Singularity detection of the rabbit electrocardiogram: An evolutionary spectral method

Liu, Xinyuan; He, Dingwu; Pei, Liuqing; Zhang, Suming

doi:10.1016/j.pnsc.2009.01.009

Cited by 4 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detailed ESA methods and corresponding algorithms can be found in [7,8]. The traditional Fourier transform describes the global frequency characteristics of a signal in the time domain.…”

Section: Evolutionary Spectrum and Time-frequency Domain Characteristmentioning

confidence: 99%

“…As the transform scale is not the real frequency, the time and frequency resolutions of a wavelet transform vary at different points in time-frequency space [14]. The evolutionary spectrum is a time-dependent spectrum with two parameters of time and frequency, and which has the same physical interpretation as the conventional spectrum of a stationary process, in that it can describe the distribution characteristics of the power spectrum density of the signals over frequency [7,8]. The essential and crucial features of the NSS are the LTFCH in the TFD.…”

Section: Evolutionary Spectrum and Time-frequency Domain Characteristmentioning

confidence: 99%

“…Figure 1(b) is the 3D evolutionary spectrum (3DES) computed from the QRS complex sample data with only one peak [8] (Figure 1(a)). The internal relationships among the time, frequency, and power density distributions of the QRS complex in a cardiac cycle are revealed by Figure 1(b), which contains the instantaneous electromagnetic information about the cardiac system in the neighborhood of a time instant corresponding to the R-wave.…”

Section: Evolutionary Spectrum and Time-frequency Domain Characteristmentioning

confidence: 99%

“…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 .…”

mentioning

confidence: 99%

See 3 more Smart Citations

Characteristics of magnetocardiography and electrocardiography in the time-frequency domain

et al. 2011

Self Cite

View full text Add to dashboard Cite

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...

show abstract

“…Detailed ESA methods and corresponding algorithms can be found in [7,8]. The traditional Fourier transform describes the global frequency characteristics of a signal in the time domain.…”

Section: Evolutionary Spectrum and Time-frequency Domain Characteristmentioning

confidence: 99%

Section: Evolutionary Spectrum and Time-frequency Domain Characteristmentioning

confidence: 99%

Section: Evolutionary Spectrum and Time-frequency Domain Characteristmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Characteristics of magnetocardiography and electrocardiography in the time-frequency domain

et al. 2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…Currently singularity detection of industry signals depends on transform [7,8] and instantaneous frequency calculation [9]. Related problems are discussed as well [10,11].…”

Section: Introductionmentioning

confidence: 99%

Ignition Coil Insulation Fault Detection Algorithm

Li¹,

Zhao²,

Wang³

2010

JDCTA

View full text Add to dashboard Cite

Pulse singularities with variable amplitudes appear in the corresponding positions of ignition coil primary current waveform when insulation fault occurs during the ignition process. We propose an algorithm to detect the singularities in ignition coil primary current signal. First calculate the multiscale continuous wavelet transform of the fault signal. Then pursue modulus maxima from the coarsest scale to the finest scale along the links between the adjacent scales. The pursue method avoids that the distribution characterization of modulus maxima on the modulus maxima map affects the detection outcome. Last draw the modulus maxima lines and determine the locations of the singularities. The results of experiments show that it has approximate computational complexity with the traditional modulus maxima singularity detection algorithm. In addition the algorithm has less error than traditional algorithm and some more complicated algorithms on identifying the singularities' locations in a noisy environment.

show abstract

Characteristic parameters of electromagnetic signals from a human heart system

刘新元¹,

裴留庆²,

王寅³

et al. 2011

Chinese Phys. B

View full text Add to dashboard Cite

The electromagnetic field of a human heart system is a bioelectromagnetic field. Electrocardiography (ECG) and magnetocardiography (MCG) are both carriers of electromagnetic information about the cardiac system, and they are nonstationary signals. In this study, ECG and MCG data from healthy subjects are acquired; the MCG data are captured using a high-Tc radio frequency superconducting quantum interference device (HTc rf SQUIDs) and the QRS complexes in these data are analysed by the evolutionary spectrum analysis method. The results show that the quality factor Q and the central frequency fz of the QRS complex evolutionary spectrum are the characteristic parameters (CHPs) of ECG and MCG in the time-frequency domain. The confidence intervals of the mean values of the CHPs are estimated by the Student t distribution method in mathematical statistics. We believe that there are threshold ranges of the mean values of Q and fz for healthy subjects. We have postulated the following criterion: if the mean values of CHPs are in the proper ranges, the cardiac system is in a normal condition and it possesses the capability of homeostasis. In contrast, if the mean values of the CHPs do not lie in the proper ranges, the homeostasis of the cardiac system is lacking and some cardiac disease may follow. The results and procedure of MCG CHPs in the study afford a technological route for the application of HTc rf SQUIDs in cardiology.

show abstract

Singularity detection of the rabbit electrocardiogram: An evolutionary spectral method

Cited by 4 publications

References 7 publications

Characteristics of magnetocardiography and electrocardiography in the time-frequency domain

Characteristics of magnetocardiography and electrocardiography in the time-frequency domain

Ignition Coil Insulation Fault Detection Algorithm

Characteristic parameters of electromagnetic signals from a human heart system

Contact Info

Product

Resources

About