Gilani Mehrin, Eklund J Mikael, Makrehchi Masoud
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:3461-3464. doi: 10.1109/EMBC.2016.7591473.
Atrial fibrillation (AF) is one of the most common life-threatening arrhythmia affecting around six million adults in the US. Typical detection of AF requires tedious and manual analysis of ECG which can often delay medical intervention. With the advent of wearable devices that can accurately record the time interval between two heartbeats (RR interval), automated and timely detection of AF is now possible. In this paper, we engineer novel heart rate variability features based on linear and non-linear dynamics of RR intervals. Unlike complex features extracted from ECG signals, these features can be easily obtained using wearable sensors. We propose automated classifiers to detect AF episodes and also compare the performance of different classifiers. Our proposed classifier has a very high sensitivity (98%) and specificity (95%) and outperforms prior published works.
心房颤动（AF）是最常见的危及生命的心律失常之一，在美国约有600万成年人受其影响。典型的房颤检测需要对心电图进行繁琐的人工分析，这常常会延误医疗干预。随着能够精确记录两次心跳之间时间间隔（RR间期）的可穿戴设备的出现，现在可以实现房颤的自动及时检测。在本文中，我们基于RR间期的线性和非线性动力学设计了新颖的心率变异性特征。与从心电图信号中提取的复杂特征不同，这些特征可以使用可穿戴传感器轻松获得。我们提出了自动分类器来检测房颤发作，并比较不同分类器的性能。我们提出的分类器具有非常高的灵敏度（98%）和特异性（95%），优于先前发表的研究成果。
