Depicting Respiratory Characteristics of Blood Pressure Signal by Using Empirical Mode Decomposition

Abstract

Aim: To explore adequate parameters for EMD of ABP signal; to determine the intrinsic characteristics of ABP waveform through the analysis of IMFs' averaged period and its energy density; to examine the effect of different respiration patterns on IMFs extracted from ABP waveform by CEEMD.Arterial blood pressure (ABP) reflects cardiac function, vessel compliance, and cardiorespiratory interaction and ABP analysis provides the estimators of this physiological information. But it is inconvenient for quantitative ABP assessment due to several influences, such as respiration. Recently, a novel adaptive method, called empirical mode decomposition (EMD), was proposed, and it was useful for non-stationary intrinsic characteristics extraction. Though some literatures examined that EMD helps for physiological signal analysis study, the method applied for ABP signal still needs further investigation. This study proposed a standard procedure of specific EMD for ABP intrinsic characterization during spontaneous breathing, 6-cycle breathing, and hyperventilation. The extracted components, called intrinsic mode functions (IMFs), were determined with the examined parameters, including ensemble number, added noise, and the stop criterion. The IMFs of ABP signal were categorized into five major intrinsic components, including the noise and irregular fluctuation (IMF1), beat-to-beat cardiac intervals (IMF2), characteristics of pressure waveform morphology (IMF3), base beat (IMF4), and respiratory related fluctuation (IMF5 and IMF6).The results showd that the characteristics of IMFs were quantified by averaged period and corresponding energy density with good reproducibility. The proposed algorithm produced meaningful IMFs representing the cardiac rhythm, intrinsic waveform mophology, and the intrinsic influence of respiration fluctuations. EMD helps for analyzing the underlying mechanisms of control processes, including cardiorespiratory coupling and interactions among organ systems at multiple time scales.

The Adaptive Frequency Band for Blood Pressure Variability Measurement during Nonstationary Conditions

“…It had been examined that the non-stationary characteristics of IMFs can be assessed by the spectral integration method with averaged period and corresponding energy density [14]. This method was originally used to study the characterization of intrinsic white noise by evaluating the distribution of IMFs' logarithmic averaged period and corresponding logarithmic energy density [15].…”

“…The spectral analysis, such as FFT or DWT, was performed for quantitative assessment according to the specific fixed frequency band. In adaptive BPV based on EMD, the non-stationary intrinsic trends (IMF5-7 in this study) were extracted from ABP signal by EMD [14]. Each non-stationary intrinsic trend was assessed by the spectral analysis with averaged frequency and corresponding energy density for the comparison and the examination.…”

“…Though there were several studies examined that the potential usage of EMD in BPV analysis, the evaluation was still limited by the time resolution of beat-tobeat blood pressure [13]. It had been examined that the non-stationary trend can be extracted by EMD and be quantitatively assessed by the spectral integration method, including the calculation of averaged period and energy density [14]. By using this method, the assessment of BPV becomes adaptive, called adaptive BPV, which is based on adaptive trend extraction rather than the specific fixed frequency band.…”