He Kaiyue, Yang Cuiwei
Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China.
Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China;Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200093, P.R.China;Shanghai Engineering Research Center of Cardiac Electrophysiology, Shanghai 201318, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Jun 25;37(3):487-495. doi: 10.7507/1001-5515.201910005.
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.
心房颤动（AF）是临床上最常见的心律失常，可导致血流动力学改变、心力衰竭和中风，严重影响人类生命健康。作为一种进行性疾病，房颤的治疗会随着病情恶化而变得越来越困难，对房颤进行早期预测和干预是遏制病情恶化的关键。基于此，在本研究中，我们通过控制乙酰胆碱的剂量，改变了五只杂种犬的房颤易感性，并试图通过分析不同窦性心律状态下心房心外膜的电生理来对其进行评估。在此，我们从四个方面提出指标来研究心房激动规律。它们是心房激动节律的变异性、最早心房激动的变化、心房激动延迟的变化以及左右心房不同步。通过二元逻辑回归分析，将上述多个指标转化为房颤诱发率，用于对窦性心律期间的信号进行分类。分类的敏感性、特异性和准确性分别达到85.7%、95.8%和91.7%。实验结果表明，所提方法具有评估心房房颤易感性的能力，对房颤的早期预测和干预具有重要临床意义。
