Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Department of Cardiology, Xinghua City People's Hospital, Jiangsu 225700, China.
Math Biosci Eng. 2022 Jul 11;19(10):9877-9894. doi: 10.3934/mbe.2022460.
Detection of atrial fibrillation (AF) events is significant for early clinical diagnosis and appropriate intervention. However, in existing detection algorithms for paroxysmal AF (AFp), the location of AF starting and ending points in AFp is not concerned. To achieve an accurate identification of AFp events in the long-term dynamic electrocardiograms (ECGs), this paper proposes a two-step method based on machine learning. In the first step, based on features extracted from the calculated R-to-R intervals (RR intervals, the cycle of heart beat), the rhythm type of the ECG signal is first classified into three classes (AFp rhythm, persistent AF (AFf) rhythm, and non-atrial fibrillation (non-AF, N) rhythm) using support vector machine (SVM). In the second step, the starting and ending points for AF episodes of AFp rhythms predicted in the first step are further located based on heartbeat classification. By training a deep convolutional neural network with phased training, the segmented beats of AFp rhythms are divided into AF beats and non-AF beats to determine the beginning and end of any AF episode. The proposed two-step method is trained and tested on the 4th China Physiological Signal Challenge 2021 databases. A final score U of 1.9310 is obtained on the unpublished test set maintained by the challenge organizers, which demonstrates the advantage of the two-step method in AFp event detection. The work is useful for assessing AF burden index for AFp patients.
心房颤动 (AF) 事件的检测对早期临床诊断和适当干预至关重要。然而，在阵发性 AF (AFp) 的现有检测算法中，并不关注 AFp 中 AF 起始和结束点的位置。为了在长期动态心电图 (ECG) 中准确识别 AFp 事件，本文提出了一种基于机器学习的两步法。在第一步中，基于从计算的 R 到 R 间隔（心跳周期）中提取的特征，使用支持向量机 (SVM) 将 ECG 信号的节律类型首先分为三类（AFp 节律、持续性 AF (AFf) 节律和非心房颤动 (非-AF, N) 节律）。在第二步中，根据心跳分类进一步定位第一步中预测的 AFp 节律的 AF 发作的起始和结束点。通过使用分阶段训练的深度卷积神经网络进行训练，将 AFp 节律的分段节拍分为 AF 节拍和非-AF 节拍，以确定任何 AF 发作的开始和结束。所提出的两步法在 2021 年第四届中国生理信号挑战赛的数据库上进行了训练和测试。在挑战赛组织者维护的未公布的测试集上获得了 1.9310 的最终分数 U，这证明了两步法在 AFp 事件检测中的优势。这项工作有助于评估 AFp 患者的 AF 负担指数。
