Nonlinear phase interaction between nonstationary signals: a comparison study of methods based on Hilbert-Huang and Fourier transforms

Empirical mode decomposition of local field potential data from optogenetic experiments

Introduction

This study investigated the effects of cocaine administration and parvalbumin-type interneuron stimulation on local field potentials (LFPs) recorded in vivo from the medial prefrontal cortex (mPFC) of six mice using optogenetic tools.

Methods

The local network was subject to a brief 10 ms laser pulse, and the response was recorded for 2 s over 100 trials for each of the six subjects who showed stable coupling between the mPFC and the optrode. Due to the strong non-stationary and nonlinearity of the LFP, we used the adaptive, data-driven, Empirical Mode Decomposition (EMD) method to decompose the signal into orthogonal Intrinsic Mode Functions (IMFs).

Results

Through trial and error, we found that seven is the optimum number of orthogonal IMFs that overlaps with known frequency bands of brain activity. We found that the Index of Orthogonality (IO) of IMF amplitudes was close to zero. The Index of Energy Conservation (IEC) for each decomposition was close to unity, as expected for orthogonal decompositions. We found that the power density distribution vs. frequency follows a power law with an average scaling exponent of ~1.4 over the entire range of IMF frequencies 2-2,000 Hz.

Discussion

The scaling exponent is slightly smaller for cocaine than the control, suggesting that neural activity avalanches under cocaine have longer life spans and sizes.

Daily rhythm of dynamic cerebral autoregulation in patients after stroke

Dynamic cerebral autoregulation (dCA) in healthy young adults displays a daily variation. Whether the rhythm exists in patients with stroke is unknown. We studied 28 stroke patients (age: 26-83 years, 7 females) within 48 hours after thrombolysis. dCA was assessed 54 times in these patients during supine rest (twice in 26 and once in 2 patients): 9 assessments between 0-9AM, 12 between 9AM-2PM, 20 between 2-7PM, and 13 between 7PM-12AM. To estimate dCA, phase shifts between spontaneous oscillations of cerebral blood flow velocity (CBFV) in the middle cerebral artery and arterial blood pressure (BP) were obtained in four frequency bands: <0.05 Hz, 0.05-0.1 Hz, 0.1-0.2 Hz, and >0.2 Hz. CBFV-BP phase shifts at <0.05 Hz were significantly larger between 2-7PM, suggesting better dCA, than those at other times (p < 0.0001), and the daily rhythm was consistent for stroke and non-stroke sides. No significant rhythms were observed at higher frequencies (all p > 0.2). All results were independent of age, sex, stroke type and severity, and other cardiovascular conditions. dCA after stroke showed a daily rhythm, leading to a better regulation of CBFV at <0.05 Hz during the afternoon. The finding may have implications for daily activity management of stroke patients.

Cross-Frequency Coupling and Intelligent Neuromodulation

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes

Neurophysiological signals are often noisy, nonsinusoidal, and consist of transient bursts. Extraction and analysis of oscillatory features (such as waveform shape and cross-frequency coupling) in such data sets remains difficult. This limits our understanding of brain dynamics and its functional importance. Here, we develop iterated masking empirical mode decomposition (itEMD), a method designed to decompose noisy and transient single-channel data into relevant oscillatory modes in a flexible, fully data-driven way without the need for manual tuning. Based on empirical mode decomposition (EMD), this technique can extract single-cycle waveform dynamics through phase-aligned instantaneous frequency. We test our method by extensive simulations across different noise, sparsity, and nonsinusoidality conditions. We find itEMD significantly improves the separation of data into distinct nonsinusoidal oscillatory components and robustly reproduces waveform shape across a wide range of relevant parameters. We further validate the technique on multimodal, multispecies electrophysiological data. Our itEMD extracts known rat hippocampal θ waveform asymmetry and identifies subject-specific human occipital α without any prior assumptions about the frequencies contained in the signal. Notably, it does so with significantly less mode mixing compared with existing EMD-based methods. By reducing mode mixing and simplifying interpretation of EMD results, itEMD will enable new analyses into functional roles of neural signals in behavior and disease.

NEW & NOTEWORTHY We introduce a novel, data-driven method to identify oscillations in neural recordings. This approach is based on empirical mode decomposition and reduces mixing of components, one of its main problems. The technique is validated and compared with existing methods using simulations and real data. We show our method better extracts oscillations and their properties in highly noisy and nonsinusoidal datasets.

Impairment of Cardiac Autonomic Nerve Function in Pre-school Children With Intractable Epilepsy

Objective: Intractable epilepsy and uncontrolled seizures could affect cardiac function and the autonomic nerve system with a negative impact on children's growth. The aim of this study was to investigate the variability and complexity of cardiac autonomic function in pre-school children with pediatric intractable epilepsy (PIE). Methods: Twenty four-hour Holter electrocardiograms (ECGs) from 93 patients and 46 healthy control subjects aged 3-6 years were analyzed by the methods of traditional heart rate variability (HRV), multiscale entropy (MSE), and Kurths-Wessel symbolization entropy (KWSE). Receiver operating characteristic (ROC) curve analysis was used to estimate the overall discrimination ability. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) models were also analyzed. Results: Pre-school children with PIE had significantly lower HRV measurements than healthy controls in time (Mean_RR, SDRR, RMSSD, pNN50) and frequency (VLF, LF, HF, LF/HF, TP) domains. For the MSE analysis, area 1_5 in awake state was lower, and areas 6_15 and 6_20 in sleep state were higher in PIE with a significant statistical difference. KWSE in the PIE group was also inferior to that in healthy controls. In ROC curve analysis, pNN50 had the greatest discriminatory power for PIE. Based on both NRI and IDI models, the combination of MSE indices (wake: area1_5 and sleep: area6_20) and KWSE (m = 2, τ = 1, α = 0.16) with traditional HRV measures had greater discriminatory power than any of the single HRV measures. Significance: Impaired HRV and complexity were found in pre-school children with PIE. HRV, MSE, and KWSE could discriminate patients with PIE from subjects with normal cardiac complexity. These findings suggested that the MSE and KWSE methods may be helpful for assessing and understanding heart rate dynamics in younger children with epilepsy.

