Gordon Max, Williams Cranos
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina 27607, USA www.ncsu.edu,
Pac Symp Biocomput. 2019;24:42-53.
The accurate detection of premature ventricular contractions (PVCs) in patients is an important task in cardiac care for some patients. In some cases, the usefulness to physicians in detecting PVCs stems from their long-term correlations with dangerous heart conditions. In other cases their potential as a precursor to serious cardiac events may make their detection a useful early warning mechanism. In many of these applications, the long-term nature of the monitoring required and the infrequency of PVCs make manual observation for PVCs impractical. Existing methods of automated PVC detection suffer from drawbacks such as the need to use difficult to extract morphological features, domain-specific features, or large numbers of estimated parameters. In particular, systems using large numbers of trained parameters have the potential to require large amounts of training data and computation and may have issues generalizing due to their potential to overfit. To address some of these drawbacks, we developed a novel PVC detection algorithm based around a convolutional autoencoder to address these weaknesses and validated our method using the MIT-BIH arrhythmia database.
准确检测患者的室性早搏（PVCs）对一些患者的心脏护理而言是一项重要任务。在某些情况下，医生检测PVCs的作用源于它们与危险心脏状况的长期关联。在其他情况下，它们作为严重心脏事件先兆的可能性使得对其检测成为一种有用的早期预警机制。在许多此类应用中，所需监测的长期性以及PVCs出现的不频繁性使得人工观察PVCs不切实际。现有的自动检测PVCs的方法存在诸多缺点，比如需要使用难以提取的形态学特征、特定领域特征或大量估计参数。特别是，使用大量训练参数的系统可能需要大量训练数据和计算，并且由于存在过拟合的可能性，可能在泛化方面存在问题。为了解决其中一些缺点，我们围绕卷积自动编码器开发了一种新颖的PVC检测算法，以解决这些弱点，并使用麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库对我们的方法进行了验证。
