Impaired cerebral autoregulation is associated with poststroke cognitive impairment.

OBJECTIVE: To investigate whether dynamic cerebral autoregulation (CA) and neuroimaging characteristics are determinants of poststroke cognitive impairment (PSCI). METHODS: Eighty patients within 7 days of acute ischemic stroke and 35 age- and sex-matched controls were enrolled. In the patients with stroke, brain magnetic resonance imaging and dynamic CA were obtained at baseline, and dynamic CA was followed up at 3 months and 1 year. Montreal Cognitive Assessment (MoCA) was performed at 3 months and 1 year. Patients with a MoCA score <23 at 1 year were defined as having PSCI, and those with a MoCA score that decreased by 2 points or more between the 3-month and 1-year assessments were defined as having progressive cognitive decline. RESULTS: In total, 65 patients completed the study and 16 developed PSCI. The patients with PSCI exhibited poorer results for all cognitive domains than did those without PSCI. The patients with PSCI also had poorer CA (lower phase shift between cerebral blood flow and blood pressure waveforms in the very low frequency band) compared with that of the patients without PSCI and controls at baseline and 1 year. CA was not different between the patients without PSCI and controls. In the multivariate analysis, low education level, lobar microbleeds, and impaired CA (very low frequency phase shift [≤46°] within 7 days of stroke), were independently associated with PSCI. In addition, impaired CA was associated with progressive cognitive decline. INTERPRETATION: Low education level, lobar microbleeds, and impaired CA are involved in the pathogenesis of PSCI.

Probing age-related changes in cardio-respiratory dynamics by multimodal coupling assessment.

Quantifying respiratory sinus arrhythmia (RSA) can provide an index of parasympathetic function. Fourier spectral analysis, the most widely used approach, estimates the power of the heart rate variability in the frequency band of breathing. However, it neglects the time-varying characteristics of the transitions as well as the nonlinear properties of the cardio-respiratory coupling. Here, we propose a novel approach based on Hilbert-Huang transform, called the multimodal coupling analysis (MMCA) method, to assess cardio-respiratory dynamics by examining the instantaneous nonlinear phase interactions between two interconnected signals (i.e., heart rate and respiration) and compare with the counterparts derived from the wavelet-based method. We used an online database. The corresponding RSA components of the 90-min ECG and respiratory signals of 20 young and 20 elderly healthy subjects were extracted and quantified. A cycle-based analysis and a synchro-squeezed wavelet transform were also introduced to assess the amplitude or phase changes of each respiratory cycle. Our results demonstrated that the diminished mean and standard deviation of the derived dynamical RSA activities can better discriminate between elderly and young subjects. Moreover, the degree of nonlinearity of the cycle-by-cycle RSA waveform derived from the differences between the instantaneous frequency and the mean frequency of each respiratory cycle was significantly decreased in the elderly subjects by the MMCA method. The MMCA method in combination with the cycle-based analysis can potentially be a useful tool to depict the aging changes of the parasympathetic function as well as the waveform nonlinearity of RSA compared to the Fourier-based high-frequency power and the wavelet-based method.

The critical role of respiratory sinus arrhythmia on temporal cardiac dynamics.