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.

Development of an addiction index and delineation 15-year trends of illicit drugs from the Taiwan national drug enhancement database

Illicit drug use contributes to substantial morbidity and mortality. Drug scheduling, a legal measure in drug enforcement, is often structured as a hierarchy based on addiction tendency, abuse trends, and harm, but may lack data-driven evidence when classifying substances. Our study aims to measure addiction tendency and use trends based on real-world data. We used the open access database of National Police Agency, Ministry of the Interior in Taiwan and analyzed all daily criminal cases of illicit drugs from 2013 to 2017 and monthly illicit drug enforcement data from the same database from 2002 to 2017. We hypothesized that repeat and frequent use despite legal consequence may be a reflection of addictive behavior, and empirical mode decomposition was applied in analysis to calculate addiction tendency indices and intrinsic 15-year use trends. Our analysis showed heroin has the highest addiction index, followed by methamphetamine. 3,4-Methyl enedioxy methamphetamine, marijuana, and ketamine had lower addictive propensities. This result is consistent with most drug scheduling hierarchies. 15-year use trends of substances were consistent with previous epidemiological studies.

[Prediction of syncope with nonlinear dynamic analysis during head-up tilt in vasovagal syncope patients]

OBJECTIVE

To quantify the relationship between cerebral blood flow velocity and peripheral blood pressure during hypotension period, aiming to predict the brain hypotension before symptomatic occurrence.

METHODS

Twenty vasovagal syncope (VVS) patients who had a previous clinical history were selected in groups and 20 pair-matched control subjects underwent 70° tilt-up test. The subjects remained supine for 30 minutes before recordings when Doppler probes, electrodes and Finapres device were prepared. After continuous baseline recordings for 10 min, the subjects underwent head up tilt (HUT) test (70°), and were standing upright for 30 minutes or until syncope was imminent. For ethical reasons, the subjects were turned back to supine position immediately after SBP dropped to ≥20 mmHg, when their consciousness persisted. The point of syncope was synchronized for all the subjects by the point SBP reached the minima. Their beat-to-beat blood pressures (BP) were recorded continuously and bilateral middle cerebral artery (MCA) flow velocities were obtained with two 2 MHz Doppler probes from a transcranial Doppler ultrasonography (TCD) system. A nonlinear dynamic method--multimodal pressure flow (MMPF) analysis was introduced to access cerebral autoregulation during different time intervals. We introduced a new indicator--syncope index (SI), which was extracted from blood flow velocity (BFV) signal to evaluate the variation of cerebral vascular tension, and could reflect the deepness of dicrotic notch in BFV signal.

RESULTS

Compared with the syncope index of the baseline value at the beginning of the tilt test, SI in VVS group showed significantly lower when the VVS occurred (0.16±0.10 vs.0.27±0.10,P<0.01),while there was no significant difference in syncope index between the control group at the end of the tilt test and the baseline value at the beginning of the tilt test. For those VVS patients, pulse index and resistance index had no significant change. Syncope index decreased significantly 3 minutes before the point of syncope (0.23±0.07 vs.0.29±0.07,P<0.01).

CONCLUSION

Dynamic regulation is exhausted when vasovagal syncope occurred. Tension decrease of small vessels could have some relationship with loss of the cerebral autoregulation capability. The proposed syncope index could be a useful parameter in predicting syncope of VVS patients since it decreased significantly up to 3 minutes earlier from the point of syncope.

Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients

Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1-600 Hz), ripple (80-250 Hz), and fast-ripple (250-600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase toward seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices.

Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods.

Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures.

Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.

Causal decomposition in the mutual causation system

Inference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may underestimate the simultaneous and reciprocal nature of causal interactions observed in real-world phenomena. Here, we present a causal-decomposition approach that is not based on prediction, but based on the covariation of cause and effect: cause is that which put, the effect follows; and removed, the effect is removed. Using empirical mode decomposition, we show that causal interaction is encoded in instantaneous phase dependency at a specific time scale, and this phase dependency is diminished when the causal-related intrinsic component is removed from the effect. Furthermore, we demonstrate the generic applicability of our method to both stochastic and deterministic systems, and show the consistency of causal-decomposition method compared to existing methods, and finally uncover the key mode of causal interactions in both modelled and actual predator–prey systems.

Causality inference in time series analysis based on temporal precedence principle between cause and effect fails to detect mutual causal interactions. Here, Yang et al. introduce a causal decomposition approach based on the covariation principle of cause and effect that overcomes this limitation.

