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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