Abstract
The respiratory signal is a critical index of cardiopulmonary function. In this paper, we implement the polyvinylidene fluoride (PVDF) sensor to collect the data of pulse waves and reference respiration signals. The correlations between the major feature values and breaths are investigated and presented. As a result, several feature values exhibit relatively good correlations with reference respiratory amplitude. The improvements of Kalman Filter for the respiratory signals extracted from feature value variances are also introduced. Moreover, we display the comparisons with low-pass filtering, Wavelet filtering, and ensemble empirical mode decomposition (EEMD) method. Using the method of identifying error breaths, the error rate of the new method is about 4.146%. The new method is feasible for real-time applications at quiet state.
Highlights
As a critical physiological signal, respiratory signals, such as respiratory disorders, have been widely studied [1], [2]
Reference [6] presents a wavelet-based algorithm for respiratory rate estimation from photoplethysmogram (PPG) signal; Reference [7] uses Doppler radar sensor for respiration estimation by ensemble empirical mode decomposition (EEMD) method
Kalman Filter can be used for data fusion of similar feature value sequences, or for filtering one single feature value, to improve the respiratory curve extracted from pulse wave
Summary
As a critical physiological signal, respiratory signals, such as respiratory disorders, have been widely studied [1], [2]. Reference [6] presents a wavelet-based algorithm for respiratory rate estimation from photoplethysmogram (PPG) signal; Reference [7] uses Doppler radar sensor for respiration estimation by ensemble empirical mode decomposition (EEMD) method. Reference [11] extracts respiratory signals from electrocardiograph (ECG) and PPG, and discusses the application of Kalman Filter in data fusion of the two breathing signals for optimization, but the data acquasition is much complex. As the Traditional Chinese Medicine (TCM) diagnosis is mainly based on pressure pulse signal, this paper discusses the feature value variation of radial pressure pulse wave with respiration. This paper will discuss the feasibility of using Kalman filter on one or multiple feature values to optimize the extracted respiratory signals.
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