Derivation of Atrial Surface Reentries Applying ICA to the Standard Electrocardiogram of Patients in Postoperative Atrial Fibrillation

Rieta, J.J.; Hornero, Fernando; Sánchez, César; Vaya, C.; Moratal, David; Sanchis, Juan

doi:10.1007/11679363_60

Cited by 3 publications

(7 citation statements)

References 13 publications

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“…Related references [1][2][3][4][5] confirmed that there existed an AF signal among the separated sources by PCA and ICA algorithm. Machines cannot recognize the AF wave as people, and they can only calculate some quantized judgments.…”

Section: Discussion On the Efficiency Of Clinical Monitoringmentioning

confidence: 62%

“…Thus, the sign of kurtosis should not be one necessary or sufficient judgment. Related references [2][3][4][5] have taken SC as one judgment, and thus SC can be regarded as one necessary judgment. Moreover, our experiment showed that the signal having a peak in 3-10 Hz may contain Gaussian noise ( Figure 6) or VA (Figure 11(c)), and SC cannot be one sufficient judgment.…”

Section: Discussion On the Efficiency Of Clinical Monitoringmentioning

confidence: 99%

“…Furthermore, Langey's experiments [4] presented that there were no significant differences for PCA and ICA algorithms when comparing the amplitude of the reference AF signal with that of the extracted AF signal. Rieta's study [5] showed that the AA signal, estimated by ICA techniques, was very close to that provided by epicardial recordings. The above research has verified that ICA is a valid and useful tool to extract the AF signal.…”

Section: Bss/pca Based Frame Of Af Signal Extractionmentioning

confidence: 92%

“…The SC [2,3] was computed as follows: where P AA is the power spectrum of the AF signal, fp is the frequency peak and fs is the sample rate. The higher the SC, the better the performance [2][3][4][5] . Relative references showed that if the extracted signal had a peak between 3 and 10 Hz, the corresponding SC would be more than 40% [2,3] , which could be a necessary condition when checking the AF signal.…”

Section: Figurementioning

confidence: 98%

“…Its prevalence increases with age, and up to 10% of the population older than 70 years has been diagnosed with AF [1][2][3][4][5] . With the projected growth of the elderly population, the prevalence of AF will certainly increase.…”

Section: Introductionmentioning

confidence: 99%

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Atrial fibrillatory signal estimation using blind source extraction algorithm based on high-order statistics

Wang

Rao

Zhang

2008

Sci. China Ser. F-Inf. Sci.

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The analysis and the characterization of atrial fibrillation (AF) requires, in a previous key step, the extraction of the atrial activity (AA) free from 12-lead electrocardiogram (ECG). This contribution proposes a novel non-invasive approach for the AA estimation in AF episodes. The method is based on blind source extraction (BSE) using high order statistics (HOS). The validity and performance of this algorithm are confirmed by extensive computer simulations and experiments on realworld data. In contrast to blind source separation (BSS) methods, BSE only extract one desired signal, and it is easy for the machine to judge whether the extracted signal is AA source by calculating its spectrum concentration, while it is hard for the machine using BSS method to judge which one of the separated twelve signals is AA source. Therefore, the proposed method is expected to have great potential in clinical monitoring. electrocardiogram (ECG), atrial fibrillation, blind source separation (BSS), blind source extraction (BSE)

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Section: Discussion On the Efficiency Of Clinical Monitoringmentioning

confidence: 62%

Section: Discussion On the Efficiency Of Clinical Monitoringmentioning

confidence: 99%

Section: Bss/pca Based Frame Of Af Signal Extractionmentioning

confidence: 92%

Section: Figurementioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Atrial fibrillatory signal estimation using blind source extraction algorithm based on high-order statistics

Wang

Rao

Zhang

2008

Sci. China Ser. F-Inf. Sci.

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Extraction of Desired Signal Based on AR Model with Its Application to Atrial Activity Estimation in Atrial Fibrillation

Wang

Rao

Shepherd

et al. 2008

EURASIP J. Adv. Signal Process.

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The use of electrocardiograms (ECGs) to diagnose and analyse atrial fibrillation (AF) has received much attention recently. When studying AF, it is important to isolate the atrial activity (AA) component of the ECG plot. We present a new autoregressive (AR) model for semiblind source extraction of the AA signal. Previous researchers showed that one could extract a signal with the smallest normalized mean square prediction error (MSPE) as the first output from linear mixtures by minimizing the MSPE. However the extracted signal will be not always the desired one even if the AR model parameters of one source signal are known. We introduce a new cost function, which caters for the specific AR model parameters, to extract the desired source. Through theoretical analysis and simulation we demonstrate that this algorithm can extract any desired signal from mixtures provided that its AR parameters are first obtained. We use this approach to extract the AA signal from 12-lead surface ECG signals for hearts undergoing AF. In our methodology we roughly estimated the AR parameters from the fibrillatory wave segment in the V1 lead, and then used this algorithm to extract the AA signal. We validate our approach using real-world ECG data.

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基于高阶统计量的盲源提取算法获取房颤信号

饶¹,

王²,

张³

2008

SSIF

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Derivation of Atrial Surface Reentries Applying ICA to the Standard Electrocardiogram of Patients in Postoperative Atrial Fibrillation

Cited by 3 publications

References 13 publications

Atrial fibrillatory signal estimation using blind source extraction algorithm based on high-order statistics

Atrial fibrillatory signal estimation using blind source extraction algorithm based on high-order statistics

Extraction of Desired Signal Based on AR Model with Its Application to Atrial Activity Estimation in Atrial Fibrillation

基于高阶统计量的盲源提取算法获取房颤信号

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