Zhou Xue, Zhu Xin, Nakamura Keijiro, Mahito Noro
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2551-2554. doi: 10.1109/EMBC.2018.8512858.
Premature ventricular contraction (PVC) is usually considered to as benign arrhythmia in the absence of structural heart diseases. However, frequent premature beats may significantly increase the risk of heart failure and even death by an arrhythmia-induced cardiomyopathy. Therefore, high PVC counts have been considered as an approach to predict the risk of severe arrhythmias. Progress of wearable devices provides a convenient tool for the detection of premature contraction in casual life. Considering the huge quantities of data recorded by wearable devices, reliable and low-cost data analysis programs should be developed for real time PVC detection. In this research, we use recurrent neural networks with, long short-term memory to detect PVC. Through validating with MIT-BIH arrhythmia database, the detection accuracy of this method is 96%-99%.
室性早搏（PVC）在无结构性心脏病时通常被认为是良性心律失常。然而，频发早搏可能会因心律失常性心肌病而显著增加心力衰竭甚至死亡的风险。因此，高PVC计数已被视为预测严重心律失常风险的一种方法。可穿戴设备的发展为在日常生活中检测早搏提供了便利工具。考虑到可穿戴设备记录的海量数据，应开发可靠且低成本的数据分析程序用于实时PVC检测。在本研究中，我们使用带有长短期记忆的循环神经网络来检测PVC。通过使用麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库进行验证，该方法的检测准确率为96% - 99%。
