Cross Time-frequency Analysis Research Articles

Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time–frequency analysis

Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal. The bivariate CP signal is formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal. The approach consists of three stages. First, the bivariate CP signal is decomposed into intrinsic mode functions (IMFs) and residuals for both HR and RR channels using bivariate fast and adaptive empirical mode decomposition (FAEMD). Second, the features are extracted using time-domain analysis, spectral analysis, and time–frequency domain analysis of IMFs from CP signal. The time–frequency domain features are computed from the cross time–frequency matrices of IMFs of CP signal. The cross time–frequency matrix of each IMF is evaluated using the Stockwell (S)-transform. Third, the support vector machine (SVM) and the random forest (RF) classifiers are used for automated detection of sleep apnea with the features from the bivariate CP signal. Our proposed approach has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively for sleep apnea detection using the 10-fold cross-validation method. The approach has yielded an average sensitivity and specificity of 73.19% and 73.13%, respectively for the subject-specific cross-validation. The performance of the approach was compared with other CPC features used for the detection of sleep apnea.

Contribution of cross time-frequency analysis in assessment of possible relationships between large-scale climatic fluctuations and rainfall of northern central Algeria

This paper is proposed for the investigation of possible relationships between the large-scale atmospheric circulation phenomena such as the North Atlantic Oscillation (NAO), Southern Oscillation (SOI), Mediterranean Oscillation (MO), Western Mediterranean Oscillation (WeMO) and rainfall of Sebaou river watershed (Northern central Algeria), covering a period of 39 years at monthly scale. Several time and scale-based methods were used: correlation and spectral analysis (CSA), continuous wavelet transform (CWT), multiresolution wavelet analysis (MRWA), cross wavelet analysis (XWT), wavelet coherence transform (WCT) and cross multiresolution wavelet analysis (CMRWA). The rainfall analysis by CSA and CWT has been clearly demonstrating the dominance of 1 year and 1–3-year modes, which they explain 30 to 51% and 25 to 28% of the variance respectively. However, the indices have shown that inter-annual fluctuations up to long-term explain between 60 and 90%. CWT and MRWA indicated significant fluctuations materialising a dry period more marked between the 1980s and 1990s with strong trend towards drier conditions starting from the 1980s, explained by the decadal components D7 and the approximation A7. In addition to the annual component, the XWT spectrums reveal strong coefficients for the SOI between 1992–2005 and 1986–2000 for the modes of 5–10 years and higher than 10 years respectively and less intense for NAO. The WCT between NAO and rainfall indicated the most significant relationship for 1 year, 1–3 years and 3–5 years approximately from the early 1980s corresponding to the dry period. However, the SOI affects rainfall only locally and with significant values more or less localised in the time-frequency space between MO, WeMO and rainfall, but this influence could be significant for low-frequency events. CWMRA shows that the components of 5–10 years and higher than 10 years are the most effective to represent climate index-rainfall significant relationships, where change in Daubechies wavelet properties can improve the correlation across the scales. Furthermore, has indicated that the short-term processes dominate the relationship index-rainfall, which masks the long-term phenomena whose influence can sometimes be very distant. As such, the rainfall variability of the study area has shown fairly significant links, at least locally with large-scale atmospheric circulation phenomena.

Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration.

Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time–frequency analysis

In this study, a framework for the characterization of the dynamic interactions between RR variability (RRV) and systolic arterial pressure variability (SAPV) is proposed. The methodology accounts for the intrinsic non-stationarity of the cardiovascular system and includes the assessment of both the strength and the prevalent direction of local coupling. The smoothed pseudo-Wigner–Ville distribution (SPWVD) is used to estimate the time–frequency (TF) power, coherence, and phase-difference spectra with fine TF resolution. The interactions between the signals are quantified by time-varying indices, including the local coupling, phase differences, time delay, and baroreflex sensitivity (BRS). Every index is extracted from a specific TF region, localized by combining information from the different spectra. In 14 healthy subjects, a head-up tilt provoked an abrupt decrease in the cardiovascular coupling; a rapid change in the phase difference (from 0.37 ± 0.23 to −0.27 ± 0.22 rad) and time delay (from 0.26 ± 0.14 to −0.16 ± 0.16 s) in the high-frequency band; and a decrease in the BRS (from 23.72 ± 7.66 to 6.92 ± 2.51 ms mmHg−1). In the low-frequency range, during a head-up tilt, restoration of the baseline level of cardiovascular coupling took about 2 min and SAPV preceded RRV by about 0.85 s during the whole test. The analysis of the Eurobavar data set, which includes subjects with intact as well as impaired baroreflex, showed that the presented methodology represents an improved TF generalization of traditional time-invariant methodologies and can reveal dysfunctions in subjects with baroreflex impairment. Additionally, the results also suggest the use of non-stationary signal-processing techniques to analyze signals recorded under conditions that are usually supposed to be stationary.

