Novel characterization method of impedance cardiography signals using time-frequency distributions

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P¿=¿0.780) and the extended modified beta distribution (P¿=¿0.765) provided similar results, higher than the rest of analyzed kernels.Peer ReviewedPostprint (published version

Aircraft flight parameter estimation based on passive acoustic techniques using the polynomial Wigner-Ville distribution

The acoustic signal from an overflying aircraft, as heard by a stationary observer, is used to estimate an aircraft’s constant height, ground speed, range, and acoustic frequency. Central to the success of this flight parameter estimation scheme is the need for an accurate estimate of the instantaneous frequency of the observed acoustic signal. In this paper, the polynomial Wigner–Ville distribution is used in this application as the instantaneous frequency estimator. Its performance and the issue of the optimal time domain window length are addressed

An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR

A method for components instantaneous frequency (IF) estimation of multicomponent signals in low signal-to-noise ratio (SNR) is proposed. The method combines a new proposed modification of a blind source separation (BSS) algorithm for components separation, with the improved adaptive IF estimation procedure based on the modified sliding pairwise intersection of confidence intervals (ICI) rule. The obtained results are compared to the multicomponent signal ICI-based IF estimation method for various window types and SNRs, showing the estimation accuracy improvement in terms of the mean squared error (MSE) by up to 23%. Furthermore, the highest improvement is achieved for low SNRs values, when many of the existing methods fail.Scopu

Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification

A direct transform for determining the trapped mass on an internal combustion engine based on the in-cylinder pressure resonance phenomenon

It has lately been demonstrated that the resonance of the in-cylinder pressure may be used for inferring the trapped mass in an internal combustion engine. The resonance frequency changes over time as the expansion stroke takes place, and hence time– frequency analysis techniques may be used for determining the instantaneous frequency. However, time–frequency analysis has different problems when obtaining the spectral content of the signal, e.g. Short-Time Fourier Transform dilutes the frequency spectrum, and the Wigner Distribution creates cross terms that difficult its interpretation. In addition, time–frequency analysis requires a significant computational burden. This paper presents a direct transform, based on the resonance phenomenon, which obtains the trapped mass by convolving the pressure trace with the theoretical resonance behaviour. The method permits avoiding the spectral problems of the time–frequency transformations by obtaining the trapped mass directly without the need of inferring the frequency contentBroatch Jacobi, JA.; Guardiola García, C.; Pla Moreno, B.; Bares Moreno, P. (2015). A direct transform for determining the trapped mass on an internal combustion engine based on the in-cylinder pressure resonance phenomenon. Mechanical Systems and Signal Processing. 62-63:480-489. doi:10.1016/j.ymssp.2015.02.023S48048962-6

A time-frequency based method for the detection and tracking of multiple non-linearly modulated components with births and deaths

International audienceThe estimation of the components which contain the characteristics of a signal attracts great attention in many real world applications. In this paper, we address the problem of the tracking of multiple signal components over discrete time series. We propose an algorithm to first detect the components from a given time-frequency distribution and then to track them automatically. In the first place, the peaks corresponding to the signal components are detected using the statistical properties of the spectral estimator. Then, an original classifier is proposed to automatically track the detected peaks in order to build components over time. This classifier is based on a total divergence matrix computed from a peak-component divergence matrix that takes account of both amplitude and frequency information. The peak-component pairs are matched automatically from this divergence matrix. We propose a stochastic discrimination rule to decide upon the acceptance of the peak-component pairs. In this way, the algorithm can estimate the number, the amplitude and frequency modulation functions, and the births and the deaths of the components without any limitation on the number of components. The performance of the proposed method, a post-processing of a time-frequency distribution is validated on simulated signals under different parameter sets. The method is also applied to 4 real-world signals as a proof of its applicability. Index Terms—Time-frequency domain, multicomponent, peak detection, component tracking, amplitude and frequency modulation , nonlinear, nonstationary, births and death

Time-frequency investigation of heart rate variability and cardiovascular system modeling of normal and chronic obstructive pulmonary disease (COPD) subjects

A study has been designed to add insight to some questions that have not been fully investigated in the heart rate variability field and the cardiovascular regulation system in normal and Chronic Obstructive Pulmonary Disease (COPD) subjects. It explores the correlations between heart rate variability and cardiovascular regulation, which interact through complex multiple feedback and control loops. This work examines the coupling between heart rate (HR), respiration (RESP), and blood pressure (BP) via closed-loop system identification techniques in order to noninvasively assess the underlying physiology. In the first part of the study, the applications of five different bilinear time-frequency representations are evaluated on modeled HRV test signals, actual electrocardiograms (ECG), BP and RESP signals. Each distribution: the short time Fourier transform (STFT), the smoothed pseudo Wigner-Ville (SPWVD), the ChoiWilliams (CWD), the Bom-Jordan-Cohen (BJC) and wavelet distribution (WL), has unique characteristics which is shown to affect the amount of smoothing and the generation of cross-terms. The CWD and the WL are chosen for further application because of overcoming the drawbacks of other distributions by providing higher resolution in time and frequency while suppressing interferences between the signal components. In the second part of the study, the Morlet, Meyer, Daubechies 4, Mexican Hat and Haar wavelets are used to investigate the heart rate and blood pressure variability from both COPD and normal subjects. The results of wavelet analysis give much more useful information than the Cohen\u27s class representations. Here we are able to quantitatively assess the parasympathetic (HF) and sympatho-vagal balance (LF:HF) changes as a function of time. As a result, COPD subjects breathe faster, have higher blood pressure variability and lower HRV. In the third part of the study, a special class of the exogenous autoregressive (ARX) model is developed as an analytical tool for uncovering the hidden autonomic control processes. Non-parametric relationships between the input and outputs of the ARX model resulting in transfer function estimations of the noise filters and the input filter were used as mechanistic cardiovascular models that have shown to have predictive capabilities for the underlying autonomic nervous system activity of COPD patients. Transfer functions of COPD cardiovascular models have similar DC gains but show a larger lag in phase as compared to the models of normal subjects. Finally, a method of severity classification is presented. This method combines the techniques of principal component analysis (PCA) and cluster analysis (CA) and has been shown to separate the COPD from the normal population with 100% accuracy. It can also classify the COPD population into at risk , mild , moderate and severe stages with 100%, 90%, 88% and 100% accuracy respectively. As a result, cluster and principal component analysis can be used to separate COPD and normal subjects and can be used successfully in COPD severity classification