Motion artifacts (MA) are potent sources of noise in wearable photoplethysmography (PPG) signals and can impact the estimation of heart rate (HR) of an individual. In this paper, a method using adaptive neural network filters (ANNF) is proposed for accurate estimation of HR using dual channel PPG signals and simultaneous, three - dimensional acceleration signals. The MA cancellation method using ANNF, utilizes acceleration data as input signal. The PPG signals serve as a target, while the error is the clean PPG signal. The proposed method also includes a post-processing smoothing and median filter which improves the HR estimation. The reason for this approach is that the acceleration signal in wearables are only within 3% of the ground truth value. Experimental results on datasets recorded from 12 subjects, publicly available, showed that the proposed algorithm achieves an absolute error of 1.15 beats per minute (BPM). The results also confirm that the proposed method is highly resilient to motion artifacts and maintains high accuracy for PPG estimation and compares favorably against other methods.

中文翻译：

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使用自适应神经网络滤波的同步加速度测量 PPG 信号的心率估计

运动伪影 (MA) 是可穿戴光电容积脉搏波 (PPG) 信号中的潜在噪声源，会影响个人心率 (HR) 的估计。在本文中，提出了一种使用自适应神经网络滤波器 (ANNF) 的方法，用于使用双通道 PPG 信号和同时的三维加速度信号准确估计 HR。使用 ANNF 的 MA 抵消方法，利用加速度数据作为输入信号。PPG 信号作为目标，而错误是干净的 PPG 信号。所提出的方法还包括改进HR估计的后处理平滑和中值滤波器。这种方法的原因是可穿戴设备中的加速度信号仅在真实值的 3% 以内。从 12 个主题记录的数据集的实验结果，公开可用，表明所提出的算法实现了每分钟 1.15 次心跳 (BPM) 的绝对误差。结果还证实，所提出的方法对运动伪影具有高度弹性，并保持 PPG 估计的高精度，并且与其他方法相比具有优势。