Chia-Chi Chang

QUANTITATIVE NON-STATIONARY ASSESSMENT OF CEREBRAL HEMODYNAMICS BY EMPIRICAL MODE DECOMPOSITION OF CEREBRAL DOPPLER FLOW VELOCITY

Dynamic regulation of cerebral circulation involves complex interaction between cardiovascular, respiratory, and autonomic nervous systems. Evaluating cerebral hemodynamics by using traditional statistic- and linear-based methods would underestimate or miss important information. Complementary ensemble empirical mode decomposition (CEEMD) has great capability of adaptive feature extraction from non-linear and non-stationary data without distortion. This study applied CEEMD for assessment of cerebral hemodynamics in response to physiologic challenges including paced 6-cycle breathing, hyperventilation, 7% CO2 breathing and head-up tilting test in twelve healthy subjects. Intrinsic mode functions (IMFs) were extracted from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) signals, and was quantified by logarithmic averaged period and logarithmic energy density. The IMFs were able to show characteristics of ABP and CBFV waveform morphology in beat-to-beat timescale and in long-term trend scale. The changes in averaged period and energy density derived from IMFs were helpful for qualitative and quantitative assessment of ABP and CBFV responses to physiologic challenges. CEEMD is a promising method for assessing non-stationary components of systemic and cerebral hemodynamics.

Assessment of autonomic nervous system by using empirical mode decomposition-based reflection wave analysis during non-stationary conditions

Arterial blood pressure (ABP) is an important indicator of cardiovascular circulation and presents various intrinsic regulations. It has been found that the intrinsic characteristics of blood vessels can be assessed quantitatively by ABP analysis (called reflection wave analysis (RWA)), but conventional RWA is insufficient for assessment during non-stationary conditions, such as the Valsalva maneuver. Recently, a novel adaptive method called empirical mode decomposition (EMD) was proposed for non-stationary data analysis. This study proposed a RWA algorithm based on EMD (EMD-RWA). A total of 51 subjects participated in this study, including 39 healthy subjects and 12 patients with autonomic nervous system (ANS) dysfunction. The results showed that EMD-RWA provided a reliable estimation of reflection time in baseline and head-up tilt (HUT). Moreover, the estimated reflection time is able to assess the ANS function non-invasively, both in normal, healthy subjects and in the patients with ANS dysfunction. EMD-RWA provides a new approach for reflection time estimation in non-stationary conditions, and also helps with non-invasive ANS assessment.

Depicting Respiratory Characteristics of Blood Pressure Signal by UsingEmpirical Mode Decomposition

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 interpretation of very high frequency band of instantaneous pulse rate variability during paced respiration

BackgroundPulse rate (PR) indicates heart beat rhythm and contains various intrinsic characteristics of peripheral regulation. Pulse rate variability (PRV) is a reliable method to assess autonomic nervous system function quantitatively as an effective alternative to heart rate variability. However, the frequency range of PRV is limited by the temporal resolution of PR based on heart rate and it is further restricted the exploration of optimal autoregulation frequency based on spectral analysis.MethodsRecently, a new novel method, called instantaneous PRV (iPRV), was proposed. iPRV breaks the limitation of temporal resolution and extends the frequency band. Moreover, iPRV provides a new frequency band, called very high frequency band (VHF; 0.4-0.9 Hz).ResultsThe results showed that the VHF indicated the influences of respiratory maneuvers (paced respiration at 6-cycle and 30-cycle) and the nonstationary condition (head-up tilt; HUT).ConclusionsVHF is as a potential indication of autoregulation in higher frequency range and with peripheral regulation. It helps for the frequency exploration of cardiovascular autoregulation.

The Adaptive Frequency Band for Blood Pressure Variability Measurementduring Nonstationary Conditions

Background: The variation of blood pressure indicates the status of cardiovascular circulation. The spectral analysis of blood pressure variability (BPV) provides a way to quantitatively assess the variations by specific fixed frequency band. Blood pressure contains various non-stationary fluctuations and varies individually. It is hard to assess the non-stationary characteristics based on stationary method. Method: Recently, a novel adaptive extraction method, called empirical mode decomposition (EMD), was proposed and is capable to extract the non-stationary intrinsic trends from blood pressure waveform. Results: The results showed that the non-stationary intrinsic trends extracted by EMD are high correlated to the power in conventional fixed frequency band (r>0.7). Conclusions: This study examined the potential usage of EMD on BPV measurement and provided the reliable estimation of BPV based on adaptive frequency band. Moreover, the main frequency of the non-stationary trend can be evaluated by this method. It helps for cardiovascular studies and the optimal frequency band exploration.

