陡脈沖干擾下的心電信號濾波及QRS提取

姚晰童; 代煜; 張建勛; 葛錦濤; 陳通; 楊灝

doi:10.13374/j.issn2095-9389.2019.06.20.004

陡脈沖干擾下的心電信號濾波及QRS提取

doi: 10.13374/j.issn2095-9389.2019.06.20.004

姚晰童¹,
代煜^1, ,,
張建勛¹,
葛錦濤¹,
陳通¹,
楊灝²

1.
南開大學機器人與信息自動化研究所，天津 300310
2.
南開大學電子信息與光學工程學院，天津 300310

基金項目: 國家重點研發計劃資助項目(2017YFC0110402)；天津市自然科學基金項目資助項目(18JCYBJC18800)

詳細信息

通訊作者:
E-mail：daiyu@nankai.edu.cn

中圖分類號: TN911.72
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- 文章訪問數: 2039
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- 被引次數: 0
出版歷程
- 收稿日期: 2019-06-20
- 刊出日期: 2020-05-01

ECG filtering and QRS extraction under steep pulse interference

1.
Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300310, China
2.
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300310, China

More Information

Corresponding author: E-mail: daiyu@nankai.edu.cn

摘要

摘要: 為消除陡脈沖帶來的干擾，分析了陡脈沖干擾的特點，建立了陡脈沖噪聲數學模型，提出了基于變分模態分解（Variational mode decomposition, VMD）的心電信號濾波算法，提取疊加在心電信號中陡脈沖干擾分量、識別陡脈沖干擾分量并剔除陡脈沖干擾分量；為減少VMD分解層數、提高實時性并減少內存消耗，提出了心電信號預處理算法；針對醫療環境中的隨機噪聲伴隨陡脈沖出現的情況，分析了VMD后子信號中隨機噪聲的特點，提出了基于VMD子信號能量估計的閾值去噪算法；利用變分模態分解的帶通濾波器組特性，提出了基于變分模態分解子信號重組的QRS波群檢測算法，配合濾波算法以提高心電信號特征檢測精度。以添加了高斯白噪聲和模擬陡脈沖干擾的MIT?BIH數據庫心電信號和醫療環境中采集的心電信號為實驗對象，分別實現對濾波算法和QRS波群檢測算法的定量對比分析。
- 心電信號 /
- 陡脈沖干擾 /
- 變分模態分解 /
- MIT?BIH數據庫 /
- QRS波群
Abstract: Applying a steep pulse voltage of appropriate amplitude to a cell membrane can induce transient and reversible breakdown of the membrane, which has broad application prospects in biomedicine and clinical fields. However, the noise generated by the steep pulse seriously interferes with a patient’s electrocardiogram (ECG) signal resulting in decrease in the accuracy of the ECG feature point detection algorithm. Thus, doctors are unable to understand the state of the patient during treatment, thus limiting complete benefits of the therapy. To eliminate the interference caused by the steep pulse, we analyzed the characteristics of steep pulse interference and established the mathematical model of steep pulse noise. Moreover, we proposed an ECG signal filtering algorithm based on variational mode decomposition (VMD) to extract the steep pulse interference component superimposed on the ECG signal. The proposed algorithm could identify and eliminate the steep pulse interference component. We also designed an ECG signal preprocessing algorithm to reduce the decomposition layer of the VMD algorithm, which improved the real-time performance and reduced the memory consumption. To identify the random noise in the medical environment accompanied by the occurrence of steep pulses, we analyzed the characteristics of random noise in the sub-signal after VMD. Further, we proposed a threshold denoising algorithm based on VMD for sub-signal energy estimation. On the basis of the characteristics of a band-pass filter bank with VMD, we proposed a QRS complex detection algorithm based on VMD sub-signal recombination. Combined with the filtering algorithm, the proposed algorithm was able to improve the accuracy of ECG signal detection. By conducting experiments on ECG signals from the MIT–BIH database with Gaussian white noise and simulated steep pulse interference and those collected in the medical environment, we compared and analyzed the filtering algorithm and QRS complex detection algorithm.
- ECG signal /
- steep pulse interference /
- variational mode decomposition /
- MIT?BIH database /
- QRS complex

