Fractal Properties of Heart Rate Dynamics: A New Biomarker for Anesthesia-Biphasic Changes in General Anesthesia and Decrease in Spinal Anesthesia.

Processed electroencephalogram (EEG) has been considered a useful tool for measuring the depth of anesthesia (DOA). However, because of its inability to detect the activities of the brain stem and spinal cord responsible for most of the vital signs, a new biomarker for measuring the multidimensional activities of the central nervous system under anesthesia is required. Detrended fluctuation analysis (DFA) is a new technique for detecting the scaling properties of nonstationary heart rate (HR) behavior. This study investigated the changes in fractal properties of heart rate variability (HRV), a nonlinear analysis, under intravenous propofol, inhalational desflurane, and spinal anesthesia. We compared the DFA method with traditional spectral analysis to evaluate its potential as an alternative biomarker under different levels of anesthesia. Eighty patients receiving elective procedures were randomly allocated different anesthesia. HRV was measured with spectral analysis and DFA short-term (4-11 beats) scaling exponent (DFAα1). An increase in DFAα1 followed by a decrease at higher concentrations during propofol or desflurane anesthesia is observed. Spinal anesthesia decreased the DFAα1 and low-/high-frequency ratio (LF/HF ratio). DFAα1 of HRV is a sensitive and specific method for distinguishing changes from baseline to anesthesia state. The DFAα1 provides a potential real-time biomarker to measure HRV as one of the multiple dimensions of the DOA.

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics.

The nonsinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single-cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time series using masked empirical mode decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency, and phase) with instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase grid space, makes it possible to compare cycles of different durations and shapes. "Normalized shapes" can then be constructed with high temporal detail while accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks nonsinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average yet exhibit high variability on a cycle-by-cycle basis. We show how principal component analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration, and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of inquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.NEW & NOTEWORTHY We propose a novel analysis approach quantifying nonsinusoidal waveform shape. The approach isolates oscillations with empirical mode decomposition before waveform shape is quantified using phase-aligned instantaneous frequency. This characterizes the full shape profile of individual cycles while accounting for between-cycle differences in duration, amplitude, and timing. We validated in simulations before applying to identify a range of data-driven nonsinusoidal shape motifs in hippocampal theta oscillations.

The role of autonomic dysfunction in predicting 1-year mortality after liver transplantation.

BACKGROUND & AIMS: Model for end-stage liver disease (MELD) score has been extensively used to prioritize patients for liver transplantation and determine their prognosis, but with limited predictive value. Autonomic dysfunction may correlate with increased mortality after liver transplant. In this study, two autonomic biomarkers, complexity and deceleration capacity, were added to the predicting model for 1-year mortality after liver transplantation. METHODS: In all, 30 patients with end-stage liver diseases awaiting liver transplantation were included. Complexity and deceleration capacity were calculated by multi-scale entropy and phase-rectified signal averaging, respectively. Different combinations of autonomic factors and MELD score were used to predict mortality rate of liver transplant after 1-year follow-up. Receiver-operating characteristics curve analysis was performed to determine clinical predictability. Area under the receiver-operating characteristics curve represents the overall accuracy. RESULTS: The 1-year mortality rate was 16.7% (5/30). The overall accuracy of MELD score used for predicting mortality after liver transplantation was 0.752. By adding complexity and deceleration capacity into the predicting model, the accuracy increased to 0.912. Notably, the accuracy of the prediction using complexity and deceleration capacity alone was 0.912. CONCLUSION: Complexity and deceleration capacity, which represent different dynamical properties of a human autonomic system, are critical factors for predicting mortality rate of liver transplantation. We recommend that these pre-operative autonomic factors may be helpful as critical adjuncts to predicting model of mortality rate in prioritizing organ allocation.

On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data.

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert-Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time-frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.

Recovery of cardiac electrophysiological alterations by heart rate complexity based on multiscale entropy following liver transplantation.

