一种实时 PPG 波峰检测方法，可在窦性节律和心律失常期间准确确定心率。
A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia.
机构信息
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
出版信息
Biosensors (Basel). 2022 Jan 29;12(2):82. doi: 10.3390/bios12020082.
OBJECTIVE
We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR.
METHODS
Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung's Gear S3 and Galaxy Watch 3.
RESULTS
The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors-30% and 66% lower-and mean heart rate and mean interbeat interval estimation errors-60% and 77% lower-when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias.
CONCLUSION
The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks.
SIGNIFICANCE
By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.
目的
我们开发了一种峰值检测算法，可通过智能手表的光电容积脉搏波（PPG）信号准确确定心率，即使存在各种心律，包括正常窦性节律（NSR）、房性早搏（PAC）、室性早搏（PVC）和心房颤动（AF）。鉴于 AF 患者对准确心率估计的临床需求，我们开发了一种新方法，与专为 NSR 设计的峰值检测算法相比，可降低心率估计误差。
方法
我们的峰值检测方法由一系列顺序算法组成，这些算法结合起来可区分上述各种心律失常。此外，还使用新的 Poincaré 图方案来区分基础 AF 心率和快速心室反应（RVR）AF，并区分 PAC/PVC 与 NSR 和 AF。算法的训练仅使用三星 Simband 智能手表数据进行，而独立测试数据则来自三星的 Gear S3 和 Galaxy Watch 3，其样本数多于训练数据。
结果
与其他七种已知对 NSR 准确的传统峰值检测算法中的最佳算法相比，新的 PPG 峰值检测算法可显著降低平均心率和逐拍心动间隔的估计误差（低 30%和 66%），并降低平均心率和平均心动间隔的估计误差（低 60%和 77%）。对于其他心律失常，我们的新 PPG 峰值检测算法是整体表现最佳的算法。
结论
该方法用于 PPG 峰值检测，可自动检测和区分不同 PPG 数据波形之间的各种心律失常，为 AF 伴有快速心室反应或 PAC/PVC 的患者提供更低的心率估计误差，并减少假阴性峰值的数量。
意义
通过在存在快速心室反应或 PAC/PVC 的 AF 情况下实现心率的准确确定，我们使临床医生能够根据 PPG 数据更准确地推荐心率控制。
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