Time-frequency Spectral Analysis Research Articles

Differences in peripheral microcirculatory blood flow regulation in chronic kidney disease based on wavelet analysis of resting near-infrared spectroscopy

Vascular impairment is closely related to increased mortality in chronic kidney disease (CKD). The objective of this study was to assess impairments in the regulation of peripheral microvascular perfusion in patients with CKD based on time-frequency spectral analysis of resting near-infrared spectroscopy (NIRS) signals. Total hemoglobin (tHb) concentration and tissue saturation index (TSI) signals were collected using NIRS for a continuous 5 mins at 10 Hz from the forearm of 55 participants (34 CKD including 5 with end-stage renal disease, and 21 age-matched control). Continuous wavelet transform-based spectral analysis was used to quantify the spectral amplitude within five pre-defined frequency intervals (I, 0.0095–0.021 Hz; II, 0.021–0.052 Hz; III, 0.052–0.145 Hz; IV, 0.145–0.6 Hz and V, 0.6–2.0 Hz), representing endothelial, neurogenic, myogenic, respiratory and heartbeat activity, respectively. CKD patients showed lower tHb average spectral amplitude within the neurogenic frequency interval compared with controls (p = 0.014), consistent with an increased sympathetic outflow observed in CKD. CKD patients also showed lower TSI average spectral amplitude within the endothelial frequency interval compared with controls (p = 0.046), consistent with a reduced endothelial function in CKD. These findings demonstrate the potential of wavelet analysis of NIRS to provide complementary information on peripheral microvascular regulation in CKD.

Analysis of Interharmonics Generation in Induction Motors Driven by Variable Frequency Drives and AC Choppers

In modern power systems, interharmonics have emerged as noninteger frequency components that appear in current or voltage signals. Double-stage AC-DC-AC converters have been identified as a primary source of interharmonics. Previous studies have highlighted modulation techniques, filters, passive components, unbalanced conditions and DC link effects as relevant factors. However, the interaction between these factors and the inertial properties of induction motors has not been explored. This study assesses various types of AC drives, including variable frequency drives (VFDs) and soft starters (AC choppers), to investigate their interaction with machine inertial properties in interharmonic generation. The employed technique for time-frequency spectral analysis is the wavelet synchrosqueezed transform combined with a clustering method. This paper demonstrates that the interaction of the inertial properties of low-power induction motors with the variations of the switching frequencies of the VFDs, the filters used in the DC link and a soft start indirectly contribute to harmonic generation and potentially to interharmonics in the electrical grid and the outputs of the inverter.

Analyzing, modelling, and simulating nonstationary thunderstorm winds in two horizontal orthogonal directions at a point in space

Thunderstorm winds in the horizontal plane are time-varying, resulting in the time-varying alongwind direction and mean wind speed in the alongwind direction. The fluctuating winds in the alongwind and crosswind directions are time-varying as well. The characterization of the mean and fluctuating winds for their simulation in two horizontal orthogonal directions are incomplete or unavailable in the literature. In the present study, we analyze and model the thunderstorm winds with respect to their time- and directional-variation of the alongwind speed and direction, as well as the fluctuating wind components in the alongwind and crosswind directions. For the analysis, we use the continuous harmonic wavelet. We show that the power spectral density (PSD) functions of the fluctuating winds depend on the time-varying mean wind speed. Based on the time-frequency spectral analysis, we propose the PSD functions and lagged coherence functions for the fluctuating thunderstorm winds in the alongwind and crosswind directions. We also present new empirical models for the time-varying alongwind direction and mean wind speed in the alongwind direction. Finally, we suggest the steps to simulate the wind speed in the alongwind and crosswind directions with time-varying directions and provide numerical examples to illustrate the effectiveness of our proposed models.