Autonomic nervous dysfunction is a known cardiac sequalae in patients with end-stage liver disease and is associated with a poor prognosis. Heart rate analysis using nonlinear models such as multiscale entropy (MSE) or complexity may identify marked changes in these patients where conventional heart rate variability (HRV) measurements do not. To investigate the application of heart rate complexity (HRC) based on MSE in liver transplantation settings. Thirty adult recipients of elective living donor liver transplantation were enrolled. HRV parameters using conventional HRV analysis and HRC analysis were obtained at the following time points: (1) 1 day before surgery, (2) postoperative day (POD) 7, (3) POD 14, (4) POD 90, and (5) POD 180. Preoperatively, patients with MELD score ≥ 25 had significantly lower HRC compared to patients with lower MELD scores. This difference in HRC disappeared by POD 7 following liver transplantation and subsequent analyses at POD 90 and 180 continued to show no significant difference. Our results indicated a significant negative correlation between HRC based on MSE analysis and liver disease severity preoperatively, which may be more sensitive than conventional linear HRV analysis. HRC in patients with MELD score ≧ 25 improved over time and became comparable to those with MELD < 25 as early as in 7 days.

Deceleration capacity of heart rate variability as a predictor of sedation related hypotension.

High risk and geriatric patients are supposed to suffer higher risks of hypotension underwent painless endoscopic procedures. This study evaluated different biomarkers associated with hypotension in off-site patients and aimed to determine the most relevant risk factors in space and monitoring limited environment. The inclusions of this observational cohort study underwent complex endoscopic procedures were sedated with age-adjusted doses of target-controlled infusion of propofol. The following pre-sedative parameters were analysed: time domain, frequency domain, and Deceleration capacity (DC) of heart rate variability, estimated cardiac output data and the index of cardiac contractility from the cardiometer. Patients were divided into hypotension group (blood pressure < 90 mmHg or a > 35% decrease) and non-hypotension group according to peri-sedative blood pressure, regression analysis is used to examine the association between factors and hypotension. Total data from 178 patients (age range: 33-94 years) were analysed. Age was not significantly different between the hypotension and non-hypotension groups (p = 0.978). Among all the factors, DC was most associated with hypotension (p = 0.05), better than cardiometer, age, and ASA status. In conclusion, DC, which can be interpreted as the indicator of parasympathetic activity and was significantly and negatively correlated with sedation-related hypotension. Pre-sedative measuring DC from routine ECG monitoring is simple and cost-effective and should be added to haemodynamic monitoring in the endoscopic room.

Frontoparietal Beta Amplitude Modulation and its Interareal Cross-frequency Coupling in Visual Working Memory.

Visual working memory (VWM) relies on sustained neural activities that code information via various oscillatory frequencies. Previous studies, however, have emphasized time-frequency power changes, while overlooking the possibility that rhythmic amplitude variations can also code frequency-specific VWM information in a completely different dimension. Here, we employed the recently-developed Holo-Hilbert spectral analysis to characterize such nonlinear amplitude modulation(s) (AM) underlying VWM in the frontoparietal systems. We found that the strength of AM in mid-frontal beta and gamma oscillations during late VWM maintenance and VWM retrieval correlated with people's VWM performance. When behavioral performance was altered with transcranial electric stimulation, AM power changes during late VWM maintenance in beta, but not gamma, tracked participants' VWM variations. This beta AM likely codes information by varying its amplitude in theta period for long-range propagation, as our connectivity analysis revealed that interareal theta-beta couplings-bidirectional between mid-frontal and right-parietal during VWM maintenance and unidirectional from right-parietal to left-middle-occipital during late VWM maintenance and retrieval-underpins VWM performance and individual differences.

Revealing the Dynamic Nature of Amplitude Modulated Neural Entrainment With Holo-Hilbert Spectral Analysis.

