基于心电和脉搏波数据融合的呼吸率估计

Estimation of Respiratory Rate Based on Data Fusion Using Electrocardiogram and Pulse Wave

本研究提出了一种通过心电和脉搏波提取呼吸信号并基于卡尔曼滤波的多路数据融合估计呼吸率的算法。算法分别从心电的RR间期、R波的绝对高度和脉搏波搏动周期中提取呼吸信号,利用AR模型估计呼吸率,根据信号波形、节律和频谱特征获得反映信号质量高低的质量指数,然后基于信号质量指数和卡尔曼滤波残差进行数据融合,获得融合呼吸率。14名志愿者参加了实验。结果表明,融合呼吸率比单独从心电或脉搏信号提取的呼吸率更好地反映了呼吸率的变化。与压阻式呼吸传感器提供的参考呼吸率相比,融合呼吸率误差为(-0.03±2.78)次/min,而从心电RR间期、R波的绝对高度和脉搏法提取的呼吸率的误差分别为(0.62±3.30)、(0.42±3.47)和(-0.17±2.69)次/min。总体认为,基于多路数据融合的方法可以有效避免干扰的影响,较准确地估计呼吸率。

In this paper,an algorithm was proposed to estimate respiratory rate using electrocardiogram（ECG） and pulse wave（PW）,based on multiple sensor data fusion with Kalman filter.Respiratory signal was acquired separately from the RR interval series and the R absolute amplitude of ECG,as well as from the beat cycles of PW.The respiratory rate was estimated by AR model.Signal quality indices（SQIs） were acquired based on signal waveform,rhythm and spectral features.Then the respiratory rate was fused based on SQIs and residuals of Kalman filter.The signals were sampled from 14 volunteers.The results indicate that the fused respiratory rate performs better than those derived from ECG and PW directly.Compared to the reference of a piezoresistive sensor,the estimation error of data fusion method is（-0.03±2.78） breaths/min.It is much less than both ECG-derived rates（RR interval series：（0.62±3.30）breaths/min;R absolute amplitude：（0.42±3.47） breaths/min） and PW-derived rate（-0.17±2.69） breaths/min）.In conclusion,the multiple data fusion method is noise-resisted and suitable for respiratory rate estimation.