Transcranial ultrasound neuromodulation of the thalamic visual pathway in a large animal model and the dose-response relationship with MR-ARFI

Neuromodulation of deep brain structures via transcranial ultrasound stimulation (TUS) is a promising, but still elusive approach to non-invasive treatment of brain disorders. The purpose of this study was to confirm that MR-guided TUS of the lateral geniculate nucleus (LGN) can modulate visual evoked potentials (VEPs) in the intact large animal; and to study the impact on cortical brain oscillations. The LGN on one side was identified with T2-weighted MRI in sheep (all male, n = 9). MR acoustic radiation force imaging (MR-ARFI) was used to confirm localization of the targeted area in the brain. Electroencephalographic (EEG) signals were recorded, and the visual evoked potential (VEP) peak-to-peak amplitude (N70 and P100) was calculated for each trial. Time–frequency spectral analysis was performed to elucidate the effect of TUS on cortical brain dynamics. The VEP peak-to-peak amplitude was reversibly suppressed relative to baseline during TUS. Dynamic spectral analysis demonstrated a change in cortical oscillations when TUS is paired with visual sensory input. Sonication-associated microscopic displacements, as measured by MR-ARFI, correlated with the TUS-mediated suppression of visual evoked activity. TUS non-invasively delivered to LGN can neuromodulate visual activity and oscillatory dynamics in large mammalian brains.

Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis.

Evaluating the performance of source apportionment (SA) models is difficult due to the non-observable nature of source contribution in reality. Here we propose a new approach to assess the performance of Chemical Transport Models (CTMs) for SA based on wavelet time-frequency spectral analysis and Grey Incidence Analysis (GIA). For each source category, certain species that better reflect the periodic characteristics of the emission sources were selected as the chemical tracers. The consistency of the time series between the simulated source contributions and the observed source-specific chemical tracers was then examined using a GIA model based on the perspective of similarity, and characterized by the GIA scores. By applying this method to six typical pollution episodes, we evaluated the performance of the Comprehensive Air Quality Model with Extensions-Particle Source Apportionment Technology (CAMx-PSAT) model for PM2.5 SA from different temporal and spatial scales. The source- and episode-dependent optimal average time and main source regions were obtained. This approach is robust for facilitating a relatively meticulous evaluation of the performance of CTMs for PM2.5 SA, and provides additional insight for decision-making for heavy pollution emergencies.

Photoacoustic characterization of bone physico-chemical information.

Osteoporosis usually alters the chemical composition and physical microstructure of bone. Currently, most clinical techniques for bone assessment are focused on the either bone microstructure or bone mineral density (BMD). In this study, a novel multi-wavelength photoacoustic time-frequency spectral analysis (MWPA-TFSA) method was introduced based on the optical absorption spectra and photoacoustic effects of biological macromolecules, which evaluates changes in bone chemical composition and microstructure. The results demonstrated that osteoporotic bones had decreased BMD, more lipids, and wider trabecular separation filled with larger marrow clusters, which were consistent with multiple gold-standard results, suggesting that the MWPA-TFSA method has the potential to provide a thorough bone physico-chemical information evaluation noninvasively and nonradiatively.

Mask filtering to the Wigner-Ville distribution

The Wigner-Ville distribution (WVD) is a high-resolution time-frequency spectral analysis method for nonstationary signals; yet, it suffers from cross-term interference among signal components. We proposed applying a masking filter directly to the WVD time-frequency spectrum to suppress the cross-term interference. Conventional methods for suppressing interference include the smoothed pseudo-WVD (SP-WVD) method, which incorporates smooth filtering in the time and frequency directions. We exploited the SP-WVD spectrum as a reference to design the masking filter; thus, the mask-filtered WVD (MF-WVD) procedure is data-adaptive. The MF-WVD method preserves the high-resolution energy concentration in the spectrum portrayed by the standard WVD, while suppressing the cross-term interference cleanly as in the SP-WVD method. Applying the MF-WVD method to field 3D seismic data generates high-resolution spectral cubes for various frequencies, and these spectral cubes may be used intuitively for detecting reservoir-related characteristics.