Patterns in external sensory stimuli can rapidly entrain neuronally generated oscillations observed in electrophysiological data. Here, we manipulated the temporal dynamics of visual stimuli with cross-frequency coupling (CFC) characteristics to generate steady-state visual evoked potentials (SSVEPs). Although CFC plays a pivotal role in neural communication, some cases reporting CFC may be false positives due to non-sinusoidal oscillations that can generate artificially inflated coupling values. Additionally, temporal characteristics of dynamic and non-linear neural oscillations cannot be fully derived with conventional Fourier-based analyses mainly due to trade off of temporal resolution for frequency precision. In an attempt to resolve these limitations of linear analytical methods, Holo-Hilbert Spectral Analysis (HHSA) was investigated as a potential approach for examination of non-linear and non-stationary CFC dynamics in this study. Results from both simulation and SSVEPs demonstrated that temporal dynamic and non-linear CFC features can be revealed with HHSA. Specifically, the results of simulation showed that the HHSA is less affected by the non-sinusoidal oscillation and showed possible cross frequency interactions embedded in the simulation without any a priori assumptions. In the SSVEPs, we found that the time-varying cross-frequency interaction and the bidirectional coupling between delta and alpha/beta bands can be observed using HHSA, confirming dynamic physiological signatures of neural entrainment related to cross-frequency coupling. These findings not only validate the efficacy of the HHSA in revealing the natural characteristics of signals, but also shed new light on further applications in analysis of brain electrophysiological data with the aim of understanding the functional roles of neuronal oscillation in various cognitive functions.

Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition.

How to quantify the complexity of a physiological signal is a crucial issue for verifying the underlying mechanism of a physiological system. The original algorithm of detrended fluctuation analysis (DFA) quantifies the complexity of signals using the DFA scaling exponent. However, the DFA scaling exponent is suitable only for an integrated time series but not the original signal. Moreover, the method of least squares line is a simple detrending operation. Thus, the analysis results of the original DFA are not sufficient to verify the underlying mechanism of physiological signals. In this study, we apply an innovative timescale-adaptive algorithm of empirical mode decomposition (EMD) as the detrending operation for the modified DFA algorithm. We also propose a two-parameter scale of randomness for DFA to replace the DFA scaling exponent. Finally, we apply this modified algorithm to the database of human heartbeat interval from Physiobank, and it performs well in identifying characteristics of heartbeat interval caused by the effects of aging and of illness.

Multi-timescale phase-amplitude couplings in transitions of anesthetic-induced unconsciousness.

Under general anesthesia (GA), advanced analysis methods enhance the awareness of the electroencephalography (EEG) signature of transitions from consciousness to unconsciousness. For nonlinear and nonstationary signals, empirical mode decomposition (EMD) works as a dyadic filter bank to reserve local dynamical properties in decomposed components. Moreover, cross-frequency phase-amplitude coupling analysis illustrates that the coupling between the phase of low-frequency components and the amplitude of high-frequency components is correlated with the brain functions of sensory detection, working memory, consciousness, and attentional selection. To improve the functions of phase-amplitude coupling analysis, we utilized a multi-timescale approach based on EMD to assess changes in brain functions in anesthetic-induced unconsciousness using a measure of phase-amplitude coupling. Two groups of patients received two different anesthetic recipes (with or without ketamine) during the induction period of GA. Long-term (low-frequency) coupling represented a common transitional process of brain functions from consciousness to unconsciousness with a decay trend in both groups. By contrast, short-term coupling reflected a reverse trend to long-term coupling. However, the measures of short-term coupling also reflected a higher degree of coupling for the group with ketamine compared with that without ketamine. In addition, the coupling phase is a factor of interest. The phases for different combinations of coupling components showed significant changes in anesthetic-induced unconsciousness. The coupling between the delta-band phase and the theta-band amplitude changed from in-phase to out-phase coupling during the induction process from consciousness to unconsciousness. The changes in the coupling phase in EEG signals were abrupt and sensitive in anesthetic-induced unconsciousness.

Unraveling nonlinear electrophysiologic processes in the human visual system with full dimension spectral analysis.

Natural sensory signals have nonlinear structures dynamically composed of the carrier frequencies and the variation of the amplitude (i.e., envelope). How the human brain processes the envelope information is still poorly understood, largely due to the conventional analysis failing to quantify it directly. Here, we used a recently developed method, Holo-Hilbert spectral analysis, and steady-state visually evoked potential collected using electroencephalography (EEG) recordings to investigate how the human visual system processes the envelope of amplitude-modulated signals, in this case with a 14 Hz carrier and a 2 Hz envelope. The EEG results demonstrated that in addition to the fundamental stimulus frequencies, 4 Hz amplitude modulation residing in 14 Hz carrier and a broad range of carrier frequencies covering from 8 to 32 Hz modulated by 2 Hz amplitude modulation are also found in the two-dimensional frequency spectrum, which have not yet been recognized before. The envelope of the stimulus is also found to dominantly modulate the response to the incoming signal. The findings thus reveal that the electrophysiological response to amplitude-modulated stimuli is more complex than could be revealed by, for example, Fourier analysis. This highlights the dynamics of neural processes in the visual system.