Frequency and amplitude modulation of resting-state fMRI signals and their functional relevance in normal aging

The intrinsic composition and functional relevance of resting-state blood oxygen level-dependent signals are fundamental in research using functional magnetic resonance imaging (fMRI). Using the Hilbert-Huang Transform to estimate high-resolution time-frequency spectra, we investigated the instantaneous frequency and amplitude modulation of resting-state fMRI signals, as well as their functional relevance in a large normal-aging cohort (n = 420, age = 21-89 years). We evaluated the cognitive function of each participant and recorded respiratory signals during fMRI scans. The results showed that the Hilbert-Huang Transform effectively categorized resting-state fMRI power spectra into high (0.087-0.2 Hz), low (0.045-0.087 Hz), and very-low (≤0.045 Hz) frequency bands. The high-frequency power was associated with respiratory activity, and the low-frequency power was associated with cognitive function. Furthermore, within the cognition-related low-frequency band (0.045-0.087 Hz), we discovered that aging was associated with the increased frequency modulation and reduced amplitude modulation of the resting-state fMRI signal. These aging-related changes in frequency and amplitude modulation of resting-state fMRI signals were unaccounted for by the loss of gray matter volume and were consistently identified in the default mode and salience network. These findings indicate that resting-state fMRI signal modulations are dynamic during the normal aging process. In summary, our results refined the functionally related blood oxygen level-dependent frequency band in a considerably narrow band at a low-frequency range (0.045-0.087 Hz) and challenged the current method of resting-fMRI preprocessing by using low-frequency filters with a relatively wide range below 0.1 Hz.

Uniform Phase Empirical Mode Decomposition: An Optimal Hybridization of Masking Signal and Ensemble Approaches

The empirical mode decomposition (EMD) is an established method for the time-frequency analysis of nonlinear and nonstationary signals. However, one major drawback of the EMD is the mode mixing effect. Many modifications have been made to resolve the mode mixing effect. In particular, disturbance-assisted EMDs, such as the noise-assisted EMD and the masking EMD, have been proposed to resolve this problem. These disturbance-assisted approaches have led to a better performance of the EMD in the analysis of real-world data sets, but they may also have two side effects: the mode splitting and residual noise effects. To minimize or eliminate the mode mixing effect while avoiding the two side effects of traditional disturbance-assisted EMDs, we propose an EMD-based algorithm assisted by sinusoidal functions with a designed uniform phase distribution with a comprehensive theoretical explanation for the substantial reduction of the mode splitting and the residual noise effects simultaneously. We examine the performance of the new method and compare it to those of other disturbance-assisted EMDs using synthetic signals. Finally numerical experiments with real-world examples are conducted to verify the performance of the proposed method.

The effect of dim light at night on cerebral hemodynamic oscillations during sleep: A near-infrared spectroscopy study

Recent studies have reported that dim light at night (dLAN) is associated with risks of cardiovascular complications, such as hypertension and carotid atherosclerosis; however, little is known about the underlying mechanism. Here, we evaluated the effect of dLAN on the cerebrovascular system by analyzing cerebral hemodynamic oscillations using near-infrared spectroscopy (NIRS). Fourteen healthy male subjects underwent polysomnography coupled with cerebral NIRS. The data collected during sleep with dim light (10 lux) were compared with those collected during sleep under the control dark conditions for the sleep structure, cerebral hemodynamic oscillations, heart rate variability (HRV), and their electroencephalographic (EEG) power spectrum. Power spectral analysis was applied to oxy-hemoglobin concentrations calculated from the NIRS signal. Spectral densities over endothelial very-low-frequency oscillations (VLFOs) (0.003-0.02 Hz), neurogenic VLFOs (0.02-0.04 Hz), myogenic low-frequency oscillations (LFOs) (0.04-0.15 Hz), and total LFOs (0.003-0.15 Hz) were obtained for each sleep stage. The polysomnographic data revealed an increase in the N2 stage under the dLAN conditions. The spectral analysis of cerebral hemodynamics showed that the total LFOs increased significantly during slow-wave sleep (SWS) and decreased during rapid eye movement (REM) sleep. Specifically, endothelial (median of normalized value, 0.46 vs. 0.72, p = 0.019) and neurogenic (median, 0.58 vs. 0.84, p = 0.019) VLFOs were enhanced during SWS, whereas endothelial VLFOs (median, 1.93 vs. 1.47, p = 0.030) were attenuated during REM sleep. HRV analysis exhibited altered spectral densities during SWS induced by dLAN, including an increase in very-low-frequency and decreases in low-frequency and high-frequency ranges. In the EEG power spectral analysis, no significant difference was detected between the control and dLAN conditions. In conclusion, dLAN can disturb cerebral hemodynamics via the endothelial and autonomic systems without cortical involvement, predominantly during SWS, which might represent an underlying mechanism of the increased cerebrovascular risk associated with light exposure during sleep.

Impairment of heart rhythm complexity in patients with drug-resistant epilepsy: An assessment with multiscale entropy analysis

OBJECTIVE

Epilepsy and seizures can have dramatic effects on the cardiac function. The aim of this study was to investigate the heart rhythm complexity in patients with drug-resistant epilepsy (DRE).

METHODS

Ambulatory 24-h electrocardiograms (ECG) from 70 DRE patients and 50 healthy control subjects were analyzed using conventional heart rate variability (HRV) and multiscale entropy (MSE) methods The variation of complexity indices (CI), which was calculated from MSE profile, was determined.

RESULTS

DRE patients had significantly lower time domain (Mean RR, SDNN, RMSSD, pNN50) and frequency domain (VLF, LF, HF, TP) HRV measurements than healthy controls. Of the MSE analysis, MSE profile, CI including Slope 5, Area 1-5, Area 6-15 and Area 6-20 were significantly lower than those in the healthy control group. In receiver operating characteristic (ROC) curve analysis, VLF had the greatest discriminatory power for the two groups. In both net reclassification improvement (NRI) model and integrated discrimination improvement (IDI) models, CI derived from MSE profiles significantly improved the discriminatory power of Mean RR, SDNN, RMSSD, pNN50, VLF, LF, HF and TP.

SIGNIFICANCE

The heart rate complexity is impaired for DRE patients. CI are useful to discriminate DRE patients from subjects with normal cardiac complexity. These findings indicate that MSE method may serve as a complementary approach for characterizing and understanding abnormal heart rate dynamics in epilepsy. Furthermore, the CI may potentially be used as a biomarker in monitoring epilepsy.

