku.edu.cn)is extremely important for identifying changes of electromagnetic status in the cardiac system, and it can also provide clues about some cardiovascular diseases. Theoretically, in the TFD the frequency spectrum of a NSS is time-dependent, which means that traditional Fourier transform and the common wavelet transform methods are not suitable for analyzing LTFCH of those signals [6]. The evolutionary spectrum and the corresponding methods are used for detecting time-frequency domain features of NSS [7,8]. We have analyzed the features of the evolutionary spectrum from the QRS complex of healthy subjects' MCG and ECG signals, and then obtained the time-frequency domain characteristic parameters, that is the quality factor Q and the center frequency f z . The results show that the MCG and ECG are both information carriers of cardiac elec...

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“…In the SPWVD, a trade-off is required between the time-frequency resolution and the removal of the cross terms: smoothing in time by g M and/or in frequency by h N reduces the time resolution and/or the frequency resolution (Pola et al 1996). Even if the marginal properties are no longer strictly valid and the time resolution reduced (depending on the duration of g M ), the SPWVD can be used to estimate both the instantaneous power (IPow) and frequency (IF) of the signal from the first two order moments of the distribution The instantaneous power (IPow) can be converted into the instantaneous amplitude as follows:…”

Section: (B) Data Analysis: Signal Processingmentioning

confidence: 99%

“…To follow instantaneous changes in spectral activities, we selected as the timefrequency method the SPWVD, which is based on the Wigner-Ville distribution (Cohen 1989;Pola et al 1996). The Wigner-Ville distribution is always realvalued, with a very good time and frequency resolution preserving time and frequency shifts.…”

Section: (B) Data Analysis: Signal Processingmentioning

confidence: 99%

Endogenous circannual rhythm in luteinizing hormone secretion: insight from signal analysis coupled with mathematical modelling

Vidal

Médigue

Malpaux

et al. 2009

Phil. Trans. R. Soc. A.

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In sheep, as in many vertebrates, the seasonal pattern of reproduction is timed by the annual photoperiodic cycle, characterized by seasonal changes in the day length. The photoperiodic information is translated into a circadian profile of melatonin secretion. After multiple neuronal relays (within the hypothalamus), melatonin affects gonadotrophin-releasing hormone (GnRH) secretion, which in turn controls ovarian cyclicity. The pattern of GnRH secretion is mirrored by that of luteinizing hormone (LH) secretion, whose plasmatic level can be easily measured. We addressed the question of whether there exists an endogenous circannual rhythm in a tropical sheep (Blackbelly) population that exhibits clear seasonal ovarian activity when ewes are subject to temperate latitudes. We based our analysis on LH time series collected in the course of 3 years from ewes subject to a constant photoperiodic regime. Owing to intra-and interanimal variability and unequal sampling times, the existence of an endogenous rhythm is not straightforward. We have used time-frequency signal processing methods, and especially the smooth pseudo-Wigner-Ville distribution, to extract possible hidden rhythms from the data. To further investigate the low-frequency (LF) and high-frequency (HF) components of the signals, we have designed a simple mathematical model of the LH plasmatic level accounting for the effect of experimental sampling times. The model enables us to (i) confirm the existence of an endogenous circannual rhythm as detected by the LF signal component, (ii) investigate the action mechanism of the photoperiod on the pulsatile pattern of LH secretion (control of the interpulse interval), and (iii) conclude that the HF component is mainly due to the experimental sampling protocol.

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Estimation of the power spectral density in nonstationary cardiovascular time series: assessing the role of the time-frequency representations (TFR)

Cited by 119 publications

References 18 publications

Heart rate variability is related to training load variables in interval running exercises

Heart rate variability is related to training load variables in interval running exercises

Characteristics of magnetocardiography and electrocardiography in the time-frequency domain

Endogenous circannual rhythm in luteinizing hormone secretion: insight from signal analysis coupled with mathematical modelling

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