Changes In Frequency Components Research Articles

Studying Erythromelalgia Using Doppler Flowmetry Perfusion Signals and Wavelet Analysis-An Exploratory Study.

Erythromelalgia (EM) is a rare disease, which is still poorly characterized. In the present paper, we compared the hand perfusion of one female EM patient, under challenges, with a healthy control group. Using a laser Doppler flowmeter (LDF) with an integrated thermal probe, measurements were taken in both hands at rest (Phase I) and after two separate challenges-post-occlusive hyperemia (PORH) in one arm (A) and reduction of skin temperature (cooling) with ice in one hand (B) (Phase II). The final measurement periods corresponded to recovery (Phases III and IV). The control group involved ten healthy women (27.3 ± 7.9 years old). A second set of measurements was taken in the EM patient one month after beginning a new therapeutic approach with beta-blockers (6.25 mg carvedilol twice daily). Z-scores of the patient's LDF and temperature fluctuations compared to the control group were assessed using the Wavelet transform (WT) analysis. Here, fluctuations with |Z| > 1.96 were considered significantly different from healthy values, whereas positive or negative Z values indicated higher or lower deviations from the control mean values. Cooling elicited more measurable changes in LDF and temperature fluctuations, especially in higher frequency components (cardiac, respiratory, and myogenic), whereas PORH notably evoked changes in lower frequency components (myogenic, autonomic, and endothelial). No significant Z-score deviations were observed in the second measurement, which might signify a stabilization of the patient's distal perfusion following the new therapeutic approach. This analysis involving one EM patient, while clearly exploratory, has shown significant deviations in WT-derived physiological components' values in comparison with the healthy group, confirming the interest in using cold temperature as a challenger. The apparent agreement achieved with the clinical evaluation opens the possibility of expanding this approach to other patients and pathologies in vascular medicine.

Application of Variational Mode decomposition in GPR Detection of Oil Contaminated Soil

In this article, Ground penetrating radar (GPR) is used to investigate the contaminated soil by oil pollutants in oilfield operation area. Time frequency analysis method is applied to GPR data to reveal the changes of GPR signal frequency components with time variation, which is very important for data interpretation. We use variational mode decomposition (VMD) for time frequency analysis. It can decompose radar signal into several quasi-orthogonal intrinsic mode functions (IMFs) with limited bandwidth, and has strong robustness and local decomposition ability. We apply the VMD on field data acquired in the area with waste liquid contaminated soil in oilfield. The decomposed IMFs from GPR data shows that the signals from the contaminated soil is characterized by the strong energy and low frequency. Then the instantaneous attribute analysis of the IMF is carried out to delineate the scope and thickness of contaminated soil. The experimental results show that the limited bandwidth IMF with specific sparsity obtained by VMD method is more conducive to the processing and interpretation of GPR data.

Sparse unscaled time-frequency transform and its application on seismic attenuation delineation

Seismic attenuation can be qualitatively delineated by the difference between low- and high-frequency components before and after attenuation. Time-frequency (TF) transform is a useful tool that is commonly used in the characterization of frequency component changes caused by seismic attenuation, in which the representative S-transform (ST) often is used for solving this issue. However, ST has a fixed TF localization and a significant frequency shift issue, leading to low resolution and accuracy of the results when used for qualitative attenuation estimation. To solve these issues, this study introduces an unscaled generalized ST (UGST) based on the unscaled ST to avoid the dominant frequency shift and achieve flexible adjustment of the TF localization. To further improve the resolution of TF, the solution of UGST is regarded as a convex optimization process with [Formula: see text]-[Formula: see text]-norm joint constraints, and the sparse UGST (SUGST) is solved by using the Bregman iterative algorithm. Synthetic experiments indicate that SUGST has high TF resolution and dominant frequency fidelity. Qualitative attenuation delineations of synthetic and field data are consistent with the synthetic model and reservoir interpretation. Its resolution and accuracy also are better than contrast methods, which further demonstrate the validity and advantage of the SUGST-based workflow in seismic attenuation delineation and reservoir interpretation.

Surface electromyographic frequency characteristics of the quadriceps differ between continuous high- and low-torque isometric knee extension to momentary failure.