Exploring the Abnormal Modulation of the Autonomic Systems during Nasal Flow Limitation in Upper Airway Resistance Syndrome by Hilbert-Huang Transform

Patients with nasal flow limitation and upper airway resistance syndrome (UARS) during sleep can present with low blood pressure and disturbing symptoms associated with hypervagotony. We hypothesized that the dynamic changes of the autonomic system related to inspiratory flow limitation can be quantified by the developed analytic technique applied on beat-to-beat heart rate (RR intervals) and finger photoplethysmography (PPG).

Spurious cross-frequency amplitude-amplitude coupling in nonstationary, nonlinear signals

Recent studies of brain activities show that cross-frequency coupling (CFC) play an important role in memory and learning. Many measures have been proposed to investigate the CFC phenomenon, including the correlation between the amplitude envelopes of two brain waves at different frequencies - cross-frequency amplitude-amplitude coupling (AAC). In this short communication, we describe how nonstationary, nonlinear oscillatory signals may produce spurious cross-frequency AAC. Utilizing the empirical mode decomposition, we also propose a new method for assessment of AAC that can potentially reduce the effects of nonlinearity and nonstatonarity and, thus, help to avoid the detection of artificial AACs. We compare the performances of this new method and the traditional Fourier-based AAC method. We also discuss the strategies to identify potential spurious AACs.

Heart rhythm complexity impairment in patients undergoing peritoneal dialysis

Cardiovascular disease is one of the leading causes of death in patients with advanced renal disease. The objective of this study was to investigate impairments in heart rhythm complexity in patients with end-stage renal disease. We prospectively analyzed 65 patients undergoing peritoneal dialysis (PD) without prior cardiovascular disease and 72 individuals with normal renal function as the control group. Heart rhythm analysis including complexity analysis by including detrended fractal analysis (DFA) and multiscale entropy (MSE) were performed. In linear analysis, the PD patients had a significantly lower standard deviation of normal RR intervals (SDRR) and percentage of absolute differences in normal RR intervals greater than 20 ms (pNN20). Of the nonlinear analysis indicators, scale 5, area under the MSE curve for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were significantly lower than those in the control group. In DFA anaylsis, both DFA α1 and DFA α2 were comparable in both groups. In receiver operating characteristic curve analysis, scale 5 had the greatest discriminatory power for two groups. In both net reclassification improvement model and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of SDRR, pNN20, and pNN50. In conclusion, PD patients had worse cardiac complexity parameters. MSE parameters are useful to discriminate PD patients from patients with normal renal function.

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.

Spontaneous Low-Frequency Cerebral Hemodynamics Oscillations in Restless Legs Syndrome with Periodic Limb Movements During Sleep: A Near-Infrared Spectroscopy Study

Background and Purpose

Periodic limb movements (PLM) during sleep (PLMS) are associated with cortical and cardiovascular activation. Changes in cerebral hemodynamics caused by cortical activity can be measured using near-infrared spectroscopy (NIRS). We investigated oscillatory components of cerebral hemodynamics during PLM and different sleep stages in restless legs syndrome (RLS) patients with PLMS.

Methods

Four female RLS patients with PLMS, and four age- and sex-matched normal controls were included. PLM and sleep stages were scored using polysomnography, while the spontaneous cerebral hemodynamics was measured by NIRS. The phase and amplitude of the cerebral oxyhemoglobin concentration [HbO] and the deoxyhemoglobin concentration [Hb] low-frequency oscillations (LFOs) were evaluated during each sleep stage [waking, light sleep (LS; stages N1 and N2), slow-wave sleep (stage N3), and rapid eye movement (REM) sleep]. In RLS patients with PLMS, the cerebral hemodynamics during LS was divided into LS with and without PLM.

Results

The cerebral hemodynamics activity varied among the different sleep stages. There were changes in phase differences between [HbO] and [Hb] LFOs during the different sleep stages in the normal controls but not in the RLS patients with PLMS. The [HbO] and [Hb] LFO amplitudes were higher in the patient group than in controls during both LS with PLM and REM sleep.

Conclusions

The present study has demonstrated the presence of cerebral hemodynamics disturbances in RLS patients with PLMS, which may contribute to an increased risk of cerebrovascular events.

Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

BACKGROUND

Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience.

NEW METHOD

To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise.

RESULTS

We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD.

COMPARISON WITH EXISTING METHOD(S)

Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution.

CONCLUSIONS

We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

Non-Linear Characterisation of Cerebral Pressure-Flow Dynamics in Humans

Cerebral metabolism is critically dependent on the regulation of cerebral blood flow (CBF), so it would be expected that vascular mechanisms that play a critical role in CBF regulation would be tightly conserved across individuals. However, the relationships between blood pressure (BP) and cerebral blood velocity fluctuations exhibit inter-individual variations consistent with heterogeneity in the integrity of CBF regulating systems. Here we sought to determine the nature and consistency of dynamic cerebral autoregulation (dCA) during the application of oscillatory lower body negative pressure (OLBNP). In 18 volunteers we recorded BP and middle cerebral artery blood flow velocity (MCAv) and examined the relationships between BP and MCAv fluctuations during 0.03, 0.05 and 0.07Hz OLBNP. dCA was characterised using project pursuit regression (PPR) and locally weighted scatterplot smoother (LOWESS) plots. Additionally, we proposed a piecewise regression method to statistically determine the presence of a dCA curve, which was defined as the presence of a restricted autoregulatory plateau shouldered by pressure-passive regions. Results show that LOWESS has similar explanatory power to that of PPR. However, we observed heterogeneous patterns of dynamic BP-MCAv relations with few individuals demonstrating clear evidence of a dCA central plateau. Thus, although BP explains a significant proportion of variance, dCA does not manifest as any single characteristic BP-MCAv function.

