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Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals

Berraih Sid Ahmed, Baakek Yettou Nour El Houda, Debbal Sidi Mohammed El Amine

Polish Journal of Medical Physics and Engineering

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2021

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Vol. 27, Iss. 1

73--85

EN

Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition. The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities. This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.

2

Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals

Hosny Mohamed, Zhu Minwei, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

704--716

EN

Successful deep brain stimulation surgery for Parkinson’s disease (PD) patients hinges on accurate clustering of the functional regions along the electrode insertion trajectory. Microelectrode recording (MER) is employed as a substantial tool for neuro-navigation and localizing the optimal target, such as the subthalamic nucleus (STN), intraoperatively. MER signals deliver a framework to reveal the underlying characteristics of STN. The motivation behind this work is to explore the application of Higher-order statistics and spectra (HOS) for an automated delineation of the neurophysiological borders of STN using MER signals. Database collected from 21 PD patients were used. Two HOS methods (Bispectrum and cumulant) were exploited to probe non-Gaussian properties of STN region. This is followed by utilizing classifiers, namely K-nearest neighbor, decision tree, Boosting and support vector machine (SVM), to choose the superior classifier. Comparison of the performance achieved via HOS alongside the state-of-the-art techniques shows that the proposed features are better suited for identifying STN borders and achieve higher results. Average classification accuracy, sensitivity, specificity, area under the curve and Youden’s J statistics of 94.81%, 96.73%, 92.15%, 0.9444% and 0.8888, respectively, were yielded using SVM with 8 bispectrum and 241 cumulants features. The proposed model can aid the neurosurgeon in STN detection.

3

A Robust Audio Watermarking Scheme using Higher-order Statistics in Empirical Mode Decomposition Domain

Wang W., Niu P., Yang H., Zhang Y., Ma T.

Fundamenta Informaticae

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2014

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Vol. 130, nr 4

467--490

EN

The development of a desynchronization invariant audio watermarking scheme without degrading acoustical quality is a challenging work. This paper proposes a robust audio watermarking scheme in Empirical Mode Decomposition (EMD) domain, in which the higher-order statistics and synchronization code are utilized. Firstly, the wavelet de-noising is performed on the original host audio, the de-noised digital audio is segmented, and then each segment is cut into two parts. Secondly, with the spatial watermarking technique, synchronization code is embedded into the statistics average value of audio samples in the first part. Thirdly, for the second part, EMD is performed, and a series of Intrinsic Mode Functions (IMFs) and a residual are given, and then the higher-order statistics of residual are obtained by using the Hausdorff distance. Finally, the digital watermark is embedded into the residual in EMD domain by using the higher-order statistics. Simulation results show that the proposed watermarking scheme is not only inaudible and robust against common signal processing operations such as MP3 compression, noise addition,resampling, and re-quantization etc, but also robust against the desynchronization attacks such as random cropping, amplitude variation, pitch shifting, and jittering etc.

4

Amplitude-Frequency Monitoring of Power Quality Transients using Higher-Order Statistics and Self-Organizing Neural Networks

Gonzalez de la Rosa J. J., Aguera-Perez A., Palomares-Salas J. C., Moreno-Munoz A.

Przegląd Elektrotechniczny

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2011

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R. 87, nr 8

128-137

EN

In this paper a smart automatic classification of PQ transients is performed attending to their amplitudes and frequencies, and the extreme of higher-order cumulants. Feature extraction stage is double folded. First, these statistical measurements reveal the hidden geometry for a constant amplitude or frequency, conforming the 2D clustering grace to the third and fourth-order features associated to each signal anomaly, coupled to the 50-Hz power line. Precisely the main contribution of the work is the novel finding that the maxima and the minima of the higher-order cumulants distribute according to curves families, each of which associated to the transient's frequency or amplitude. Given a statistical order, each datum in a curve corresponds to the initial amplitude (or constant frequency), and to a couple of extremes (min-max) associated to the statistical estimator. The random grouping along each curve reveals the a priori hidden geometry, linked to the subjacent electrical phenomenon. Secondly, the regular surface grid in the input space (amplitude-frequency) experiments a transformation to the output space which is developed by the higher-order statistics. Once the geometry in the feature space has been found, we show the computational intelligence modulus, based in Self-Organizing Maps, which performs satisfactory learning along each frequency and amplitude curve. Performance of a four-neuron network with different geometries is shown, confirming the curves' patterns.

PL

W artykule opisano automatyczną metodę klasyfikacji jakości energii w stanach przejściowych z uwzględnieniem amplitudy, częstotliwości i wartości ekstremalnych. W pierwszym etapie przeprowadzane są pomiary statystyczne dla stałej amplitudy i częstotliwości uwzględniające klastry 2D i właściwości trzeciego i czwartego rzędu towarzyszące anomaliom. Następnie uwzględniana jest geometria sieci. Po tym etapie włączany jest moduł sztucznej inteligencji bazujący na sieciach neuronowych.

5

Electrical Transients Monitoring via Higher-Order Cumulants and Competitive Layers

González de la Rosa J., Moreno Munoz A.

Przegląd Elektrotechniczny

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2009

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R. 85, nr 10

284-289

EN

This work deals with Power Quality (PQ) transients detection and characterization using higher-order sliding cumulants, whose maxima and minima are the coordinates of two-dimensional feature vectors. We recall the former research results which discriminate between two types of transients (impulsive and oscillatory) using third and fourth-order cumulants. Then, we use fourth-order cumulants to differentiate transients from ”healthy” signals and other anomalous features in the power-line sine. The classification strategy is based in competitive layers. The results show that the measured vectors are classified into clearly differentiated clusters in the feature space. The experience sets the foundations of an automatic procedure for PQ event detection.

PL

W artykule poruszono zagadnienia jakości energii, zwłaszcza wykrywania stanów przejściowych i ich charakterystykę z wykorzystaniem kumulant wyższych rzędów. Przywołano wyniki poprzednich badań rozróżniających dwa typy stanów przejściowych (impulsywne i oscylacyjne), do których użyto kumulant trzeciego- i czwartego rzędu. Kumulanty czwartego rzędu wykorzystywano do różniczkowania stanów przejściowych występujących w sinusoidzie linii. Mierzone wektory są klasyfikowane w wyraźnie różniące się grupy w przestrzeni właściwości.

6

On-line model identification for active noise control using integrated bispectrum analysis

Główka T.

Archives of Control Sciences

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2007

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Vol. 17, no. 2

191-207

EN

In the paper a problem of the on-line model identification of secondary and feedback paths in the feedforward ANC system is considered. The system is closed-loop, with low signal-to-noise ratio and with the disturbance affecting the input and output of the identified paths. To overcome the mentioned difficulties a new approach to the identification based on the higher-order spectra is presented. The integrated bispectrum-based identification method is proposed and the results of its applying are provided and compared with the results derived from the classical methods. The estimates are computed on the basis of data acquired in the laboratory (real-world) experiment as well as in the computer simulations.