Surface EMG (sEMG) has been used to compare loading conditions during exercise. Studies often explore mean/median frequencies. This potentially misses more nuanced electrophysiological differences between exercise tasks. Therefore, wavelet-based analysis was used to evaluate electrophysiological characteristics in the sEMG signal of the quadriceps under both higher- and lower-torque (70% and 30% of MVC, respectively) isometric knee extension performed to momentary failure. Ten recreationally active adult males with previous resistance training experience were recruited. Using a within-session, repeated-measures, randomised crossover design, participants performed isometric knee extension whilst sEMG was collected from the vastus medialis (VM), rectus femoris (RF) and vastus lateralis (VL). Mean signal frequency showed similar characteristics in each condition at momentary failure. However, individual wavelets revealed different frequency component changes between the conditions. All frequency components increased during the low-torque condition. But low-frequency components increased, and high-frequency components decreased, in intensity throughout the high-torque condition. This resulted in convergence of the low-torque and high-torque trial wavelet characteristics towards the end of the low-torque trial. Our results demonstrate a convergence of myoelectric signal properties between low- and high-torque efforts with fatigue via divergent signal adaptations. Further work should disentangle factors influencing frequency characteristics during exercise tasks.

Changes in EEG frequency characteristics during sevoflurane general anesthesia: feature extraction by variational mode decomposition.

Mode decomposition is a method for extracting the characteristic intrinsic mode function (IMF) from various multidimensional time-series signals. Variational mode decomposition (VMD) searches for IMFs by optimizing the bandwidth to a narrow band with the [Formula: see text] norm while preserving the online estimated central frequency. In this study, we applied VMD to the analysis of electroencephalogram (EEG) recorded during general anesthesia. Using a bispectral index monitor, EEGs were recorded from 10 adult surgical patients (the median age: 47.0, and the percentile range: 27.0-59.3years) who were anesthetized with sevoflurane. We created an application named EEG Mode Decompositor, which decomposes the recorded EEG into IMFs and displays the Hilbert spectrogram. Over the 30-min recovery from general anesthesia, the median (25-75 percentile range) bispectral index increased from 47.1 (42.2-50.4) to 97.4 (96.5-97.6), and the central frequencies of IMF-1 showed a significant change from 0.4 (0.2-0.5) Hz to 0.2 (0.1-0.3) Hz. IMF-2, IMF-3, IMF-4, IMF-5, and IMF-6 increased significantly from 1.4 (1.2-1.6) Hz to 7.5 (1.5-9.3) Hz, 6.7 (4.1-7.6) Hz to 19.4 (6.9-20.0) Hz, 10.9 (8.8-11.4) Hz to 26.4 (24.2-27.2) Hz, 13.4 (11.3-16.6) Hz to 35.6 (34.9-36.1) Hz, and 12.4 (9.7-18.1) Hz to 43.2 (42.9-43.4) Hz, respectively. The characteristic frequency component changes in specific IMFs during emergence from general anesthesia were visually captured by IMFs derived using VMD. EEG analysis by VMD is useful for extracting distinct changes during general anesthesia.

Evaluation of Drug Abuse on Brain Function using Power Spectrum Analysis of Electroencephalogram Signals in Methamphetamine, Opioid, Cannabis, and Multi-Drug Abuser Groups

The effect of different types of substances on brain function is still challenging; however, many studies have shown the functional and structural damage to the brain under influence of substance abuse. This study aimed to quantitatively compare the effect of opioid (Op), methamphetamine (Meth), cannabis (Can), and simultaneous methamphetamine and opioid (Multi-Drug (MD)) abuse on brain function. Furthermore, the impacts of pure Op and Meth abuse were considered with simultaneous substance abuse. In this descriptive study, the electroencephalogram (EEG) signal was recorded from 52 participants in the Meth, Op, Can, and MD abusers, and the Healthy Control (HC) groups at rest state. EEG data were analyzed on the frequency domain with electrode-based, cortex-based, and hemisphere-based approaches. However, the power spectrum in the delta band in the Op group, the gamma band in the Can group, and the gamma and beta bands in the MD group more significantly increased compared to the HC group, the power spectrum values in the Meth group reduced in the alpha, beta, and gamma bands. Moreover, the power spectrum values in the MD group more significantly higher than the Meth and Op groups in the beta and gamma bands. Since substance abuse in different types caused various changes in frequency components, the different power spectrum bands analysis in abusers can be reasonable to apply as a biomarker to detect the drug types.