Reversible heart rhythm complexity impairment in patients with primary aldosteronism

Excess aldosterone secretion in patients with primary aldosteronism (PA) impairs their cardiovascular system. Heart rhythm complexity analysis, derived from heart rate variability (HRV), is a powerful tool to quantify the complex regulatory dynamics of human physiology. We prospectively analyzed 20 patients with aldosterone producing adenoma (APA) that underwent adrenalectomy and 25 patients with essential hypertension (EH). The heart rate data were analyzed by conventional HRV and heart rhythm complexity analysis including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We found APA patients had significantly decreased DFAα2 on DFA analysis and decreased area 1-5, area 6-15, and area 6-20 on MSE analysis (all p < 0.05). Area 1-5, area 6-15, area 6-20 in the MSE study correlated significantly with log-transformed renin activity and log-transformed aldosterone-renin ratio (all p < = 0.01). The conventional HRV parameters were comparable between PA and EH patients. After adrenalectomy, all the altered DFA and MSE parameters improved significantly (all p < 0.05). The conventional HRV parameters did not change. Our result suggested that heart rhythm complexity is impaired in APA patients and this is at least partially reversed by adrenalectomy.

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.

Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes

OBJECTIVE

Type 2 diabetes mellitus (DM) accelerates brain aging and cognitive decline. Complex interactions between hyperglycemia, glycemic variability and brain aging remain unresolved. This study investigated the relationship between glycemic variability at multiple time scales, brain volumes and cognition in type 2 DM.

RESEARCH DESIGN AND METHODS

Forty-three older adults with and 26 without type 2 DM completed 72-hour continuous glucose monitoring, cognitive tests and anatomical MRI. We described a new analysis of continuous glucose monitoring, termed Multi-Scale glycemic variability (Multi-Scale GV), to examine glycemic variability at multiple time scales. Specifically, Ensemble Empirical Mode Decomposition was used to identify five unique ultradian glycemic variability cycles (GVC1-5) that modulate serum glucose with periods ranging from 0.5-12 hrs.

RESULTS

Type 2 DM subjects demonstrated greater variability in GVC3-5 (period 2.0-12 hrs) than controls (P<0.0001), during the day as well as during the night. Multi-Scale GV was related to conventional markers of glycemic variability (e.g. standard deviation and mean glycemic excursions), but demonstrated greater sensitivity and specificity to conventional markers, and was associated with worse long-term glycemic control (e.g. fasting glucose and HbA1c). Across all subjects, those with greater glycemic variability within higher frequency cycles (GVC1-3; 0.5-2.0 hrs) had less gray matter within the limbic system and temporo-parietal lobes (e.g. cingulum, insular, hippocampus), and exhibited worse cognitive performance. Specifically within those with type 2 DM, greater glycemic variability in GVC2-3 was associated with worse learning and memory scores. Greater variability in GVC5 was associated with longer DM duration and more depression. These relationships were independent of HbA1c and hypoglycemic episodes.

CONCLUSIONS

Type 2 DM is associated with dysregulation of glycemic variability over multiple scales of time. These time-scale-dependent glycemic fluctuations might contribute to brain atrophy and cognitive outcomes within this vulnerable population.

Detecting phase-amplitude coupling with high frequency resolution using adaptive decompositions

BACKGROUND

Phase-amplitude coupling (PAC)--the dependence of the amplitude of one rhythm on the phase of another, lower-frequency rhythm - has recently been used to illuminate cross-frequency coordination in neurophysiological activity. An essential step in measuring PAC is decomposing data to obtain rhythmic components of interest. Current methods of PAC assessment employ narrowband Fourier-based filters, which assume that biological rhythms are stationary, harmonic oscillations. However, biological signals frequently contain irregular and nonstationary features, which may contaminate rhythms of interest and complicate comodulogram interpretation, especially when frequency resolution is limited by short data segments.

NEW METHOD

To better account for nonstationarities while maintaining sharp frequency resolution in PAC measurement, even for short data segments, we introduce a new method of PAC assessment which utilizes adaptive and more generally broadband decomposition techniques - such as the empirical mode decomposition (EMD). To obtain high frequency resolution PAC measurements, our method distributes the PAC associated with pairs of broadband oscillations over frequency space according to the time-local frequencies of these oscillations.

COMPARISON WITH EXISTING METHODS

We compare our novel adaptive approach to a narrowband comodulogram approach on a variety of simulated signals of short duration, studying systematically how different types of nonstationarities affect these methods, as well as on EEG data.

CONCLUSIONS

Our results show: (1) narrowband filtering can lead to poor PAC frequency resolution, and inaccuracy and false negatives in PAC assessment; (2) our adaptive approach attains better PAC frequency resolution and is more resistant to nonstationarities and artifacts than traditional comodulograms.

A practical approach to parameter estimation applied to model predicting heart rate regulation

Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities. Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting baroreceptor feedback regulation of heart rate during head-up tilt. The three methods include: structured analysis of the correlation matrix, analysis via singular value decomposition followed by QR factorization, and identification of the subspace closest to the one spanned by eigenvectors of the model Hessian. Results showed that all three methods facilitate identification of a parameter subset. The "best" subset was obtained using the structured correlation method, though this method was also the most computationally intensive. Subsets obtained using the other two methods were easier to compute, but analysis revealed that the final subsets contained correlated parameters. In conclusion, to avoid lengthy computations, these three methods may be combined for efficient identification of parameter subsets.

