基于自适应心率搜索模型的心率提取算法

Heart rate extraction algorithm based on adaptive heart rate search model

光容积描记图(PPG)是一种低成本、无创的心率测量技术,目前已经广泛应用于智能可穿戴设备中。然而PPG信号本身极易受到运动噪声的干扰,导致在剧烈运动状态下的心率计算准确率较低。针对这一问题,本文提出一种基于自适应心率搜索模型的心率提取算法。算法首先对加速度信号以及PPG信号进行预处理,之后分别从两种信号中提取出步频信息与历史心率信息,根据两种信息与当前心率间的关系建立自适应模型,以此动态输出心率在频域的可能出现范围,通过缩小真实心率在频域的查找范围来排除剧烈噪声干扰。在2015年IEEE信号处理杯十二组公开数据中,本文算法结果平均绝对误差为1.12次/分(皮尔森系数:0.996;一致性误差:-0.184次/分);在十组自测运动数据中,本文算法结果平均绝对误差为3.19次/分(皮尔森系数:0.990;一致性误差:1.327次/分)。结合实验结果来看,本文提出的算法能有效提取运动噪声干扰下的心率信息,在智能臂带设备中具有投入使用的潜能。

Photoplethysmography(PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute(bpm)(Pearson correlation coefficient: 0.996;consistency error:-0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm(Pearson correlation coefficient: 0.990;consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.