Reflection wave analysis based on ensemble empirical mode decomposition

In recent studies, the reflection waveform analysis (RWA) in arterial blood pressure (ABP) is the important method for cardiovascular system assessment. But conventional RWA contains some limitations during several clinical experiments, such as the unrecognizable reflection waveform morphology during Valsalva maneuver (VM). This study proposed a new RWA based on ensemble empirical mode decomposition (EEMD), which extracted the intrinsic feature of ABP waveform, including reflection wave and trend of ABP. Furthermore, the reflection time (Tr) was computed by EEMD-based RWA and the results of agreement test showed that Tr can be estimated with unrecognizable reflection waveform. It helps for the cardiovascular system assessment during specific physiological challenges, such as VM. Moreover, it helps for cardiovascular auto-regulation studies for reflection wave monitoring by VM study.

Can Very High Frequency Instantaneous Pulse Rate Variability Serve as an Obvious Indicator of Peripheral Circulation

HRV (heart rate variability) is a general noninvasive method for indicating the activities of ANS (autonomic nervous system). The surrogate of HRV is PRV (pulse rate variability), where the variability of resting PPG (photo plethysmo graphy) indicates the activities of ANS and peripheral circulation. However, the quick responses of thermoregulation and limb movement are restrictedly performed since of the beat-to-beat timescale property of PRV. Recently, iPRV (instantaneous pulse rate variability) has been developed to break the limitation. The iPRV adopts empirical mode decomposition for noise reduction and estimates the instantaneous period for higher time resolution. Thus, an ultra-frequency band called VHF (very high frequency) was held. The aim of this study is to concentrate the iPRV presentation on fever adolescent who is possibly under immature body regulation. Thirty participates, whose age were 7 to 18 years old, were recruited in pediatric clinic. The resting PPG signals were acquired for 10 minutes in a supine position. The analysis results show the conventional ranges of iPRV also observe the activities of ANS. As well, the modified indices including VHF are significant difference (p-value < 0.05) between fever and feverless symptoms. Based on the statistical evidence, this study provides the potential of the indicator of thermoregulation on peripheral circulation.

Frequency Domain Analysis for Assessing Fluid Responsiveness by Using Instantaneous Pulse Rate Variability

In the ICU, fluid therapy is conventional strategy for the patient in shock. However, only half of ICU patients have well-responses to fluid therapy, and fluid loading in non-responsive patient delays definitive therapy. Prediction of fluid responsiveness (FR) has become intense topic in clinic. Most of conventional FR prediction method based on time domain analysis, and it is limited ability to indicate FR. This study proposed a method which predicts FR based on frequency domain analysis, named instantaneous pulse rate variability (iPRV). iPRV provides a new indication in very high frequency (VHF) range (0.4-0.8Hz) of spectrum for peripheral responses. Twenty six healthy subjects participated this study and photoplethysmography signal was recorded in supine baseline, during head-up tilt (HUT), and passive leg raising (PLR), which induces variation of venous return and helps for quantitative assessment of FR individually. The result showed the spectral power of VHF decreased during HUT (573.96±756.36 ms2 in baseline; 348.00±434.92 ms2 in HUT) and increased during PLR (573.96±756.36 ms2 in baseline; 718.92±973.70 ms2 in PLR), which present the compensated regulation of venous return and FR. This study provides an effective indicator for assessing FR in frequency domain and has potential to be a reliable system in ICU.

A novel index of photoplethysmography by using instantaneous pulse rate variability during non-stationary condition

Heart rate variability (HRV) and pulse rate variability (PRV) have been widely used as an automatic nervous system (ANS) activities observation. Based on the timescale limitation of heart beat (for HRV) and pulse waveform (for PRV), one method of instantaneous pulse rate variability (iPRV) with photoplethysmography (PPG) had been proposed as a HRV and PRV surrogate during non-stationary condition in frequency domain. In this paper, the examination in time-domain PPG during passive leg raise (PLR) trial was studied. The 24 subjects pre-experiment resulted that iPRV and HRV are positive correlation (r-value = 0.680±0.099 in baseline; r-value = 0.688±0.096 in PLR). Furthermore, the iPRV containing much complex intrinsic components on higher frequency bands provides more intrinsic cardiovascular information for further assessment. This proposed examination in time-domain PPG is the possible leading way to more useful indicator for healthcare application.

Quantitative non-stationary assessment of cardiovascular diseases based on arterial blood pressure waveform by using Hilbert-Huang transform

Analysis of arterial blood pressure (ABP) signal provides valuable information of cardiovascular function. But ABPs non-stationary characteristics are still unclear and it is hard to evaluate non-stationary characteristics by stationary analysis method, such as Fourier transform and wavelet transform. The aim of the study is to quantitatively evaluate the ABPs non-stationary characteristics and examine the feasibility of this method by the study of cardiovascular diseases patients. Eight ABPs intrinsic features were extracted by empirical mode decomposition (EMD) and were evaluated by averaged period and energy density according to the spectral integration method. In the study, 33 subjects (15 healthy subjects, 7 patients with autonomic failure, 11 patients with orthostatic tachycardia syndrome) participated in the passive head-up tilt experiment. The results showed that the characteristics of ABP fluctuation were clearly presented by the changes of IMFs and corresponding averaged periods and energy density. This method was feasible for the qualitative and quantitative assessment of ABP in normal healthy subjects and patients with impaired autonomic regulation of cardiovascular system.