A data-driven noise reduction method and its application for the enhancement of stress wave signals

Ensemble empirical mode decomposition (EEMD) has been recently used to recover a signal from observed noisy data. Typically this is performed by partial reconstruction or thresholding operation. In this paper we describe an efficient noise reduction method. EEMD is used to decompose a signal into several intrinsic mode functions (IMFs). The time intervals between two adjacent zero-crossings within the IMF, called instantaneous half period (IHP), are used as a criterion to detect and classify the noise oscillations. The undesirable waveforms with a larger IHP are set to zero. Furthermore, the optimum threshold in this approach can be derived from the signal itself using the consecutive mean square error (CMSE). The method is fully data driven, and it requires no prior knowledge of the target signals. This method can be verified with the simulative program by using Matlab. The denoising results are proper. In comparison with other EEMD based methods, it is concluded that the means adopted in this paper is suitable to preprocess the stress wave signals in the wood nondestructive testing.

Impaired cerebral autoregulation is associated with brain atrophy and worse functional status in chronic ischemic stroke

Dynamic cerebral autoregulation (dCA) is impaired following stroke. However, the relationship between dCA, brain atrophy, and functional outcomes following stroke remains unclear. In this study, we aimed to determine whether impairment of dCA is associated with atrophy in specific regions or globally, thereby affecting daily functions in stroke patients.

We performed a retrospective analysis of 33 subjects with chronic infarctions in the middle cerebral artery territory, and 109 age-matched non-stroke subjects. dCA was assessed via the phase relationship between arterial blood pressure and cerebral blood flow velocity. Brain tissue volumes were quantified from MRI. Functional status was assessed by gait speed, instrumental activities of daily living (IADL), modified Rankin Scale, and NIH Stroke Score.

Compared to the non-stroke group, stroke subjects showed degraded dCA bilaterally, and showed gray matter atrophy in the frontal, parietal and temporal lobes ipsilateral to infarct. In stroke subjects, better dCA was associated with less temporal lobe gray matter atrophy on the infracted side ( = 0.029), faster gait speed ( = 0.018) and lower IADL score (0.002). Our results indicate that better dynamic cerebral perfusion regulation is associated with less atrophy and better long-term functional status in older adults with chronic ischemic infarctions.

Phase-amplitude investigation of spontaneous low-frequency oscillations of cerebral hemodynamics with near-infrared spectroscopy: a sleep study in human subjects

We have investigated the amplitude and phase of spontaneous low-frequency oscillations (LFOs) of the cerebral deoxy- and oxy-hemoglobin concentrations ([Hb] and [HbO]) in a human sleep study using near-infrared spectroscopy (NIRS). Amplitude and phase analysis was based on the analytic signal method, and phasor algebra was used to decompose measured [Hb] and [HbO] oscillations into cerebral blood volume (CBV) and flow velocity (CBFV) oscillations. We have found a greater phase lead of [Hb] vs. [HbO] LFOs during non-REM sleep with respect to the awake and REM sleep states (maximum increase in [Hb] phase lead: ~π/2). Furthermore, during non-REM sleep, the amplitudes of [Hb] and [HbO] LFOs are suppressed with respect to the awake and REM sleep states (maximum amplitude decrease: 87%). The associated cerebral blood volume and flow velocity oscillations are found to maintain their relative phase difference during sleep, whereas their amplitudes are attenuated during non-REM sleep. These results show the potential of phase-amplitude analysis of [Hb] and [HbO] oscillations measured by NIRS in the investigation of hemodynamics associated with cerebral physiology, activation, and pathological conditions.

A nonlinear dynamic approach reveals a long-term stroke effect on cerebral blood flow regulation at multiple time scales

Cerebral autoregulation (CA) is an important vascular control mechanism responsible for relatively stable cerebral blood flow despite changes of systemic blood pressure (BP). Impaired CA may leave brain tissue unprotected against potentially harmful effects of BP fluctuations. It is generally accepted that CA is less effective or even inactive at frequencies >∼0.1 Hz. Without any physiological foundation, this concept is based on studies that quantified the coupling between BP and cerebral blood flow velocity (BFV) using transfer function analysis. This traditional analysis assumes stationary oscillations with constant amplitude and period, and may be unreliable or even invalid for analysis of nonstationary BP and BFV signals. In this study we propose a novel computational tool for CA assessment that is based on nonlinear dynamic theory without the assumption of stationary signals. Using this method, we studied BP and BFV recordings collected from 39 patients with chronic ischemic infarctions and 40 age-matched non-stroke subjects during baseline resting conditions. The active CA function in non-stroke subjects was associated with an advanced phase in BFV oscillations compared to BP oscillations at frequencies from ∼0.02 to 0.38 Hz. The phase shift was reduced in stroke patients even at > = 6 months after stroke, and the reduction was consistent at all tested frequencies and in both stroke and non-stroke hemispheres. These results provide strong evidence that CA may be active in a much wider frequency region than previously believed and that the altered multiscale CA in different vascular territories following stroke may have important clinical implications for post-stroke recovery. Moreover, the stroke effects on multiscale cerebral blood flow regulation could not be detected by transfer function analysis, suggesting that nonlinear approaches without the assumption of stationarity are more sensitive for the assessment of the coupling of nonstationary physiological signals.

Cerebral autoregulation is an important mechanism that regulates blood supply to brain tissue to match metabolic demands during daily activities. Impaired cerebral autoregulation increases the dependence of cerebral blood flow on systemic blood pressure, and is associated with fatal outcomes in patients after brain injury and acute ischemic stroke. Reliable and noninvasive assessment of cerebral autoregulation is still a major challenge in medical diagnostics and clinic studies, mainly because blood pressure and flow are intrinsically nonstationary (possessing complex oscillations/fluctuations with varying amplitude and frequency) while traditional methods for assessment of the pressure-flow dependence assume stationary signals. We propose a new computational technique that is based on nonlinear theories without the assumption of stationary signals. This technique allows us to detect the degradation of cerebral autoregulation in patients with mild ischemic stroke even at >6 months after the insult. The degradation was present in both stroke and non-stroke sides and was accompanied by an altered pressure-flow interaction over a wide range of frequencies from 0.02–0.38 Hz. Our results challenges the traditionally accepted functional region of autoregulation (<∼0.1 Hz). The observed long-term influences of stroke highlight the importance of reliable monitoring of cerebral blood flow regulation for the management and daily care of stroke patients.

Stress wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model

Stress-wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary stress wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to stress wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real stress wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered stress waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.

The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure--a pilot study of multiscale entropy

AIMS

The influences of nonstationarity and nonlinearity on heart rate time series can be mathematically qualified or quantified by multiscale entropy (MSE). The aim of this study is to investigate the prognostic value of parameters derived from MSE in the patients with systolic heart failure.

METHODS AND RESULTS

Patients with systolic heart failure were enrolled in this study. One month after clinical condition being stable, 24-hour Holter electrocardiogram was recording. MSE as well as other standard parameters of heart rate variability (HRV) and detrended fluctuation analysis (DFA) were assessed. A total of 40 heart failure patients with a mea age of 56±16 years were enrolled and followed-up for 684±441 days. There were 25 patients receiving β-blockers treatment. During follow-up period, 6 patients died or received urgent heart transplantation. The short-term exponent of DFA and the slope of MSE between scale 1 to 5 were significantly different between patients with or without β-blockers (p = 0.014 and p = 0.028). Only the area under the MSE curve for scale 6 to 20 (Area(6-20)) showed the strongest predictive power between survival (n = 34) and mortality (n = 6) groups among all the parameters. The value of Area(6-20)21.2 served as a significant predictor of mortality or heart transplant (p = 0.0014).

CONCLUSION

The area under the MSE curve for scale 6 to 20 is not relevant to β-blockers and could further warrant independent risk stratification for the prognosis of CHF patients.

Decomposing the association of completed suicide with air pollution, weather, and unemployment data at different time scales

BACKGROUND

Research has implicated environmental risk factors, such as meteorological variables, in suicide. However, studies have not investigated air pollution, known to induce acute medical conditions and increase mortality, in suicide. This study comprehensively assesses the temporal relationship between suicide and air pollution, weather, and unemployment variables in Taipei City from January 1 1991 to December 31 2008.

METHODS

This research used the empirical mode decomposition (EMD) method to de-trend the suicide data into a set of intrinsic oscillations, called intrinsic mode functions (IMFs). Multiple linear regression analysis with forward stepwise method was used to identify significant predictors of suicide from a pool of air pollution, weather, and unemployment data, and to quantify the temporal association between decomposed suicide IMFs with these predictors at different time scales.

RESULTS

Findings of this study predicted a classic seasonal pattern of increased suicide occurring in early summer by increased air particulates and decreased barometric pressure, in which the latter was in accordance with increased temperature during the corresponding time. Gaseous air pollutants, such as sulfur dioxide and ozone, were found to increase the risk of suicide at longer time scales. Decreased sunshine duration and sunspot activity predicted the increased suicide. After controlling for the unemployment factor, environmental risks predicted 33.7% of variance in the suicide data.

CONCLUSIONS

Using EMD analysis, this study found time-scale dependent associations between suicide and air pollution, weather and unemployment data. Contributing environmental risks may vary in different geographic regions and in different populations.

Acute increase of complexity in the neurocardiovascular dynamics following carotid stenting

OBJECTIVE

Percutaneous carotid angioplasty and stenting (CAS) has been used to improve cerebral circulation and autoregulation. However, whether CAS ameliorates the autonomic regulatory dynamics remains unclear. This prospective study examines the neurocardiovascular dynamics following carotid stenting.

METHODS

Thirty minutes electrocardiograms were recorded at three different time points (pre-operative, 1-h post-operative, 1-day post-operative) on twelve male patients (mean age 70.8 ± 9.6 years) receiving unilateral primary CAS. The HR data were analyzed by the conventional heart rate variability (HRV) and the multiscale entropy (MSE) methods; the former associates with autonomic activities and the latter quantifies the regulatory complexity of heart beat intervals. Loss of complexity at multiple scales has been associated with decoupled regulatory network in vivo.

RESULTS

Conventional HRV indices showed no difference after CAS. Complexity indices increased significantly on scales 2-8 at 1-h and on scales 2-3 1-day post-treatment. The lower scale MSE (1-5) correlated with the frequency components of conventional HRV indices. The increased complexity could imply a restoration of the neurocardiovascular dynamics on the path to a healthier state.

CONCLUSIONS

Primary CAS can induce a recovery in the neurocardiovascular regulatory dynamics in patients with high-grade carotid stenosis.

Temporal associations between weather and headache: analysis by empirical mode decomposition

BACKGROUND

Patients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area.

METHODOLOGY/PRINCIPAL FINDINGS

The study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts.

CONCLUSIONS/SIGNIFICANCE

Using EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons.