Scale-dependent intrinsic entropies of complex time series.

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

Investigating Power Density and the Degree of Nonlinearity in Intrinsic Components of Anesthesia EEG by the Hilbert-Huang Transform: An Example Using Ketamine and Alfentanil.

Empirical mode decomposition (EMD) is an adaptive filter bank for processing nonlinear and non-stationary signals, such as electroencephalographic (EEG) signals. EMD works well to decompose a time series into a set of intrinsic mode functions with specific frequency bands. An IMF therefore represents an intrinsic component on its correspondingly intrinsic frequency band. The word of 'intrinsic' means the frequency is totally adaptive to the nature of a signal. In this study, power density and nonlinearity are two critical parameters for characterizing the amplitude and frequency modulations in IMFs. In this study, a nonlinearity level is quantified using degree of waveform distortion (DWD), which represents the characteristic of waveform distortion as an assessment of the intra-wave modulation of an IMF. In the application of anesthesia EEG analysis, the assessments of power density and DWD for a set of IMFs represent dynamic responses in EEG caused by two different anesthesia agents, Ketamine and Alfentanil, on different frequency bands. Ketamine causes the increase of power density and the decrease of nonlinearity on γ-band neuronal oscillation, which cannot be found EEG responses of group B using Alfentanil. Both agents cause an increase of power density and a decrease of nonlinearity on ß-band neuronal oscillation accompany with a loss of consciousness. Moreover, anesthesia agents cause the decreases of power density and nonlinearity (i.e. DWD) for the low-frequency IMFs.

Novel application of a Wii remote to measure spasticity with the pendulum test: Proof of concept.

BACKGROUND: The pendulum test is a standard clinical test for quantifying the severity of spasticity. In the test, an electrogoniometer is typically used to measure the knee angular motion. The device is costly and difficult to set up such that the pendulum test is normally time consuming. OBJECTIVE: The goal of this study is to determine whether a Nintendo Wii remote can replace the electrogroniometer for reliable assessment of the angular motion of the knee in the pendulum test. METHODS: The pendulum test was performed in three control participants and 13 hemiplegic stroke patients using both a Wii remote and an electrogoniometer. The correlation coefficient and the Bland-Altman difference plot were used to compare the results obtained from the two devices. The Wilcoxon signed-rank test was used to compare the difference between hemiplegia-affected and nonaffected sides in the hemiplegic stroke patients. RESULTS: There was a fair to strong correlation between measurements from the Wii remote and the electrogoniometer (0.513

Expression profiles of loneliness-associated genes for survival prediction in cancer patients.

Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for high- lonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Loneliness- associated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.

Nonlinear analysis of sensory organization test for subjects with unilateral vestibular dysfunction.

Vestibular disorder is the cause of approximately 50% of dizziness in older people. The vestibular system is a critical postural control mechanism, and posturography analysis is helpful for diagnosing vestibular disorder. In clinical practice, the sensory organization test (SOT) is used to quantify postural control in an upright stance under different test conditions. However, both aging and vestibular disorder cause declines of postural control mechanisms. The aim of this study was to enhance the performance of the SOT using a nonlinear algorithm of empirical mode decomposition (EMD) and to verify the differences of effects caused by aging and/or illnesses benefits to clinical diagnosis. A total of 51 subjects belonging to 3 groups--healthy-young, healthy-elderly and dizzy--were recruited for this study. New dynamic parameters of the SOT were derived from the center of pressure (COP) signals. EMD served as an adaptive filter bank to derive the low- and high-frequency components of the COP. The effects on four ratios of sensory analysis caused by aging and vestibular disorder can be investigated for the specific frequency bands. According to our findings, new SOT parameters derived from the component with the specific frequency band more sensitively reflect the functional condition of vestibular dysfunction. Furthermore, both aging and vestibular dysfunction caused an increase in magnitude for the low-frequency component of the AP-direction COP time series. In summary, the low-frequency fluctuation reflects the stability of postural control, while the high-frequency fluctuation is sensitive to the functional condition of the sensory system. EMD successfully improved the accuracy of SOT measurements in this investigation.