Temporal cardiac properties provide alternative information in analyzing heart rate variability (HRV), which may be disregarded by the standard HRV analyses. Patients with congestive heart failure (CHF) are known to have distinct temporal features from the healthy individuals. However, the underlying mechanism leading to the variation remains unclear. Whether or not these parameters can finely classify the severity for CHF patients is uncertain as well. In this work, an electrocardiogram was monitored in advanced CHF patients using 24-h Holter in four conditions, including baseline, one and three months after atenolol therapy, and healthy individuals. Slope and area under the curve (AUC) of multiscale entropy (MSE) curve over short (scales 1-5) and long (scales 6-20) scales, and detrended fluctuation analysis (DFA) scaling exponents at short (4-11 beats) and intermediate (>11 beats) window sizes were calculated. The results show that short-time scale MSE-derived parameters (slope: -0.08 ± 0.10, -0.03 ± 0.10, 0.02 ± 0.06, 0.08 ± 0.06; AUC: 4.03 ± 2.11, 4.69 ± 1.28, 4.73 ± 0.94, and 6.17 ± 1.23) and short-time scale DFA exponent (0.79 ± 0.16, 0.95 ± 0.22, 1.11 ± 0.19, and 1.35 ± 0.20) can hierarchically classify all four conditions. More importantly, simulated R-R intervals with different fractions and amplitude of respiratory sinus arrhythmia (RSA) components were examined to validate our hypothesis regarding the essentiality of RSA in the improvement of cardiovascular function, and its tight association with unpredictability and fractal property of HRV, which is in line with our hypothesis that RSA contributes significantly to the generation of the unpredictability and fractal behavior of HR dynamics.NEW & NOTEWORTHY Temporal cardiac properties provide useful diagnostic parameters for patients with congestive heart failure (CHF). Our study hierarchically classified CHF patients with ß-blocker treatment by using multiscale entropy and detrended fluctuation analysis. Also, we provided the evidence to validate the critical role of respiratory sinus arrhythmia in the fractal properties of heart rate variability.

Robust Fetal Heart Beat Detection via R-Peak Intervals Distribution.

Monitoring fetal heart rate during pregnancy is essential to assist clinicians in making more timely decisions. Non-invasive monitoring of fetal heart activities using abdominal ECGs is useful for diagnosis of heart defects. However, the extracted fetal ECGs are usually too weak to be robustly detected. Thus, it is a necessity to enhance fetal R-peak since their peaks may be hidden within the signal due to the immaturity of the fetal cardiovascular system. Therefore, to improve the detection of the fetal heartbeat, a novel fetal R-peak enhancement technique was proposed to statistically generate the weighting mask according to the distribution of the neighboring temporal intervals between each pair of peaks. Two sets of simulations were designed to validate the reliability of the method: challenges with different levels of (1) noise contamination and (2) R-peak interval changing rate. The simulation results showed that the weighting mask improved the accuracy of the R-peak detection rate by 25% and decreased the false alarm rate by 20% with white noise contamination, and ensured high R-peak detection rate (>80%), especially with mild noise contamination (noise amplitude ratio <1.5 and noise rate per minute <25%). For the simulations with continuous R-peak intervals changing, the masking process can still effectively eliminate noise contamination especially when the amplitude of the sinusoidal fetal R-R intervals is lower than 50 ms. For the real fetus ECGs, the detection rate was increased by 3.498%, whereas the false alarm rate was decreased by 3.933%. Next, we implemented the fetal R-peak enhancement technique to investigate fractal regulation and multiscale entropy of the real fetal heartbeat intervals. Both scaling exponent (∼0.6 to ∼1 in scale 4-15) and entropy measure (scale 6-10) increased with gestational ages (22-40 weeks). The results confirmed fractal slope and complexity of fetal heartbeat intervals can reflect the maturation of fetus organism.

Dynamic Cerebral Autoregulation Is an Independent Functional Outcome Predictor of Mild Acute Ischemic Stroke.

Background and Purpose- Cerebral autoregulation is impaired in patients with acute ischemic stroke. The purpose of this study was to investigate whether dynamic cerebral autoregulation (dCA) indices constitute an independent functional outcome predictor of acute ischemic stroke. Methods- In this study, 86 patients at days 3 to 7 after acute ischemic stroke and 40 age- and sex-matched controls were enrolled for assessing their dCA indices under spontaneous hemodynamic fluctuations. The dCA indices of patients with favorable outcomes (modified Rankin Scale score ≤1 at 3 months, n=65), patients with unfavorable outcomes (modified Rankin Scale score ≥2 at 3 months, n=21), and controls were compared. Results- The dCA indices, namely the phase shift at very low frequency band (phase_VLF), in the patients with unfavorable outcomes were significantly worse than those in the patients with favorable outcomes. However, the phase_VLF in the patients with favorable outcomes did not differ from those in the controls. Impaired dCA was associated with elevated mean arterial pressure and large infarction volume but was also present in patients with normal mean arterial pressure or small infarction volume. Phase_VLF was a predictor of outcomes in the receiver operating characteristic analysis (area under the curve: 0.722; P<0.001). Multivariate analysis revealed that a phase_VLF value of <61° was independently associated with unfavorable outcomes (odds ratio=4.90; P=0.024). Conclusions- Phase_VLF is an independent functional outcome predictor of acute ischemic stroke.

