Research on heart rate extraction algorithm in motion state based on normalized least mean square combining ensemble empirical mode decomposition.
10.7507/1001-5515.201812022
- Author:
Duyan GENG
1
,
2
;
Jie ZHAO
2
;
Chenxu WANG
2
;
Jiaji DONG
2
;
Qi NING
2
;
Yan WANG
2
Author Information
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China.
- Publication Type:Journal Article
- Keywords:
ensemble empirical mode decomposition;
normalized least mean square adaptive filtering;
permutation entropy;
photoplethysmographic
- From:
Journal of Biomedical Engineering
2020;37(1):71-79
- CountryChina
- Language:Chinese
-
Abstract:
In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.