Relative pitch representations and invariance to timbre

Information in speech and music is often conveyed through changes in fundamental frequency (f0), perceived by humans as “relative pitch”. Relative pitch judgments are complicated by two facts. First, sounds can simultaneously vary in timbre due to filtering imposed by a vocal tract or instrument body. Second, relative pitch can be extracted in two ways: by measuring changes in constituent frequency components from one sound to another, or by estimating the f0 of each sound and comparing the estimates. We examined the effects of timbral differences on relative pitch judgments, and whether any invariance to timbre depends on whether judgments are based on constituent frequencies or their f0. Listeners performed up/down and interval discrimination tasks with pairs of spoken vowels, instrument notes, or synthetic tones, synthesized to be either harmonic or inharmonic. Inharmonic sounds lack a well-defined f0, such that relative pitch must be extracted from changes in individual frequencies. Pitch judgments were less accurate when vowels/instruments were different compared to when they were the same, and were biased by the associated timbre differences. However, this bias was similar for harmonic and inharmonic sounds, and was observed even in conditions where judgments of harmonic sounds were based on f0 representations. Relative pitch judgments are thus not invariant to timbre, even when timbral variation is naturalistic, and when such judgments are based on representations of f0.

Sonic Visualiser: Visualisation, Analysis, and Annotation of Music Audio Recordings

Sonic Visualiser: Visualisation, Analysis, and Annotation of Music Audio Recordings

Colorless movement of focal spot with arbitrary velocity based on mirrors with special shape

Focal spot with moving speed is a new concept proposed in recent years, which has many applications in physics of intense field. The current method is to control the moving speed of focal spot by chirped pulse technique and chromatic aberration focusing, which suffers from the change of frequency components with the movement of focal spot. In this paper, we propose a method to realize the colorless movement of focal spot at arbitrary speed by using two kinds of mirrors. One is an aspherical mirror with long focal depth and the other is a stepped mirror. For a specific moving speed, we derive the relationship between the surface of aspherical mirror and the wavefront of laser by the geometrical optics. The stepped mirror is used to form stepped wavefront to separate the relationship between the shape of wavefront and the direction of laser. Time-dependent simulations are carried out to verify the real focusing property after considering diffraction effects, and focal spots with moving speed of 0.5c, 2c and −c are obtained.

Perceptual visual security assessment by fusing local and global feature similarity

Selective encryption is an effective technique for multimedia big data encryption. A perceptual metric that takes the human visual system (HVS) into account is necessary for evaluating the visual security degrees of selectively encrypted images. Existing metrics are mainly based on the similarity of structural information which is represented by local spatial contrast features. However, visual security is concerned with not only the distortion of structural information but also the leakage of important visual information. The local features cannot exactly express the leakage of important visual information. This paper presents a perceptual visual security assessment metric by fusing local and global feature similarity. Considering the HVS response, the proposed metric measures in three aspects: the distortion of structural information, the leakage of important visual information, and the changes of frequency components. To measure the distortion of structure information, local pattern similarity is calculated based on the normalized Hamming distance between the local binary pattern (LBP) binary codes. The similarity of the global LBP histogram is computed to evaluate the leakage of important visual information. A lowpass weighted discrete cosine transform (DCT) frequency similarity is presented to detect the changes of various frequency components. Experimental results demonstrate that the proposed metric achieves significantly higher performance and stronger robustness than the state-of-the-art metrics.

Monitoring method for femtosecond laser modification of silicon carbide via acoustic emission techniques

The interaction between femtosecond laser (fs-laser) and silicon carbide (SiC) is complex, and it will form various modified surface structures related to the physical phenomena of melting, evaporation and plasma. Acoustic emission (AE) is used as a non-destructive real-time detection method that can capture local changes in materials and monitor the process of fs-laser modification of silicon carbide. In this paper, AE analysis, including single parameter analysis, correlation analysis and short time Fourier transform (STFT), are used to analyze the process of laser modified SiC. The relationships between material removal, surface structure characteristics, surface defects and AE signals are established. Results indicate that the material removal of SiC surface ablated by fs-laser increases with the laser fluence, which results in the increase of MARSE and high-frequency component scatters (300 ∼ 400 kHz) as well as the dramatic change of Root Mean Square (RMS). The different surface structures formed on the SiC are classified and characterized by AE signals. The similar modified surface structure causes signal scatter to overlap, proving that the clustering characteristics of the AE signals are closely related to the modified surface structures. The color change of frequency components in the STFT spectrum is related to the surface defects generated in the process of fs-laser modification. The larger the laser fluence irradiate on the area, the more evenly the color are distributed.

