P波参数结合人工智能算法在心房颤动检测中的价值

The value of P wave parameters combined with artificial intelligence algorithm in atrial fibrillation detection

心房颤动(房颤)是最常见的心律失常。ECG上P波代表心房去极化,包含心房电活动和结构特性;异常P波参数已被证明对于评估患者是否已患有或即将发生房颤具有重要价值。然而,由于技术和生物学原因,心电图中检测心房活动具有挑战性。现随着机器学习和深度学习技术在房颤检测中不断开展,若能发展创新的方法聚焦于检测P波可能提供更加准确的分类器,且不损害模型透明度。该综述主要探讨了人工智能技术结合P波参数特征在房颤检测中的价值。

Atrial fibrillation is the most common arrhythmia.P wave on ECG represents atrial depolarization,including atrial electrical activity and structural characteristics.Abnormal P wave parameters have been shown to be of great value in assessing whether patients have or are about to have atrial fibrillation.However,the detection of atrial activity in electrocardiogram is challenging due to technical and biological reasons.With the continuous development of machine learning and deep learning technologies in atrial fibrillation detection,the development of innovative methods focusing on detecting P waves may provide a more accurate classifier without compromising model transparency.This review mainly explored the value of artificial intelligence technology combined with P-wave parameter characteristics in the detection of atrial fibrillation.