Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.
心房颤动（房颤）是一种常见的心律失常，可导致血栓形成并增加脑卒中甚至死亡的风险。针对临床应用中疾病筛检低假阴性率的需求，本文提出一种改进的低假阴性率卷积神经网络。通过在交叉熵损失函数中引入正则化系数，差别对待阳性和阴性样本的代价成本，使得网络训练时可加大对假阴性的惩罚。采用三甲医院采集的包含 21 077 位受试者的患者间临床数据集进行验证，相对于传统交叉熵损失函数，使用改进的损失函数可将假阴性率由 2.22% 降低至 0.97%，所选正则化系数可将灵敏度由 97.78% 提升至 98.35%，准确率 96.62% 亦较原来的 96.49% 有所提升。所提算法可在不牺牲准确率的前提下降低假阴性率，降低漏诊可能性以免错过最佳治疗时期，可为其他疾病的临床辅助诊断提供一种可变参数的损失函数。.
Keywords: atrial fibrillation; convolutional neural network; false-negative rate; loss function.