Multi-channel spectrograms for speech processing applications using deep learning methods

Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature maps. In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. However, this may limit the potential of convolutional networks to learn different representations of the audio signal. In this paper, we propose a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to extract phone-attribute features. For this, two different deep learning-based models are considered: convolutional neural networks and recurrent neural networks with convolution layers.

Classification of epilepsy period based on combination feature extraction methods and spiking swarm intelligent optimization algorithm

SummaryEpilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time‐frequency features and principal component features of epilepsy. The time‐frequency features are obtained by WPT or STFT‐PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance.

An Image Processing Approach to Machine Fault Diagnosis Based on Visual Words Representation

Machine fault diagnosis and remaining service life prognosis provide the basis for condition-based maintenance, and is key to operational reliability. Accurate assessment of machine health requires effective analysis of vibration data, which is typically performed by examining the change in frequency components. One limitation associated with these methods is the empirical knowledge required for fault feature selection. This paper presents an image processing approach to automatically extract features from vibration signal, based on visual words representation. Specifically, a time-frequency image of vibration signal is obtained through wavelet transform, which is then used to extract “visual word” features for recognizing fault related patterns. The extracted features are subsequently fed into sparse representation-based classifier for classification. Evaluation using experimental bearing data confirmed the effectiveness of the developed method with a classification accuracy of 99.7%.

Stabilizing Graphically Extended Hand for Hand Tremors

Tremors, which refer the trembling or shaking of body parts, are caused by the involuntary contractions of muscles and are one of the most common movement disorders that often reduce individuals’ the levels of the hand functions and also affect their independent body movements adversely. “Extended Hand (EH)” is an augmented body technology, which visually extends the length of a user’s arm by a spatial augmented reality. It could facilitate individuals whose reaching areas are limited by body movement disorders to interact with distant objects. However, the technology is currently limited, because any trembling of the hands contacting with the touch panel of the EH system is amplified, causing EH to shake in the physical world as well. To make EH benefit individuals whose hands and body are disabled by tremors, we proposed methods for stabilizing the movements and gestures of the EH to allow EH controlled steadily by their trembling hands. We developed a hand tremor simulator by which trembling hands interacting with EH were imitated, and investigated the effectiveness of stabilization methods by conducting a series of pointing and grasping experiments. The stabilization method composed of Kalman filter and Hysteresis was confirmed to be effective in reducing the unstable translation and unstable stationary state of EH, and the method combining the stabilization of the distances from the fingertips to their centroid and the analysis of the change in low frequency components of the touching areas effectively enable trembling hands to control the EH gestures steadily.

Motor unit firing frequency of lower limb muscles during an incremental slide board skating test

This study investigated how the combination of workload and fatigue affected the frequency components of muscle activation and possible recruitment priority of motor units during skating to exhaustion. Ten male competitive speed skaters performed an incremental maximal test on a slide board. Activation of six muscles from the right leg was recorded throughout the test. A time-frequency analysis was performed to compute overall, high, and low frequency bands from the whole signal at 10, 40, 70, and 90% of total test time. Overall activation increased for all muscles throughout the test (p < 0.05 and ES > 0.80). There was an increase in low frequency (90 vs. 10%, p = 0.035, ES = 1.06) and a decrease in high frequency (90 vs. 10%, p = 0.009, ES = 1.38, and 90 vs. 40%, p = 0.025, ES = 1.12) components of gluteus maximus. Strong correlations were found between the maximal cadence and vastus lateralis, gluteus maximus and gluteus medius activation at the end of the test. In conclusion, the incremental skating test lead to an increase in activation of lower limb muscles, but only gluteus maximus was sensitive to changes in frequency components, probably caused by a pronounced fatigue.