HTML全文

圖 1 受陡脈沖干擾的ECG。（a）受到陡脈沖干擾的心電信號；（b）未受陡脈沖干擾的心電信號

Figure 1. ECG disturbed by steep pulses: (a) ECG disturbed by steep pulses; (b) ECG not disturbed by steep pulses

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圖 2 衰減模型單邊幅頻圖

Figure 2. Diagram of a single-sided amplitude–frequency attenuation model

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圖 3 VMD 6階分解子信號。（a）原信號；（b）u₁；（c）u₂；（d）u₃；（e）u₄；（f）u₅；（g）u₆

Figure 3. Use of VMD to divide the signal into six layers: (a) original signal; (b) u₁; (c) u₂; (d) u₃; (e) u₄; (f) u₅; (g) u₆

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圖 4 u₂中干擾分量消除效果。（a）閾值處理前；（b）閾值處理后

Figure 4. Interference component elimination effect in u₂: (a) before using thresholds; (b) after using thresholds

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圖 5 消除隨機噪聲分量之后的子信號。（a） u₄；（b） u₅

Figure 5. Sub-signal after eliminating random noise components: (a) u₄; (b) u₅

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圖 6 R波檢測的算法驗證。（a）原信號；（b）重組的QRS；（c） R波增強

Figure 6. Verification of the R-wave detection algorithm: (a) original signal; (b) restructured QRS wave; (c) enhanced R wave

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圖 7 R波檢測的算法流程圖

Figure 7. Flowchart of the R-wave detection algorithm

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圖 8 實驗設備展示。（a）不可逆電消融樣機；（b）心電信號采集板

Figure 8. Experimental equipment: (a) prototype of irreversible electrical pulse ablation surgery; (b) ECG signal sampling circuit

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圖 9 信號預處理效果對比

Figure 9. Effect of signal preprocessing interference

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圖 10 對分解層數和懲罰因子進行驗證

Figure 10. Values of K and quadratic penalty were verified through experiments

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圖 11 施放陡脈沖期間相關噪聲抑制。（a）陡脈沖干擾消除前；（b）消除陡脈沖干擾后支；（c）完全消除陡脈沖干擾

Figure 11. Result of eliminating steep pulse interference: (a) ECG disturbed by steep pulses; (b) forehead is retained; (c) fore branches are eliminated

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圖 12 陡脈沖干擾抑制效果對比。（a）本文提出算法效果；（b）小波算法效果；（c）帶阻濾波器算法效果

Figure 12. Comparison of the results of steep pulse noise filtering: (a) algorithm in this article; (b) wavelet algorithm; (c) bandpass filter

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圖 13 a，b最優值驗證。（a） b恒定a改變；（b） a恒定b改變

Figure 13. Verification of the optimal values of a and b: (a) when b is constant and a is changed; (b) when a is constant and b is changed

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表 1 陡脈沖干擾消除效果對比

Table 1. Results obtained after a variety of filtering processes

SNR of the original signal/dB	Bandpass filter（SNR）/dB	Wavelet packet filter（SNR）/dB	Algorithm of this paper（SNR）/dB
1.481	?5.369	0.199	8.022
?6.732	?11.456	?8.710	3.070
?12.110	?18.928	?13.913	?0.410

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表 2 去噪效果對比

Table 2. Comparison of the results of the filtering processes

Algorithm	SNR between ECG and random noise /dB
Algorithm	Random noise SNR=1 dB	Random noise SNR=3 dB	Random noise SNR=8 dB	Random noise SNR=10 dB
Wavelet^[8]	7.121	8.116	11.659	15.227
EMD^[9]	6.129	8.900	12.387	14.981
This paper	8.039	10.122	14.649	20.836

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表 3 與常用算法準確度對比

Table 3. Comparison of the accuracy of the algorithms

Algorithm	$ {N}_{{\rm{t}}{\rm{o}}{\rm{t}}{\rm{a}}{\rm{l}}} $	TP	FP	FN	ED/%	SE/%
Difference threshold algorithm^[20]	16218	14157	765	1296	12.710	91.61
Bandpass filter algorithm^[21]		15138	377	703	6.659	95.56
Algorithm in this article		15741	360	117	2.942	99.23

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