EXPERIMENTAL RESEARCH ON FORECASTING INDEX OF BIOLOGICAL IMAGE AEROBIC EXERCISE ANALYSIS OF ARTIFICIAL NEURAL NETWORK

ABSTRACT Objective: The paper uses artificial neural network images to explore the effects of aerobic exercise on the gamma rhythm of theta period in the awake hippocampal CA1 area of APP/PS1/tau mice and the low-frequency gamma rhythm of the sleep state hippocampal CA1 area SWR period. Methods: Clean grade 6-month-old APP/PS1/tau mice were randomly divided into quiet group (AS) and exercise group (AE), C57BL/6J control group mice were randomly divided into quiet group (CS) and exercise group (CE). The AE group and the CE group performed 12-week treadmill exercise, 5d/week, 60min/d, the first 10min exercise load was 12m/min, the last 50min was 15m/min treadmill slope was 0°. Eight-arm maze detection of behavioral changes in mice; multi-channel in vivo recording technology to record the electrical signals of the awake state and sleep state in the hippocampal CA1 area, MATLAB extracts the awake state theta period and sleep state SWR period, multi-window spectrum estimation method Perform time-frequency analysis and power spectral density analysis. Results: 12 weeks of aerobic exercise can significantly improve the working memory and reference memory of the AS group, increase the gamma energy in theta period of the awake hippocampus CA1 area and the low-frequency gamma energy in the sleep state CA1 area SWR period. Conclusions: Aerobic exercise can improve the neural network state of the AD model and increase the gamma energy in theta period of the hippocampus CA1 area, and the low-frequency gamma energy in the SWR period is one of the neural network mechanisms for its overall behavioral improvement. Level of evidence II; Therapeutic studies - investigation of treatment results.

Wavelet transform-based photoacoustic time-frequency spectral analysis for bone assessment

In this study, we investigated the feasibility of using photoacoustic time-frequency spectral analysis (PA-TFSA) for evaluating the bone mineral density (BMD) and bone structure. Simulations and ex vivo experiments on bone samples with different BMDs and mean trabecular thickness (MTT) were conducted. All photoacoustic signals were processed using the wavelet transform-based PA-TFSA. The power-weighted mean frequency (PWMF) was evaluated to obtain the main frequency component at different times. The y-intercept, midband-fit, and slope of the linearly fitted curve of the PWMF over time were also quantified. The results show that the osteoporotic bone samples with lower BMD and thinner MTT have higher frequency components and lower acoustic frequency attenuation over time, thus higher y-intercept, midband-fit, and slope. The midband-fit and slope were found to be sensitive to the BMD; therefore, both parameters could be used to distinguish between osteoporotic and normal bones (p < 0.05).

The W transform

Time-frequency spectral analysis methods such as the S transform cannot appropriately present low-frequency anomalies in seismic traces because they generate a time-frequency spectrum with a low resolution in time at low frequencies. I have developed the W transform to improve the time resolution of the spectrum at low frequencies, for an effective detection of seismic anomalies related to hydrocarbon reservoirs in petroleum exploration and abnormal features in near-surface geophysics. The W transform has three features: (1) the spectral energy is concentrated around the dominant frequency of a seismic waveform, rather than being shifted toward higher frequencies by the S transform; (2) the implementation is numerically stable because it avoids any potential frequency singularity in the S transform; (3) the Gaussian window function is defined using a nonstationary frequency weight, rather than using a stationary frequency weight in the S transform, because the dominant frequency is a time-dependent function. Therefore, the time-frequency spectrum generated by the W transform appropriately represents seismic properties varying with geologic depth, and it has an improved time resolution at low frequencies that makes it suitable for characterizing reservoirs in petroleum geophysics and for detecting karsts in construction engineering.

