基于心外膜标测的心房易颤性评估

何凯悦; 杨翠微

doi:10.7507/1001-5515.201910005

基于心外膜标测的心房易颤性评估

doi: 10.7507/1001-5515.201910005

何凯悦¹,
杨翠微^{1, 2, 3, ,}

详细信息

通讯作者:
杨翠微，Email：yangcw@fudan.edu.cn

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出版历程
- 收稿日期: 2019-10-08
- 修回日期: 2020-04-16
- 发布日期: 2020-03-17

Assessment of atrial fibrillation inducibility based on epicardial mapping signals

Kaiyue HE¹,
Cuiwei YANG^{1, 2, 3
, ,}

More Information

Corresponding author: YANG Cuiwei, Email: yangcw@fudan.edu.cn

摘要

摘要: 心房颤动（简称：房颤）是临床上最常见的心律失常，可引起血流动力学改变、心力衰竭、脑卒中等并发症，严重影响着人类的生命健康。作为一种自身促进性疾病，房颤的治疗难度会随着病情恶化逐渐增加，早期预测和干预是遏止病情恶化的关键。基于此，本文通过控制乙酰胆碱静脉注射剂量，改变了 5 只杂交犬的心房易颤性，并尝试通过分析不同状态窦性心律下心房心外膜的电活动特性来评估心房易颤性。本文主要从心房激动间期变异性、心房最早激动点转移、心房激动时延变化、左右心房失步四个方面提出 4 项指标来研究心房的激动规律，并利用二元逻辑回归分析，将多项指标转化为房颤诱发成功概率，从而对窦性信号进行分类。分类的敏感性、特异性和准确率分别达到 85.7%、95.8% 和 91.7%。实验结果表明，本文方法具有评估心房易颤性的能力，对房颤的提前预测和干预具有重要的临床意义。
- 房颤 /
- 心外膜标测 /
- 电生理特性 /
- 二元逻辑回归
Abstract: Atrial fibrillation (AF) is the most common arrhythmia in clinic, which can cause hemodynamic changes, heart failure and stroke, and seriously affect human life and health. As a self-promoting disease, the treatment of AF can become more and more difficult with the deterioration of the disease, and the early prediction and intervention of AF is the key to curbing the deterioration of the disease. Based on this, in this study, by controlling the dose of acetylcholine, we changed the AF vulnerability of five mongrel dogs and tried to assess it by analyzing the electrophysiology of atrial epicardium under different states of sinus rhythm. Here, indices from four aspects were proposed to study the atrial activation rule. They are the variability of atrial activation rhythm, the change of the earliest atrial activation, the change of atrial activation delay and the left-right atrial dyssynchrony. By using binary logistic regression analysis, multiple indices above were transformed into the AF inducibility, which were used to classify the signals during sinus rhythm. The sensitivity, specificity and accuracy of classification reached 85.7%, 95.8% and 91.7%, respectively. As the experimental results show, the proposed method has the ability to assess the AF vulnerability of atrium, which is of great clinical significance for the early prediction and intervention of AF.
- atrial fibrillation /
- epicardial mapping /
- electrophysiology /
- binary logistic regression

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图 1 AA 间期异变

Figure 1. The changes in AA interval

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图 2 心房最早激动点转移示意图

Figure 2. Diagram of the earliest activation points shifting in the atriums

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图 3 心房时延变化阈值量化示意图

Figure 3. Quantified variation maps of atrial activation delay

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图 4 左右心房同步-失步-同步示意图

Figure 4. Diagram of left and right atrial synchronization-dyssynchronization-synchronization

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图 5 降噪结果

Figure 5. Denoising results

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图 6 局域滤波结果

Figure 6. Local filtering results

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图 7 激动时刻提取结果

Figure 7. The results of activation time extraction

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图 8 左右心房等时图

Figure 8. Isochronic mapping of the left and right atriums

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图 9 基于激动时刻分析的二元 Logistic 回归分类示意图

Figure 9. Schematic results of binary logistic regression classification based on activation time analysis

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表 1 小波分层降噪阈值

Table 1. Wavelet decomposition denoising threshold

小波系数所在层数	重构信号频带范围/Hz	小波阈值经验值
第一层高频系数	500～1 000	3 倍于本层系数标准差
第二层高频系数	250～500	3 倍于本层系数标准差
第三层高频系数	125～250	本层系数标准差
第四层高频系数	75～125	本层系数标准差
第五层高频系数	37.5～75	0.25 倍于本层系数标准差

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表 2 二元逻辑回归分类结果

Table 2. Results of binary logistic classification

分类指标	敏感性	特异性	准确率
N_CAA	54.8%	91.7%	74.4%
N_CFA	42.9%	81.3%	63.3%
R_AADC	78.6%	95.8%	87.8%
N_LRAA	69.0%	87.5%	78.9%
N_CAA，，N_CFA，R_AADC，N_LRAA	85.7%	95.8%	91.1%

