Chen Xianjie, Cheng Zhaoyun, Wang Sheng, Lu Guoqing, Xv Gaojun, Liu Qianjin, Zhu Xiliang
Department of Cardiovascular Surgery, Fuwai Central China Cardiovascular Hospital, Henan Cardiovascular Hospital and Zhengzhou University, Zhengzhou, China.
Department of Cardiovascular Surgery, Fuwai Central China Cardiovascular Hospital, Henan Cardiovascular Hospital and Zhengzhou University, Zhengzhou, China.
Comput Methods Programs Biomed. 2021 Apr;202:106009. doi: 10.1016/j.cmpb.2021.106009. Epub 2021 Feb 17.
The incidence of atrial fibrillation is increasing annually. We develop an automatic detection system, which is of great significance for the early detection and treatment of atrial fibrillation. This can lead to the reduction of the incidence of critical illnesses and mortality.
We propose an atrial fibrillation detection algorithm based on multi-feature extraction and convolutional neural network of atrial activity via electrocardiograph signals, and compare its detection based on cluster analysis, one-versus-one rule and support vector machine, using accuracy, specificity, sensitivity and true positive rate as evaluation criteria.
The atrial fibrillation detection algorithm proposed in this paper has an accuracy rate of 98.92%, a specificity of 97.04%, a sensitivity of 97.19%, and a true positive rate of 96.47%. The average accuracy of the algorithms we compared is 80.26%, and the accuracy of our algorithm is 23.25% higher than this average pertaining to the other algorithms.
We implemented an atrial fibrillation detection algorithm that meets the requirements of high accuracy, robustness and generalization ability. It has important clinical and social significance for early detection of atrial fibrillation, improvement of patient treatment plans and improvement of medical diagnosis.
心房颤动的发病率逐年上升。我们开发了一种自动检测系统，这对于心房颤动的早期检测和治疗具有重要意义。这可以降低危重病的发病率和死亡率。
我们提出了一种基于多特征提取和通过心电图信号对心房活动进行卷积神经网络的心房颤动检测算法，并以聚类分析、一对一规则和支持向量机为基础比较其检测效果，使用准确率、特异性、灵敏度和真阳性率作为评估标准。
本文提出的心房颤动检测算法的准确率为98.92%，特异性为97.04%，灵敏度为97.19%，真阳性率为96.47%。我们比较的算法的平均准确率为80.26%，我们算法的准确率比其他算法的这个平均值高23.25%。
我们实现了一种满足高精度、鲁棒性和泛化能力要求的心房颤动检测算法。它对于心房颤动的早期检测、改善患者治疗方案和改进医学诊断具有重要的临床和社会意义。