Complexity of human postural control in subjects with unilateral peripheral vestibular hypofunction.

Complexity is a new measure for identifying the adaptability of a complex system to meet possible challenges. For a center of pressure (COP) time series, the complexity measure represents the stability of postural control. In this study, multiscale entropy (MSE) was used to evaluate the complexity of COP time series in six test conditions of sensory organization test (SOT). Complexity index (CI) is defined as the summation of entropies with coarse-graining scales 1-20 by MSE. A total of 51 subjects belonging to 3 groups - healthy-young, healthy-elderly and dizzy - were recruited in this study. The COP signals in both anteroposterior (AP) and mediolateral (ML) directions were analyzed respectively. According to our results, the CI of AP-direction COP time series is significantly correlated to the equilibrium score, which represents the stability of postural control in SOT. The AP-direction sway is significant larger than the ML-direction sway, particularly in the test conditions with sway-surface. In additions, the CI of AP-direction COP for the healthy-elderly and dizzy groups are significantly lower than those for the healthy young group in the test conditions 1-4. The CI of ML-direction COP for the healthy-elderly group is significantly lower than those for the healthy-young and dizzy groups under test conditions 3 and 6. These results show that the complexity loss is a common status of AP-direction COP time series for both healthy-elderly and dizzy groups, and the complexity of ML-direction COP time series for subjects with unilateral vestibular dysfunction is higher than that for the healthy-elderly group specifically under test conditions 3 and 6.

Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms.

BACKGROUND: The multi-mode modulation is a key feature of sleep EEG. And the short-term fractal property reflects the sympathovagal modulation of heart rate variability (HRV). The properties of EEG and HRV strongly correlated with sleep status and are interesting in clinic diagnosis. NEW METHOD: 19 healthy female subjects were included for over-night standard polysomnographic study. Hilbert Huang transform (HHT) was used to characterize the temporal features of slow- and fast-wave oscillations decomposed from sleep EEG at different stages. Masking signals were used for solving the mode-mixing problem in HHT. On the other hand, detrended fluctuation analysis (DFA) was used to assess short-term property of HRV denoted as DFA α1, which reflects the temporal activity of autonomic nerve system (ANS). Thus, the dynamic interaction between sleep EEG and HRV can be examined through the relationship between the features of sleep EEG and DFA α1 of HRV. RESULTS: The frequency feature of sleep EEG serves as a good indicator for the depth of sleep during non-rapid eye movement (NREM) sleep, and amplitude feature of fast-wave oscillation is a good index for distinguishing rapid eye movement (REM) from NREM sleep. COMPARISON WITH EXISTING METHOD: The relationship between DFA α1 of HRV and the mean amplitude of fast-wave oscillation of sleep EEG affirmed with Pearson correlation coefficient is more significant than the correlation verified by the traditional spectral analysis. CONCLUSION: The dynamic properties of sleep EEG and HRV derived by EMD and DFA represent important features for cortex and ANS activities during sleep.

Investigating properties of the cardiovascular system using innovative analysis algorithms based on ensemble empirical mode decomposition.

Cardiovascular system is known to be nonlinear and nonstationary. Traditional linear assessments algorithms of arterial stiffness and systemic resistance of cardiac system accompany the problem of nonstationary or inconvenience in practical applications. In this pilot study, two new assessment methods were developed: the first is ensemble empirical mode decomposition based reflection index (EEMD-RI) while the second is based on the phase shift between ECG and BP on cardiac oscillation. Both methods utilise the EEMD algorithm which is suitable for nonlinear and nonstationary systems. These methods were used to investigate the properties of arterial stiffness and systemic resistance for a pig's cardiovascular system via ECG and blood pressure (BP). This experiment simulated a sequence of continuous changes of blood pressure arising from steady condition to high blood pressure by clamping the artery and an inverse by relaxing the artery. As a hypothesis, the arterial stiffness and systemic resistance should vary with the blood pressure due to clamping and relaxing the artery. The results show statistically significant correlations between BP, EEMD-based RI, and the phase shift between ECG and BP on cardiac oscillation. The two assessments results demonstrate the merits of the EEMD for signal analysis.