Predicting atrial fibrillation inducibility in a canine model by multi-threshold spectra of the recurrence complex network

Bai, Baodan; Wang, Yuanyuan; Yang, Cuiwei

doi:10.1016/j.medengphy.2012.07.012

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…This paper combines complex network community structure and k-core analysis to make further research into chaotic sequences of rolling bearing fault vibration signals [12][13][14][15][16][17][18] . Research shows that efficient resolution of complex networks of bearings can make the fault information be identified quickly.…”

Section: Introductionmentioning

confidence: 99%

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

Sun,

Cai,

Gao

et al. 2024

J. Phys.: Conf. Ser.

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A complex network is a method to analyze nonlinear time series on multiple temporal and spatial scales. K-core analysis of several typical nonlinear series was studied, and then based on this, a fast algorithm method was employed to divide the community structure. According to the definition of k-core, the rolling bearings complex network was divided into different cores at the router level, and the main characteristic quantities such as degree distribution and clustering coefficient of every k-core can be analyzed. The results show that there is a corresponding relationship between the characteristic parameters and the community structure of the bearing. The analysis results show that compared with the traditional method, the method of complex network community structure and k-core analysis has a better diagnosis effect.

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Section: Introductionmentioning

confidence: 99%

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

Sun,

Cai,

Gao

et al. 2024

J. Phys.: Conf. Ser.

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“…But due to the difficulties and limitations in the postoperative follow-up, the normal sinus rhythm (NSR) signals of pre-AF and the postoperative signals are rarely used for AF prediction. Most of the AF prediction methods are based on the data obtained from the invasive animal experiments or clinical operations [13, 14]. Invasive mapping method is the major technique in the treatment of cardiac arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

Prediction of atrial fibrillation using the recurrence complex network of body surface potential mapping signals

Bai

Yang

et al. 2019

THC

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OBJECTIVE: Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Early diagnosis and intervention of AF is essential to avert the further fatality. The technique of noninvasive electrical mapping, especially the body surface potential mapping (BSPM), has a more practical application in the study of predicting AF, when compared with the invasive electrical mapping methods such as the epicardial mapping and interventional catheter mapping. However, the prediction of AF with noninvasive signals has been inadequately studied. Thus, the aim of this paper was to analyze the properties of atrial dynamic system based on the noninvasive BSPM signals (BSPMs), using the recurrence complex network, and consequently to evaluate its role in predicting the recurrence of AF in clinical aspect. METHOD: Twelve patients with persistent AF were included in this study. Their preoperative and postoperative BSPMs were recorded. Initially, the preoperative BSPMs were transformed into the recurrence complex network to characterize the complexity property of the atria. Subsequently, the parameters of recurrence ratio (REC), determinism (DET), entropy of the diagonal structure distribution (ENTR), and laminarity (LAM) were calculated. Furthermore, the difference in the parameters in the four regions of the body and the difference obtained from the dominant frequency (DF) method were compared. Finally, the results obtained for the atrial dynamic system complexity from a 12-lead electrocardiogram (ECG) from the BSPMs were discussed. RESULTS: Our study revealed that the patients whose REC is greater than an average threshold, and with a lower LAM presented a much higher possibility of AF recurrence, after the AF surgery. CONCLUSIONS: The recurrence complex network is a useful and convenient way to evaluate the nonlinear properties of the BSPMs in patients with AF. It has good immunity to the lead position and has a potential role in the understanding of predicting the recurrence of AF.

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“…Another one is study on atrial fibrillation mechanism based on epicardium or endocardium mapping of electrical signals [6]. From the point of method, it can be divided into linear analysis method and nonlinear analysis method [7]. Previous studies have shown that nonlinear analysis methods are more suitable for the analysis of physiological systems, especially cardiac dynamic system [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Atrial Signals Based on a Novel Complex Network

Bai¹,

Si²

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-atrial fibrillation (AF) is one of the most common arrhythmia in clinical, which is the major cause of embolic events and stroke, resulting in an important morbidity and mortality. The mechanisms leading to AF are still under extensive research. In this study, we present a novel complex network approach to analysis the dynamics of the heart during the whole AF process (before the AF to end of the AF). Three canine models of acute AF were designed and the common parameters of the novel complex network were used to investigate the method. The results show that the novel complex network parameter can not only detect the AF, but also can estimate the vulnerability of atrial fibrillation effectively.

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Predicting atrial fibrillation inducibility in a canine model by multi-threshold spectra of the recurrence complex network

Cited by 5 publications

References 29 publications

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

Prediction of atrial fibrillation using the recurrence complex network of body surface potential mapping signals

Analysis of the Atrial Signals Based on a Novel Complex Network

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