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| Estimation of Respiratory Rate Based on Data Fusion Using Electrocardiogram and Pulse Wave#br# |
1 Institute of Biomedical Engineering, Shandong University School of Medicine, Jinan 250012, China
2 Department of Cardiology, Jinan Fourth Hospital, Jinan 250031, China |
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Abstract 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.
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