Atrial fibrillation (AF) is the most common cardiac arrhythmia and can lead to sudden cardiac death and various complications, imposing a significant social burden. Electrocardiography is the gold standard for diagnosis, but missed and misdiagnoses of atrial fibrillation frequently occur due to variations in physicians' experience. Therefore, it is necessary to develop artificial intelligence models capable of accurately identifying AF in real-world clinically complex electrocardiogram (ECG) data.

To establish a clinical ECG database with multiple positive categories, and based on deep learning technology, utilize static ECG signal data to train a convolutional neural network (CNN) model for AF detection, aiming to improve the accuracy of automated AF diagnosis.

A total of 10 000 patients who met the inclusion and exclusion criteria and visited the Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University) in 2023 were selected. Among them, 1 462 cases were diagnosed with AF, and 8 538 cases were non-AF. General patient information (ages and genders) and static ECG data were collected to establish a clinical ECG database with multiple positive categories. The overall dataset was divided into training set (n=8 000), validation set (n=1 000), and test set (n=1 000) at 8∶1∶1 ratio. An advanced convolutional neural network model, AF Networks (AFNet), was developed using the training set for AF detection. The model's performance was evaluated using the validation and test sets, with metrics including sensitivity, positive predictive value, F1 score, accuracy, and the area under the ROC curve (AUC). These metrics were used to assess the model's performance in ECG diagnostic tasks and to analyze its strengths and limitations.

No statistically significant differences were observed in the comparisons across the three datasets for gender (χ²=1.32, P=0.517), age (F=0.87, P=0.419), and ECG distribution (χ²=2.666, P=0.264). In the test set, the AFNet model demonstrated a sensitivity of 98.00%, specificity of 99.29%, positive predictive value of 96.08%, negative predictive value of 99.65%, accuracy of 99.10%, an F1-score of 0.97, and AUC of 0.99 for diagnosing atrial fibrillation. In the validation set, the AFNet model also achieved high performance, with sensitivity, positive predictive value, accuracy, and F1-score reaching 96.81%, 95.80%, 99.67%, and 0.96, respectively.

The AFNet neural network model demonstrated efficient feature extraction for AF detection in a clinical ECG database with multiple positive categories. This is of great clinical value for AF screening.

心房颤动是最常见的心律失常，可导致心脏猝死及多种并发症，社会负担沉重。心电图是诊断心房颤动的金标准，由于医生的经验原因易漏诊和误诊，因此有必要开发一种能精准识别真实临床复杂心电数据的人工智能模型。

建立多种阳性类别的临床心电数据库，并基于深度学习技术，利用静态心电信号数据训练卷积神经网络心房颤动检测模型，以提高心房颤动自动诊断的准确性。

选取2023年在湖南省人民医院（湖南师范大学附属第一医院）就诊的年龄≥18岁并获得至少1次标准10 s 12导联心电图且符合选取心电图诊断分类的患者10 000例，其中心房颤动1 462例，非心房颤动8 538例。收集患者的一般资料（年龄和性别）、静态心电图数据等，建立多种阳性类别的临床心电数据库。将总体数据集按照8∶1∶1的比例分为训练集（n=8 000）、验证集（n=1 000）和测试集（n=1 000）。使用训练集拟合一种先进的卷积神经网络模型——心房颤动神经网络（AFNet）用于心房颤动的检测，使用测试集和验证集评估AFNet模型检测心房颤动的性能。

3个数据集性别（χ²=1.32，P=0.517）、年龄（F=0.87，P=0.419）、心电图分布（χ²=2.666，P=0.264）比较，差异无统计学意义。在测试集中，AFNet模型诊断心房颤动的灵敏度为98.00%，特异度为99.29%，阳性预测值为96.08%，阴性预测值为99.65%，准确率为99.10%，F1评分为0.97，受试者工作特征（ROC）曲线下面积为0.99。在验证集中，AFNet模型诊断心房颤动的灵敏度、阳性预测值、准确率和F1评分分别为96.81%、95.80%、99.67%和0.96。

本研究将神经网络模型AFNet应用于涵盖多种阳性类别的临床心电数据库，所构建的心房颤动检测系统具备高效的特征解析能力，这对心房颤动的筛查具有重要的临床意义。