Multichannel maximum-entropy method for the Wigner-Ville distribution

The Wigner-Ville distribution is a powerful technique for the time-frequency spectral analysis of nonstationary seismic data. However, the Wigner-Ville distribution suffers from cross-term interference between different wave components in seismic data. To mitigate cross-term interference, we have developed a multichannel maximum-entropy method (MEM) to modify the Wigner-Ville kernel. The method is related to the conventional maximum-entropy spectral analysis (MESA) algorithm because both algorithms use Burg’s reflection coefficients for the calculation of the prediction-error filter (PEF). The MESA algorithm works on the standard autocorrelation sequence, but it does not work for the Wigner-Ville kernel, which is an instantaneous autocorrelation sequence. Our multichannel MEM algorithm uses the PEF to modify any single Wigner-Ville kernel sequence by exploiting multiple Wigner-Ville kernel sequences simultaneously. This multichannel implementation is capable of robustly determining the reflection coefficient and a minimum-phased PEF for the Wigner-Ville kernel sequence. The Wigner-Ville distribution and the multichannel MEM algorithm in conjunction with each other in turn can produce a high-resolution time-frequency spectrum by mitigating the cross-term interferences and suppressing the spurious energy in the spectrum.

Enhancing Mu-based BCI Performance with Rhythmic Electrical Stimulation at Alpha Frequency.

The accuracy of brain-computer interfaces (BCIs) is important for effective communication and control. The mu-based BCI is one of the widely used systems, of which the related methods to improve users' accuracy is still poorly studied. Here, we examined the way to enhance the mu-based BCI performance by rhythmic electrical stimulation on the ulnar nerve at the contralateral wrist at the alpha frequency (10 Hz) during the left-and right-hand motor imagery. Time-frequency analysis, spectral analysis, and discriminant analysis were performed on the electroencephalograph (EEG) data before and after the intervention of electrical stimulation in 9 healthy subjects. We found that the ERD/S on the somatosensory and motor cortex during left-or right-hand imagination was more obvious at the mu rhythm after intervention. Furthermore, average classification accuracy between left-and right-hand imagery significantly increased from 78.43% to 88.17% after intervention, suggesting that the electrical stimulation at alpha frequency effectively regulates the brain's mu rhythm and enhances the discriminability of the left-hand and right-hand imagination tasks. These results provide evidence that the electrical stimulation at the alpha frequency is an effective way to improve the mu-based BCI performance.

Platform Session – Electroencephalography/Epilepsy: The neural bases of ictal tachycardia in temporal lobe seizures

Due to limited information from scalp electroencephalographic (EEG) recordings, brain areas driving changes in cardiac rhythm during Temporal lobe (TL) seizures are not clearly identified. Using stereotactic EEG (SEEG) recordings, we aimed at identifying which of the brain regions involved in autonomic control trigger ictal tachycardia. The neural activity of several mesial temporal lobe structures including amygdala, hippocampus, insula, and lateral temporal lobe recorded with SEEG were collected during 37 TL seizures in 9 patients, using indices based on High Frequency Activity (HFA). R-R intervals (RR) monitoring and time-frequency spectral analysis were performed to assess parasympathetic (High frequency power (HF)) and sympathetic (Low frequency/High frequency (LF/HF) ratio) reactivities. Tachycardia was associated with a significant increase in LF/HF ratio and decrease in HF. Autonomic cardiac changes were accompanied by simultaneous SEEG signal changes with an increase in seizure-related HFA in anterior hippocampal formation and amygdala, but not in insula. In our sample, TL seizures are thus accompanied by an early decrease in parasympathetic control of cardiac rhythm and by an increase of sympathetic tone, concomitant to seizure activity in anterior hippocampus and amygdala. These results support a pivotal role of hippocampus and amygdala in tachycardia occurring during TL seizures.

Validation of Heart Rate Monitor Polar RS800 for Heart Rate Variability Analysis During Exercise.