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参考文献(23)

[1]	Yoshida Takuo, Uchino S, Yokota T, et al. The impact of sustained new-onset atrial fibrillation on mortality and stroke incidence in critically ill patients: A retrospective cohort study. Journal of critical care, 2018, 44: 267-272. doi: 10.1016/j.jcrc.2017.11.040
[2]	Zhang S. Atrial fibrillation in mainland China: epidemiology and current management. Heart, 2009, 95(13): 1052-1055. doi: 10.1136/hrt.2008.146589
[3]	Schnabel R B, Yin Xiaoyan, Gona P, et al. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study. Lancet, 2015, 386(9989): 154-162. doi: 10.1016/S0140-6736(14)61774-8
[4]	Wolf P A, Mitchell J B, Baker C S, et al. Impact of atrial fibrillation on mortality, stroke, and medical costs. Arch Intern Med, 1998, 158(3): 229-234. doi: 10.1001/archinte.158.3.229
[5]	Le Heuzey J Y, Paziaud O, Piot O, et al. Cost of care distribution in atrial fibrillation patients: the COCAF study. American Heart Journal, 2004, 147(1): 121-126. doi: 10.1016/S0002-8703(03)00524-6
[6]	Kumar S, Teh A W, Medi C, et al. Atrial remodeling in varying clinical substrates within beating human hearts: relevance to atrial fibrillation. Prog Biophys Mol Biol, 2012, 110(2-3): 278-294. doi: 10.1016/j.pbiomolbio.2012.07.011
[7]	Yoshizawa T, Niwano S, Niwano H, et al. Prediction of new onset atrial fibrillation through P wave analysis in 12 Lead ECG. Int Heart J, 2014, 55(5): 422-427. doi: 10.1536/ihj.14-052
[8]	Thong T, Mcnames J, Aboy M, et al. Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans Biomed Eng, 2004, 51(4): 561-569. doi: 10.1109/TBME.2003.821030
[9]	Maryam M, Ghassemian H. Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal. Physiol Meas, 2011, 32(8): 1147-1162. doi: 10.1088/0967-3334/32/8/010
[10]	Schuessler R B, Grayson T M, Bromberg B I, et al. Cholinergically mediated tachyarrhythmias induced by a single extrastimulus in the isolated canine right atrium. Circ Res, 1992, 71(5): 1254-1267. doi: 10.1161/01.RES.71.5.1254
[11]	杨诏旭, 蔡振杰, 王跃民, 等. 乙酰胆碱诱发犬急性心房颤动模型的研究. 心脏杂志, 2002, 14(3): 195-198. doi: 10.3969/j.issn.1009-7236.2002.03.006
[12]	Everett T H 4th, Kok L C, Vaughn R H, et al. Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Trans Biomed Eng, 2001, 48(9): 969-978. doi: 10.1109/10.942586
[13]	Ganesan A N, Kuklik P, Lau D H, et al. Bipolar electrogram Shannon entropy at sites of rotational activation implications for ablation of atrial fibrillation. Circulation Arrhythmia & Electrophysiology, 2013, 6(1): 48-57.
[14]	Sun Liqian, Yang Cuiwei, Lin Zhang, et al. A preliminary study on atrial epicardial mapping signals based on graph theory. Med Eng Phys, 2014, 36(7): 875-881. doi: 10.1016/j.medengphy.2014.03.011
[15]	Ouyang Gaoxiang, Zhu Xiangyang, Ju Zhaojie, et al. Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot. IEEE J Biomed Health Inform, 2014, 18(1): 257-265. doi: 10.1109/JBHI.2013.2261311
[16]	白宝丹. 基于递归复杂网络的房颤预测分析方法研究. 上海: 复旦大学, 2012.
[17]	Chen Ying, Wu Zhong, Yang Cuiwei, et al. Investigation of atrial vulnerability by analysis of the sinus node EG from atrial fibrillation models using a phase synchronization method. IEEE Trans Biomed Eng, 2012, 59(9): 2668-2676. doi: 10.1109/TBME.2012.2208751
[18]	黄娟, 钱新, 王成林, 等. 小波滤波器的构造及其在环境研究中的应用. 南京大学学报: 自然科学版, 2007, 43(4): 389-396.
[19]	吴广宁, 刘曦, 佟来生, 等. 提取PWM脉冲下局部放电信号的sym4小波滤波器的研究. 电工技术学报, 2006, 21(2): 35-38, 50. doi: 10.3321/j.issn:1000-6753.2006.02.008
[20]	孙莉倩. 心房电标测信号的节律分析及空间关联初探. 上海: 复旦大学, 2013.
[21]	杨翠微. 基于心外膜标测技术的房颤表征方法及电生理机制研究. 复旦大学, 2008.
[22]	苗志博. 心外膜标测信号特征点检测. 上海: 复旦大学, 2014.
[23]	王济川, 郭志刚. Logistic回归模型: 方法与应用. 北京: 高等教育出版社, 2001.