Fetal ECG extraction from single-channel maternal ECG using singular value decomposition

Kanjilal, P. P.; Palit, Sarbani; Saha, Goutam

doi:10.1109/10.553712

Cited by 282 publications

(123 citation statements)

References 18 publications

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“…12 To quantify the degree of periodicity in S we defined a new Periodicity Index (PI) based on the power levels of its PC and RC components in the time-domain, measured by the standard deviation of these components around their mean values. The PI was calculated as follows: (1) Thus, for a purely periodic signal StD (RC) = 0 and PI=1.…”

Section: Singular Value Decomposition and Periodicitymentioning

confidence: 99%

“…Thus, the aim of this study was to investigate how the spatio-temporal characteristics of a cardiac fibrillation source that determine the DF of a local signal also contribute to the fractionation of that signal. Using the well established singular value decomposition (SVD) method 12 we analyzed numerical simulations and optical mapping data to test the hypothesis that the meandering pattern of a reentrant source that results in spatio-temporal instability and wave fractionation, is reflected in the power spectrum of local activity elsewhere in its surroundings and therefore may be used to trace the core of the source.…”

Section: Introductionmentioning

confidence: 99%

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Rotor meandering contributes to irregularity in electrograms during atrial fibrillation

Zlochiver¹,

Yamazaki²,

Kalifa³

et al. 2008

Heart Rhythm

153

122

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Radiofrequency ablation therapy of atrial fibrillation (AF) recently incorporated the analysis of dominant frequency (DF) and/or electrogram fractionation for guidance. However, the relationships between DF, fractionation and spatio-temporal characteristics of the AF source remain unclear. We hypothesize that meandering reentrant AF source contributes to the wave fractionation and is reflected in the power spectrum of local electrograms elsewhere in the rotor's surroundings.Methods-Meandering rotors as AF sources were simulated in 2D models of human atrial tissue and recorded in isolated sheep hearts. Non-dominant elements of the signals were differentiated from the dominant elements using singular value decomposition, whereby the purely periodic constituent (PC) relating to the rotor's DF was eliminated rendering a residual constituent (RC) that consisted of all other activity.Results-Spectral analysis of the decomposed constituents revealed peaks corresponding to the meandering frequency of the rotor tip, the magnitudes of which were proportional to the size of, and the distance to the rotor core. Similar analyses on epicardial optical signals and electrograms from isolated sheep hearts, as well as human complex fractionated atrial electrograms demonstrated applicability of the approach.Conclusion-Increased meandering of the rotor driving AF reduces activation periodicity and increases fractionation. The spectral manifestation of the rotor activity beyond the meandering region makes it possible to characterize AF source stability, as well as DF in humans using electrode mapping.

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Section: Singular Value Decomposition and Periodicitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rotor meandering contributes to irregularity in electrograms during atrial fibrillation

Zlochiver¹,

Yamazaki²,

Kalifa³

et al. 2008

Heart Rhythm

153

122

View full text Add to dashboard Cite

show abstract

“…산모의 복부에서 측정한 ECG 신호를 이용 태아의 ECG를 추출하는 신호 처리 방법에 대한 여러 연구가 수행되어 왔다 [3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: 서 론unclassified

Mobile ECG Measurement System Design with Fetal ECG Extraction Capability

Choi¹,

Kim²,

Kim³

et al. 2017

The Transactions of The Korean Institute of Electrical Engineer

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-In this paper, the abdomen ECG(AECG) is employed to measure the mother's ECG instead of the conventioanl thoracic ECG measurement. The fetus ECG signal can be extracted from the AECG using an algorithm that utilizes the mobile fetal ECG measurement platform, which is based on the BLE (Bluetooth Low Energy). The algorithm has been implemented by using a replacement processor processed directly from the platform BLE instead of the large statistical data processing required in the ICA(Independent component analysis). The proposed algorithm can be implemented on a mobile BLE wireless ECG system hardware platform to process the maternal ECG. Wireless technology can realize a compact, lowpower radio system for short distance communication and the IOT(Intenet of Things) enables the transmission of real-time ECG data. It was also implemented in the form of a compact module in order for mothers to be able to download and store the collected ECG data without having to interrupt or move the logger, and later link the module to a computer for downloading and analyzing the data. A mobile ECG measurement prototype is manufactured and tested to measure the FECG for pregnant women. The experimental results verify a real-time FECG extraction capability for the proposed system. In this paper, we propose an ECG measurement system that shows approximately 91.65% similarity to the MIT database and the conventional algorithm and SNR performance about 10% better.