Hernando, D, Garatachea, N, Almeida, R, Casajús, JA, and Bailón, R. Validation of heart rate monitor Polar RS800 for heart rate variability analysis during exercise. J Strength Cond Res 32(3): 716-725, 2018-Heart rate variability (HRV) analysis during exercise is an interesting noninvasive tool to measure the cardiovascular response to the stress of exercise. Wearable heart rate monitors are a comfortable option to measure interbeat (RR) intervals while doing physical activities. It is necessary to evaluate the agreement between HRV parameters derived from the RR series recorded by wearable devices and those derived from an electrocardiogram (ECG) during dynamic exercise of low to high intensity. Twenty-three male volunteers performed an exercise stress test on a cycle ergometer. Subjects wore a Polar RS800 device, whereas ECG was also recorded simultaneously to extract the reference RR intervals. A time-frequency spectral analysis was performed to extract the instantaneous mean heart rate (HRM), and the power of low-frequency (PLF) and high-frequency (PHF) components, the latter centered on the respiratory frequency. Analysis was done in intervals of different exercise intensity based on oxygen consumption. Linear correlation, reliability, and agreement were computed in each interval. The agreement between the RR series obtained from the Polar device and from the ECG is high throughout the whole test although the shorter the RR is, the more differences there are. Both methods are interchangeable when analyzing HRV at rest. At high exercise intensity, HRM and PLF still presented a high correlation (ρ > 0.8) and excellent reliability and agreement indices (above 0.9). However, the PHF measurements from the Polar showed reliability and agreement coefficients around 0.5 or lower when the level of the exercise increases (for levels of O2 above 60%).

VLSI Architecture for Novel Hopping Discrete Fourier Transform Computation

The hopping discrete Fourier transform (HDFT) is a new method applied for time-frequency spectral analysis of time-varying signals. In the implementation of HDFT algorithms, the updating vector transform (UVT) plays a key role, and therefore a novel recursive DFT-based UVT formula is introduced in the proposed design for a HDFT algorithm and its architecture. The perceived advantages can be summarized as: 1) the proposed algorithm reduces the number of multiplications, additions, and coefficients by 42.3%, 33.3%, and 50%, respectively, compared with Park’s method under the settings of an M-sample complex input sequence (M = 256), and an N-point recursive DFT computation scheme (N = 64) for time hop L (L = 4); 2) by adopting the hardware-sharing scheme and the register-shifting concept, the proposed design only takes nine multipliers and 12 adders for realization. The proposed hardware accelerator can be implemented using a field-programmable gate array, which can operate at 48.33 MHz clock rate. The resource utilization of combinational logic lookup tables (LUTs) and digital signal processing (DSP) blocks reduced by 11.7% and 42.5% compared with Juang et al. ’s work. For very-large-scale integration realizations, the proposed design would be more powerful than other existing algorithms in future applications focusing on DSP, filtering, and communications.

AGE-RELATED INVOLUTION OF THE SYMPATHETIC NERVOUS SYSTEM, SUDDEN CARDIAC DEATH AND FEATURES OF DIAGNOSIS OF MYOCARDIAL

Neurological nature of major cardiac diseases is attributed to elevated social tension. The established phenomena of early age involution of the sympathetic part of the vegetative nervous system due to Cannon's law make it possible to study the mechanism of sudden cardiac death and to search for diagnostic methods for its prevention. One of such methods is time-frequency spectral analysis of the heart rate variability at the early phases of orthostatic tests. The radionuclide diagnostic method has revealed new data on correlation between blood capillaries and sympathetic nervous plexus under myocardial infarction.

Esmolol pretreatment attenuates heart rate increase and parasympathetic inhibition during rapid increases in desflurane concentration: A preliminary randomized study.

