Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA.
Sensors (Basel). 2020 Oct 5;20(19):5683. doi: 10.3390/s20195683.
We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.
我们开发了一种使用智能手表采集的光电容积脉搏波（PPG）数据检测房性早搏（PAC）和室性早搏（PVC）的算法。我们的 PAC/PVC 检测算法由一系列算法组成，这些算法结合起来可以区分各种心律失常。一种新颖的向量相似性方法被用于增强 Poincaré 图方法的 PAC/PVC 检测结果。使用我们的自动运动和噪声伪影检测方法的新的 PAC/PVC 检测算法在心房颤动（AF）患者中检测到 86%的敏感性，而在包括正常窦性节律（NSR）患者时总敏感性为 67%。对于包含 NSR 和 AF 患者的综合数据，使用我们的自动运动和噪声伪影检测方法的 PAC/PVC 检测的特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确性值分别为 97%、81%、94%和 92%。此外，在将 AF 检测与 PAC/PVC 结合和不结合进行比较时，敏感性和特异性从 94.55%增加到 98.18%，从 95.75%增加到 97.90%。对于额外的独立测试数据，我们使用了两个数据集：一个是我们正在进行的临床研究中采集的智能手表 PPG 数据集，另一个是来自 Medical Information Mart for Intensive Care III 数据库的脉搏血氧仪 PPG 数据集。这两个数据集的独立测试的 PAC/PVC 分类结果在敏感性、特异性、PPV、NPV 和准确性方面均高于 92%。提出的结合使用智能手表 PPG 记录检测 PAC 和 PVC 的方法最终可以提高 AF 检测的准确性。这是第一项使用智能手表 PPG 记录检测 PAC 和 PVC 的研究之一。该方法可能具有临床重要性，因为这种增强的能力可以减少 AF 的假阳性检测，尤其是对于那些经常发生 PAC/PVC 的 NSR 患者。