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“…用。如在轴承振动信号 [7][8] 、语音信号 [9] 、电荷放电信号 [10] 等不同性质信号的消噪方面，这种矩阵形式都可以取得相当好的消噪效果。本文拟不再探讨 SVD 的消噪问题，而是提出利用 SVD 实现信号中不同频率的分离，这与通常的 SVD 消噪应用完全不同，因此不能再采用 Hankel 矩阵。为达到信号分离的目的，我们采用另一种矩阵形式：对于一个待处理信号，将信号均分成多段，利用每段形成矩阵的行向量。这种矩阵在信号处理中也有一定的应用，如 KANJILAL 等 [11][12] 利用这种矩阵通过奇异值比谱来检测信号的周期性，并从复合母体心电信号中提取胎儿心电信息，再如 CONG 等 [13] 将 SVD 用于信号处理时首先必须利用信号构造合适的矩阵 A。采用 Hankel 矩阵时可以消除信号中的噪声 [7][8][9][10] ，但是我们的目的是实现信号的分离，因此必须另外构造矩阵。对于一个数字信号序列 X=(x(1)，x(2)， … ，x(N))，取两个正整数 m 和 n，对此序列按每次 n 个点连续截取 m 段，以这 m 段构造矩阵 A 如下 (1) (2) ( ) ( 1) ( 2) …”

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“…) 尽管这种矩阵在信号处理和故障诊断中已有一定的应用 [11][12][13] ，但是简单地采用这种矩阵并不能实 T 1 2 1 1 2 1 2 1 2 T 1 2 2 1 2 1 2 , , , , , , , [7][8][9][10] ，此外，也可采用小波变换方法来消除噪声 [18] 。 (2) 为了使变结构 SVD 算法获得良好的频率分离效果，必须对信号进行整周期采样，这是由算法的信号分离机理决定的。…”

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Variable Structure SVD Algorithm and Its Application to Signal Separation

Zhao¹

2017

JME

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Abstract：The key question of applying singular value decomposition (SVD) to signal processing is the construction of the matrix. In order to separate the different frequency components from the original signal through SVD, a recursive SVD algorithm with variable matrix structure is proposed, whose idea is to change the structure of the matrix in the process of SVD recursion decomposition, each time when the SVD is carried out, the structure of the matrix to be decomposed will change regularly to adapt to the different frequency components in the signal, so that the different frequency components can be separated. The signal decomposition algorithm of the variable structure SVD is deduced, and it is proved that the original signal can be decomposed into a linear combination of a series of component signals by this algorithm. Furthermore, the signal separation mechanism of this algorithm is analyzed theoretically, and it is proved that for some frequency structure, variable structure SVD can separate the each single frequency component successively from the original signal. Finally, the separation examples of the simulation signal and actual engineering signal are provided, which demonstrate the good signal separation effect of the variable structure SVD algorithm. The comparison with wavelet analysis and multi-resolution SVD method shows that variable structure SVD can achieve the better signal separation results than these two methods.

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Fetal ECG extraction from single-channel maternal ECG using singular value decomposition

Cited by 282 publications

References 18 publications

Rotor meandering contributes to irregularity in electrograms during atrial fibrillation

Rotor meandering contributes to irregularity in electrograms during atrial fibrillation

Mobile ECG Measurement System Design with Fetal ECG Extraction Capability

Variable Structure SVD Algorithm and Its Application to Signal Separation

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