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Foetal heart rate estimation by empirical mode decomposition and MUSIC spectrum

机译:通过经验模态分解和MUSIC谱估计胎儿心率

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It is still a challenge to estimate the foetal heart rate accurately from a strong nonstationary abdominal ECG signal. Even if signal process eliminates the predominant maternal ECG component, the foetal heartbeats are still very weak due to other existing interferences. This paper introduces empirical mode decomposition (EMD) and multiple signal classification (MUSIC) to tackle this issue. Firstly, preprocessing eliminates the interferences and noise in abdominal ECG signal and then the EMD is utilized to decompose the foetal ECG signal into a set of intrinsic mode functions, which could be used to detect the foetal QRS waves. Finally, the MUSIC is applied on the foetal QRS waves indicator sequence to estimate the foetal heart rate in the frequency domain with a high resolution. The basis functions of EMD are derived from the foetal signal under test, which makes the detection process robust and adaptive. In addition, the foetal heart rate estimation is carried out in the frequency domain regardless of the detection of R-wave peaks. In the simulated experiments with the proposed method, the mean value of the fHR estimation error is 2 BPM with a standard deviation of 1.5 at SNR -30 dB, and it decreases to 0 when SNR= -16 dB. When compared to the fastICA algorithm, the proposed method shows robustness using three different real foetal ECG databases with variable degrees of nonstationarity. (C) 2018 Elsevier Ltd. All rights reserved.
机译:从强烈的非平稳腹部心电图信号准确估计胎儿心率仍然是一个挑战。即使信号过程消除了主要的孕妇心电图成分,由于其他现有干扰,胎儿心跳仍然非常微弱。本文介绍了经验模式分解(EMD)和多信号分类(MUSIC)来解决此问题。首先,预处理消除了腹部ECG信号中的干扰和噪声,然后使用EMD将胎儿ECG信号分解为一组固有模式函数,可用于检测胎儿QRS波。最后,将MUSIC应用于胎儿QRS波指示器序列,以高分辨率估计胎儿心率在频域中。 EMD的基本功能是从被测胎儿信号得出的,这使得检测过程稳定且自适应。另外,不管检测到R波峰值如何,都在频域中进行胎儿心率估计。在使用该方法进行的模拟实验中,fHR估计误差的平均值为2 BPM,在SNR -30 dB时标准偏差为1.5,而在SNR = -16 dB时降低至0。当与fastICA算法进行比较时,所提出的方法使用具有可变不稳定度的三个不同的真实胎儿ECG数据库显示了鲁棒性。 (C)2018 Elsevier Ltd.保留所有权利。

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