Algorithm for extracting respiratory signals based on electrocardiogram signals(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2019年第4期
- Page:
- 462-469
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- Algorithm for extracting respiratory signals based on electrocardiogram signals
- Author(s):
- JIANG Lian; CHEN Zhaoxue
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
- Keywords:
- ; Keywords: electrocardiogram signal; respiratory signal; cubic spline interpolation; wavelet transform; independent component analysis
- PACS:
- R318.04；TN911.7
- DOI:
- DOI:10.3969/j.issn.1005-202X.2019.04.018
- Abstract:
- Abstract: Objective To propose a new algorithm of electrocardiogram (ECG)-derived respiratory signals detection for achieving the detection of a variety of physiological parameters, reducing the complexity of equipment as well as relieving the discomfort of patients. Methods The Pan & Tompkins algorithm was used to detect the feature points of R and S waves in ECG signals. Subsequently, the obtained feature points were processed with cubic spline interpolation and re-sampling methods for obtaining the fitted R and S wave sequences at the same positions. The wavelet transform method was applied to reconstruct a respiratory signal sequence. Finally, a hybrid matrix composed of R wave sequence, S wave sequence, the reconstructed respiratory signal sequence and the original signal sequence was formed and then processed with independent component analysis to extract two source signal sequences with respiratory information, namely Z1 sequence and Z2 sequence. MATLAB software was used to verify the results of the proposed algorithm and compare the results with those obtained with other algorithms. Results The error was smaller when detecting the number of breaths per minute of a human body in time domain. The two source signal sequences extracted with independent component analysis had good correlations with other respiratory waveforms, with an average similarity higher than 95.94%. Conclusion The proposed algorithm can meet the needs of respiratory parameter detection and is proved to be effective.
Last Update: 2019-04-23