Background:Rapid increases in desflurane concentration can transiently increase the heart rate (HR). Esmolol possesses a high β1-adrenoceptor selectivity and a short duration of action. This preliminary study aimed at investigating the effects of esmolol on the HR and autonomic modulation during a desflurane-induced HR increase.Methods:American Society of Anesthesiologists physical status I female subjects, aged 20 to 50 years, who were undergoing minor breast surgery were randomly assigned to 2 groups. Rapid increases in desflurane concentration were commenced after induction of anesthesia. Each subject received either i.v. saline (control group) or esmolol 0.5 mg/kg (esmolol group) before desflurane inhalation. Using time-frequency spectral analysis of HR variability, the HR indices were studied at baseline, postinduction, posttreatment, as well as at minimal alveolar concentrations of desflurane reaching 1.0, 1.3, and 1.5. The low frequency (LF) power is influenced by both the sympathetic and parasympathetic activity, whereas the high frequency (HF) power reflects the parasympathetic activity. The LF/HF ratio is thought to reflect either sympathovagal balance or sympathetic modulation.Results:Electrocardiograms for data analysis were obtained from 8 subjects in each group. Rapid increases in desflurane concentration after induction caused a HR increase. Both the corresponding LF and HF powers were low and the LF/HF ratio remained unchanged. This indicates that the desflurane-induced HR increase may be attributed to parasympathetic inhibition and may be independent of sympathetic activation. Esmolol pretreatment effectively attenuated desflurane-induced HR increase. Moreover, subjects receiving esmolol pretreatment had increased LF and HF powers, but did not have changes in their LF/HF ratios, as compared to those without esmolol.Conclusion:Esmolol pretreatment attenuates HR increase and parasympathetic inhibition during rapid increases in desflurane concentration.

The spectral features of EEG responses to transcranial magnetic stimulation of the primary motor cortex depend on the amplitude of the motor evoked potentials.

Transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) can excite both cortico-cortical and cortico-spinal axons resulting in TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs), respectively. Despite this remarkable difference with other cortical areas, the influence of motor output and its amplitude on TEPs is largely unknown. Here we studied TEPs resulting from M1 stimulation and assessed whether their waveform and spectral features depend on the MEP amplitude. To this aim, we performed two separate experiments. In experiment 1, single-pulse TMS was applied at the same supra-threshold intensity on primary motor, prefrontal, premotor and parietal cortices and the corresponding TEPs were compared by means of local mean field power and time-frequency spectral analysis. In experiment 2 we stimulated M1 at resting motor threshold in order to elicit MEPs characterized by a wide range of amplitudes. TEPs computed from high-MEP and low-MEP trials were then compared using the same methods applied in experiment 1. In line with previous studies, TMS of M1 produced larger TEPs compared to other cortical stimulations. Notably, we found that only TEPs produced by M1 stimulation were accompanied by a late event-related desynchronization (ERD—peaking at ~300 ms after TMS), whose magnitude was strongly dependent on the amplitude of MEPs. Overall, these results suggest that M1 produces peculiar responses to TMS possibly reflecting specific anatomo-functional properties, such as the re-entry of proprioceptive feedback associated with target muscle activation.

The neural bases of ictal tachycardia in temporal lobe seizures.

Due to limited information from scalp electroencephalographic (EEG) recordings, brain areas driving changes in cardiac rhythm during Temporal lobe (TL) seizures are not clearly identified. Using stereotactic EEG (SEEG) recordings, we aimed at identifying which of the brain regions involved in autonomic control trigger ictal tachycardia. The neural activity of several mesial temporal lobe structures including amygdala, hippocampus, insula, and lateral temporal lobe recorded with SEEG were collected during 37 TL seizures in 9 patients, using indices based on High Frequency Activity (HFA). R-R intervals (RR) monitoring and time-frequency spectral analysis were performed to assess parasympathetic (High frequency power (HF)) and sympathetic (Low frequency/High frequency (LF/HF) ratio) reactivities. Tachycardia was associated with a significant increase in LF/HF ratio and decrease in HF. Autonomic cardiac changes were accompanied by simultaneous SEEG signal changes with an increase in seizure-related HFA in anterior hippocampal formation and amygdala, but not in insula. In our sample, TL seizures are thus accompanied by an early decrease in parasympathetic control of cardiac rhythm and by an increase of sympathetic tone, concomitant to seizure activity in anterior hippocampus and amygdala. These results support a pivotal role of hippocampus and amygdala in tachycardia occurring during TL seizures.

Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis.

During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible-elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications.