Chong Jo Woon, Esa Nada, McManus David D, Chon Ki H
IEEE J Biomed Health Inform. 2015 May;19(3):815-24. doi: 10.1109/JBHI.2015.2418195. Epub 2015 Mar 31.
We hypothesize that our smartphone-based arrhythmia discrimination algorithm with data acquisition approach reliably differentiates between normal sinus rhythm (NSR), atrial fibrillation (AF), premature ventricular contractions (PVCs) and premature atrial contraction (PACs) in a diverse group of patients having these common arrhythmias. We combine root mean square of successive RR differences and Shannon entropy with Poincare plot (or turning point ratio method) and pulse rise and fall times to increase the sensitivity of AF discrimination and add new capabilities of PVC and PAC identification. To investigate the capability of the smartphone-based algorithm for arrhythmia discrimination, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion, as well as seven participants with PACs and four with PVCs were recruited. Using a smartphone, we collected 2-min pulsatile time series from each recruited subject. This clinical application results show that the proposed method detects NSR with specificity of 0.9886, and discriminates PVCs and PACs from AF with sensitivities of 0.9684 and 0.9783, respectively.
我们假设，我们基于智能手机的心律失常判别算法及其数据采集方法，能够在患有这些常见心律失常的不同患者群体中，可靠地区分正常窦性心律（NSR）、心房颤动（AF）、室性早搏（PVCs）和房性早搏（PACs）。我们将连续RR间期差值的均方根、香农熵与庞加莱图（或转折点比率法）以及脉搏上升和下降时间相结合，以提高房颤判别的敏感性，并增加识别室性早搏和房性早搏的新能力。为了研究基于智能手机的算法对心律失常的判别能力，我们招募了99名受试者，其中包括88名基线时患有房颤且在电复律后转为正常窦性心律的研究参与者，以及7名患有房性早搏和4名患有室性早搏的参与者。我们使用智能手机从每个招募的受试者收集了2分钟的搏动时间序列。该临床应用结果表明，所提出的方法检测正常窦性心律的特异性为0.9886，区分室性早搏和房性早搏与房颤的敏感性分别为0.9684和0.9783。