Changes in electrocorticographic beta frequency components precede spreading depolarization in patients with acute brain injury.

Spreading depolarization (SD) occurs after traumatic brain injury, subarachnoid hemorrhage, malignant hemispheric stroke and intracranial hemorrhage. SD has been associated with secondary brain injury, which can be reduced by ketamine. In this present study frequency bands of electrocorticographic (ECoG) recordings were investigated with regards to SDs. A total of 43 patients after acute brain injury were included in this retrospective and explorative study. Relative delta 0.5-4Hz, theta 4-8Hz, alpha 8-13Hz and beta 13-40Hz bands were analyzed with regards to SD occurrence and analgesic and sedative administration. Higher frequencies, including gamma 40-70Hz, fast gamma 70-100Hz and high frequency oscillations 100-200Hz were analyzed in a subset of patients with a sampling rate of up to 400Hz. A close association of relative beta frequency and SD was found. Relative beta frequency was suppressed up to two hours prior to SD when compared to hours with no SD. This finding was partially explained by administration of ketamine. Even after removal of all patient data during administration of ketamine, SDs occurred predominantly during times with low relative beta frequency in a patient-independent analysis. Suppression of beta frequency by ketamine or without ketamine is associated with low SD counts. Alteration of beta frequency might help to predict occurrence of SDs in acutely brain injured patients.

Cerebrovascular and Autonomic Responses to Thai Traditional Massage in Patients with Ischemic Stroke

Thai traditional massage therapy has shown potential in improving vascular and autonomic functions but the potential benefits of massage for patients with ischemic stroke are yet to be explored. PURPOSE: To determine acute effects of Thai traditional massage on cerebral blood flow velocity and heart rate variability in patients with ischemic stroke. METHODS: Thirteen patients (55±3 years) with a single episode of ischemic stroke due to thrombosis of cerebral arteries (onset ≤2 years) were randomly assigned into either Thai traditional massage (TTM; n=6) or sham control (CON; n=7) group. TTM consisted of petrissage, friction, and passive stretching on the whole body for 60 minutes. Cerebral blood flow velocity (CBFV) in the middle cerebral artery using the transcranial Doppler ultrasound and heart rate variability (HRV) using the frequency domain analysis were measured before and 0, 60, 120 min, and 24 hr after TTM or sham. RESULTS: CBFV increased (p<0.05) 60 min after TTM. This measure returned to baseline values within 120 min. High frequency (HF) component of HRV increased 0, 60, and 120 min after TTM (all p<0.05). There were no significant changes in total power (TP) and low frequency (LF) components as well as the LF:HF ratio of HRV. CBFV was positively associated with TP (r=0.74) and LF (r=0.63) components of HRV (all p<0.05) at 60 min post-TTM. No significant changes in these variables were observed during the sham control condition. CONCLUSIONS: Our study demonstrated that one session of Thai traditional massage acutely improves cerebral blood flow in patients with ischemic stroke possibly through increases in parasympathetic nervous activity. Acknowledgments: This study was supported by Government Research Budget Chulalongkorn University 2015 and The 90th Anniversary Research Fund, Chulalongkorn University, Thailand.

Wear detection by means of wavelet-based acoustic emission analysis

Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring systems can be build, able to evaluate automatically the surface condition of machine components with sliding surfaces.

Influence of attenuation on acoustic emission signals in carbon fiber reinforced polymer panels

Influence of attenuation on acoustic emission (AE) signals in Carbon Fiber Reinforced Polymer (CFRP) crossply and quasi-isotropic panels is examined in this paper. Attenuation coefficients of the fundamental antisymmetric (A0) and symmetric (S0) wave modes were determined experimentally along different directions for the two types of CFRP panels. In the frequency range from 100kHz to 500kHz, the A0 mode undergoes significantly greater changes due to material related attenuation compared to the S0 mode. Moderate to strong changes in the attenuation levels were noted with propagation directions. Such mode and frequency dependent attenuation introduces major changes in the characteristics of AE signals depending on the position of the AE sensor relative to the source. Results from finite element simulations of a microscopic damage event in the composite laminates are used to illustrate attenuation related changes in modal and frequency components of AE signals.