Comparing Different Recording Lengths of Dynamic Cerebral Autoregulation: 5 versus 10 Minutes.

We compared the dynamic cerebral autoregulation (dCA) indices between 5- and 10-minute data lengths by analyzing 37 patients with ischemic stroke and 51 controls in this study. Correlation coefficient (Mx) and transfer function analysis were applied for dCA analysis. Mx and phase shift in all frequency bands were not significantly different between 5- and 10-minute recordings [mean difference: Mx = 0.02; phase shift of very low frequency (0.02-0.07 Hz) = 0.3°, low frequency (0.07-0.20 Hz) = 0.6°, and high frequency (0.20-0.50 Hz) = 0.1°]. However, the gains in all frequency bands of a 5-minute recording were slightly but significantly higher than those of a 10-minute recording (mean difference of gain: very low frequency = 0.05 cm/s/mmHg, low frequency = 0.11 cm/s/mmHg, and high frequency = 0.14 cm/s/mmHg). The intraclass correlation coefficients between all dCA indices of 5- and 10-minute recordings were favorable, especially in Mx (0.93), phase shift in very low frequency (0.87), and gain in very low frequency (0.94). The areas under the receiver operating characteristic curve for stroke diagnosis between 5- and 10-minute recordings were not different. We concluded that dCA assessed by using a 5-minute recording is not significantly different from that using a 10-minute recording in the clinical application.

Quantifying Spasticity With Limited Swinging Cycles Using Pendulum Test Based on Phase Amplitude Coupling.

Parameters derived from the goniometer measures in the Pendulum test are insufficient in describing the function of abnormal muscle activity in the spasticity. To explore a quantitative evaluation of muscle activation-movement interaction, we propose a novel index based on phase amplitude coupling (PAC) analysis with the consideration of the relations between movement and surface electromyography (SEMG) activity among 22 hemiplegic stroke patients. To take off trend and noise, we use the empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) of the angular velocity due to its superior decomposing ability in nonlinear oscillations. Shannon entropy based on angular velocity (phase)-envelope of EMG (amplitude) distribution was calculated to demonstrate characteristics of the coupling between SEMG activity and joint movement. We also compare our results with those from traditional methods such as the normalized relaxation index derived from the Pendulum test and the mean root mean square (RMS) of the SEMG signals in the study. Our results show effective discrimination ability between spastic and nonaffected limbs using our method . This study indicates the feasibility of using the novel indices based on the PAC in evaluation the spasticity among the hemiplegic stroke patients with less than three swinging cycles.

Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure-flow relation: the CARNet study.

Transfer function analysis (TFA) is a frequently used method to assess dynamic cerebral autoregulation (CA) using spontaneous oscillations in blood pressure (BP) and cerebral blood flow velocity (CBFV). However, controversies and variations exist in how research groups utilise TFA, causing high variability in interpretation. The objective of this study was to evaluate between-centre variability in TFA outcome metrics. 15 centres analysed the same 70 BP and CBFV datasets from healthy subjects (n=50 rest; n=20 during hypercapnia); 10 additional datasets were computer-generated. Each centre used their in-house TFA methods; however, certain parameters were specified to reduce a priori between-centre variability. Hypercapnia was used to assess discriminatory performance and synthetic data to evaluate effects of parameter settings. Results were analysed using the Mann-Whitney test and logistic regression. A large non-homogeneous variation was found in TFA outcome metrics between the centres. Logistic regression demonstrated that 11 centres were able to distinguish between normal and impaired CA with an AUC>0.85. Further analysis identified TFA settings that are associated with large variation in outcome measures. These results indicate the need for standardisation of TFA settings in order to reduce between-centre variability and to allow accurate comparison between studies. Suggestions on optimal signal processing methods are proposed.

Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep.

OBJECTIVES: The physiologic relationship between slow-wave activity (SWA) (0-4 Hz) on the electroencephalogram (EEG) and high-frequency (0.1-0.4 Hz) cardiopulmonary coupling (CPC) derived from electrocardiogram (ECG) sleep spectrograms is not known. Because high-frequency CPC appears to be a biomarker of stable sleep, we tested the hypothesis that that slow-wave EEG power would show a relatively fixed-time relationship to periods of high-frequency CPC. Furthermore, we speculated that this correlation would be independent of conventional nonrapid eye movement (NREM) sleep stages. METHODS: We analyzed selected datasets from an archived polysomnography (PSG) database, the Sleep Heart Health Study I (SHHS-I). We employed the cross-correlation technique to measure the degree of which 2 signals are correlated as a function of a time lag between them. Correlation analyses between high-frequency CPC and delta power (computed both as absolute and normalized values) from 3150 subjects with an apnea-hypopnea index (AHI) of ≤5 events per hour of sleep were performed. RESULTS: The overall correlation (r) between delta power and high-frequency coupling (HFC) power was 0.40±0.18 (P=.001). Normalized delta power provided improved correlation relative to absolute delta power. Correlations were somewhat reduced in the second half relative to the first half of the night (r=0.45±0.20 vs r=0.34±0.23). Correlations were only affected by age in the eighth decade. There were no sex differences and only small racial or ethnic differences were noted. CONCLUSIONS: These results support a tight temporal relationship between slow wave power, both within and outside conventional slow wave sleep periods, and high frequency cardiopulmonary coupling, an ECG-derived biomarker of "stable" sleep. These findings raise mechanistic questions regarding the cross-system integration of neural and cardiopulmonary control during sleep.

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.

A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation.

AIMS: Accurate ventricular fibrillation (VF) waveform analysis usually requires rescuers to discontinue cardiopulmonary resuscitation (CPR). However, prolonged "hands-off" time has a deleterious impact on the outcome. We developed a new filter technique that could clean the CPR artifacts and help preserve the shockability index of VF METHODS: We analyzed corrupted ECGs, which were constructed by randomly adding different scaled CPR artifacts to the VF waveforms. A newly developed algorithm was used to identify the CPR fluctuations. The algorithm contained two steps. First, decomposing the raw data by empirical mode decomposition (EMD) into several intrinsic mode fluctuations (IMFs) and combining the dominant IMFs to reconstruct a new signal. Second, calculating each CPR cycle frequency from the new signal and fitting the new signal to the original corrupted ECG by least square mean (LSM) method to derive the CPR artifacts. The estimated VF waveform was derived by subtraction of the CPR artifacts from the corrupted ECG. We then performed amplitude spectrum analysis (AMSA) for original VF, corrupted ECG and estimated VF. RESULTS: A total of 150 OHCA subjects with initial VF rhythm were included for analysis. Ten CPR artifacts signals were used to construct corrupted ECG. Even though the correlations of AMSA between the corrupted ECG vs. the original VF and the estimated VF vs. the original VF are all high (all p<0.001), the values of AMSA were obviously biased in corrupted ECG with wide limits of agreement in Bland-Altman mean-difference plot. ROC analysis of the AMSA in the prediction of defibrillation success showed that the new algorithm could preserve the cut-off AMSA value for CPR artifacts with power ratio to VF from 0 to 6 dB. CONCLUSION: The new algorithm could efficiently filter the CPR-related artifacts of the VF ECG and preserve the shockability index of the original VF waveform.