Corrosion detection with piezoelectric wafer active sensors using pitch-catch waves and cross-time–frequency analysis

A time–frequency analysis-based signal processing study for detecting active corrosion in aluminum plate-like structure utilizing the broadband piezoelectric wafer active sensors is presented in this article. Tests were conducted on an aluminum plate with a network of sensors installed on one side of the plate for Lamb wave generation and reception. The corrosion was emulated as material loss of an area of 50 × 38 mm2 on the opposite side of the plate. The corroded area resulted in a thickness loss on the plate and a change in wave propagation as well. The experimental data were first evaluated by a statistical damage index (DI) based on root mean square values and then the Cohen’s class motivated cross-time–frequency analysis. The cross-time–frequency analysis was found more reliable and precise for detecting the corrosion progression when compared to the DI method. Not only can the proposed metric correctly evaluate the phase difference of specific frequency and time, it also carries useful information of phase difference, which is strongly correlated to the physics of corrosion detection using Lamb waves. Novel aspects of this study include a sensing approach that can sense corrosion damage on both external and internal surfaces of a given structure, the employment of effective tuning in corrosion detection, and using cross-time–frequency analysis to quantitatively evaluate thickness loss. Though the corrosion studied herein is an idealized and simplified situation, the subject work on phase difference and cross-time–frequency analysis is useful first-step effort and opens a new way to perform Lamb wave-based corrosion detection. The results presented in this article combine easy-to-examine corrosion assumptions together with low-frequency antisymmetric Lamb wave analysis to provide a stepping stone for more complicated analysis needed for further real life corrosion assessment.

Cross Time-Frequency Analysis of Gastrocnemius Electromyographic Signals in Hypertensive and Nonhypertensive Subjects

The effects of hypertension are chronic and continuous; it affects gait, balance, and fall risk. Therefore, it is desirable to assess gait health across hypertensive and nonhypertensive subjects in order to prevent or reduce the risk of falls. Analysis of electromyography (EMG) signals can identify age related changes of neuromuscular activation due to various neuropathies and myopathies, but it is difficult to translate these medical changes to clinical diagnosis. To examine and compare geriatrics patients with these gait-altering diseases, we acquire EMG muscle activation signals, and by use of a timesynchronized mat capable of recording pressure information, we localize the EMG data to the gait cycle, ensuring identical comparison across subjects. Using time-frequency analysis on the EMG signal, in conjunction with several parameters obtained from the time-frequency analyses, we can determine the statistical discrepancy between diseases. We base these parameters on physiological manifestations caused by hypertension, as well as other comorbities that affect the geriatrics community. Using these metrics in a small population, we identify a statistical discrepancy between a control group and subjects with hypertension, neuropathy, diabetes, osteoporosis, arthritis, and several other common diseases which severely affect the geriatrics community.

Signal Processing-Based Direction Finder for Transient Capacitor Switching Disturbances

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <?Pub Dtl=""?>This paper develops a signal processing, time-frequency analysis-based analytic solution to locate transient capacitor switching disturbances. The flow of transient disturbance energy caused by the capacitor switching is determined by the time and frequency localized phase difference. Cross time-frequency analysis provides time- and frequency-localized phase difference between the transient voltage and current disturbance waveforms which determine the direction of transient disturbance energy flow. The time and frequency localization properties of the proposed scheme allows one to expand the application to complicated power distribution systems without knowledge of system parameters, capacitor size, and configuration. The proposed scheme has been verified by the Electromagnetic Transients Program simulation for all possible spatial locations of the capacitor switching. </para>

Application of Cross Time-Frequency Analysis to Postural Sway Behavior: The Effects of Aging and Visual Systems

In this paper, the effects of visual feedback and aging on postural sway systems and signals are investigated by analyzing the transient phase difference between "input" and "output" which correspond to center of pressure (COP) and center of mass (COM), respectively. In order to analyze the transient phase difference characteristics of COP and COM, a relatively new cross time-frequency analysis technique that provides time- and frequency-localized phase difference information is utilized. The feedback control process in the postural sway is interpreted in terms of a feedback compensator which is characterized in terms of a phase difference. Using the experimental results of the transient phase difference obtained from the cross time-frequency distribution, it is demonstrated that the postural control of young persons are more stable and rely more on visual sensory feedback to stabilize postural control compared to that of the elderly persons.