Dynamic cerebral autoregulation: different signal processing methods without influence on results and reproducibility

Cerebral autoregulation controls cerebral blood flow under changing cerebral perfusion pressure. Standards for measurement and analysis of dynamic cerebral autoregulation (dCA) are lacking. In this study, dCA reproducibility, quantified by intraclass correlation coefficient, is evaluated for different methodological approaches of transfer function analysis (TFA) and compared with multimodal pressure flow analysis (MMPF). dCA parameters were determined in 19 healthy volunteers during three 15-min lasting epochs of spontaneous breathing. Every spontaneous breathing epoch was followed by 5 min of paced breathing at 6 cycles/min. These six measurements were performed in both a morning and an afternoon session. Analysis compared raw data pre-processing by mean subtraction versus smoothness priors detrending. The estimation of spectral density was either performed by averaging of subsequent time windows or by smoothing the spectrum of the whole recording. No significant influence of pre-processing and spectral estimation on dCA parameters was found. Therefore, there seems to be no need to prescribe a specific signal-processing regime. Poor reproducibility of gain and phase was found for TFA as well as for MMPF. Based on reproducibility, no preference can be made for morning versus afternoon measurements, neither for spontaneous versus paced breathing. Finally, reproducibility results are not in favour of TFA or MMPF.

Do seasons have an influence on the incidence of depression? The use of an internet search engine query data as a proxy of human affect

BACKGROUND

Seasonal depression has generated considerable clinical interest in recent years. Despite a common belief that people in higher latitudes are more vulnerable to low mood during the winter, it has never been demonstrated that human's moods are subject to seasonal change on a global scale. The aim of this study was to investigate large-scale seasonal patterns of depression using Internet search query data as a signature and proxy of human affect.

METHODOLOGY/PRINCIPAL FINDINGS

Our study was based on a publicly available search engine database, Google Insights for Search, which provides time series data of weekly search trends from January 1, 2004 to June 30, 2009. We applied an empirical mode decomposition method to isolate seasonal components of health-related search trends of depression in 54 geographic areas worldwide. We identified a seasonal trend of depression that was opposite between the northern and southern hemispheres; this trend was significantly correlated with seasonal oscillations of temperature (USA: r = -0.872, p<0.001; Australia: r = -0.656, p<0.001). Based on analyses of search trends over 54 geological locations worldwide, we found that the degree of correlation between searching for depression and temperature was latitude-dependent (northern hemisphere: r = -0.686; p<0.001; southern hemisphere: r = 0.871; p<0.0001).

CONCLUSIONS/SIGNIFICANCE

Our findings indicate that Internet searches for depression from people in higher latitudes are more vulnerable to seasonal change, whereas this phenomenon is obscured in tropical areas. This phenomenon exists universally across countries, regardless of language. This study provides novel, Internet-based evidence for the epidemiology of seasonal depression.

Arterial stiffness using radial arterial waveforms measured at the wrist as an indicator of diabetic control in the elderly

Although current technique of photoplethysmography (PPG) is a popular noninvasive method of waveform contour analysis in assessing arterial stiffness, data obtained are frequently affected by various environmental and physiological factors. We proposed an easily operable air pressure sensing system (APSS) for radial arterial signal capturing. Totally, 108 subjects (young, the aged with or without diabetes) were recruited from July 2009 to May 2010. Arterial waveform signals from the wrist were obtained and analyzed using Hilbert-Huang transformation (HHT). Through ensemble empirical mode decomposition (EEMD), the signals were decomposed into eight intrinsic mode functions (IMF1-8) of which IMF5 was found to be the desired signal with a discernible diastolic peak. The results showed significant differences in reflection index (RI) and stiffness index (SI) from the young subjects and those from the aged participants with or without diabetes. Significant differences in RI and SI were also noted between subjects with well-controlled diabetes and those without. Good reproducibility and correlation were demonstrated. In conclusion, the present study proposed the application of radial arterial signal capturing subsystem and HHT in acquiring more reliable data on RI and SI compared with the conventional PPG method.

Cerebral flow velocities during daily activities depend on blood pressure in patients with chronic ischemic infarctions

BACKGROUND AND PURPOSE

Target blood pressure (BP) values for optimal cerebral perfusion after an ischemic stroke are still debated. We sought to examine the relationship between BP and cerebral blood flow velocities (BFVs) during daily activities.

METHODS

We studied 43 patients with chronic large vessel ischemic infarctions in the middle cerebral artery territory (aged 64.2+/-8.94 years; at 6.1+/-4.9 years after stroke) and 67 age-matched control subjects. BFVs in middle cerebral arteries were measured during supine baseline, sitting, standing, and tilt. A regression analysis and a dynamic phase analysis were used to quantify the BP-BFV relationship.

RESULTS

The mean arterial pressure was similar between the groups (89+/-15 mm Hg). Baseline BFVs were lower by approximately 30% in the patients with stroke compared with the control subjects (P=0.0001). BFV declined further with postural changes and remained lower in the stroke group during sitting (P=0.003), standing (P=0.003), and tilt (P=0.002) as compared with the control group. Average BFVs on the stroke side were positively correlated with BP during baseline (R=0.54, P=0.0022, the slope 0.46 cm/s/mm Hg) and tilt (R=0.52, P=0.0028, the slope 0.40 cm/s/mm Hg). Regression analysis suggested that BFV may increase approximately 30% to 50% at mean BP >100 mm Hg. Orthostatic hypotension during the first minute of tilt or standing was independently associated with lower BFV on the stroke side (P=0.0008). Baseline BP-BFV phase shift derived from the phase analysis was smaller on the stroke side (P=0.0006).

CONCLUSIONS

We found that BFVs are lower in patients with stroke and daily activities such as standing could induce hypoperfusion. BFVs increase with mean arterial pressure >100 mm Hg. Dependency of BFV on arterial pressure may have implications for BP management after stroke. Further prospective investigations are needed to determine the impact of these findings on functional recovery and strategies to improve perfusion pressure during daily activities after ischemic stroke.