Идентификация цифровых последовательностей с интегральным биспектральным преобразованием

The paper presents algorithms for identifying signals and determining the threshold of false identification based on the integral bispectrum conversion and the Euclidean distance calculation. The analytical calculation of statistical characteristics in the form of the average probability of identification error, identification error of a known signal and a new signal is carried out. The advantages of bispectral signal conversion over spectral power density in identifying signals with their strong mutual correlation (from 0.5 to 0.9) are shown. Mathematical and computer modeling of the signal identification procedure and the optimal threshold for determining a new signal is performed. The simulation results confirmed the coincidence with the theoretical values of the probability of signal identification error. Manokhin A. E. Identification of Digital Signals with Integrated Bispectral Conversion. Ural Radio Engineering Journal. 2023;7(1):56–71. (In Russ.) DOI 10.15826/urej.2023.7.1.004.В работе представлены алгоритмы идентификации сигналов и определения порога ложной идентификации на основе формирования интегрального биспектра и вычисления евклидового расстояния. Проведен аналитический расчет статистических характеристик в виде средней вероятности ошибки идентификации, ошибки идентификации известного сигнала и нового сигнала. Показаны преимущества биспектрального преобразования сигнала перед спектральной плотностью мощности в идентификации сигналов при их сильной взаимной корреляции (от 0,5 до 0,9). Выполнено математическое и компьютерное моделирование процедуры идентификации сигналов и формирования оптимального порога, позволяющего определить новый сигнал. Результаты моделирования подтвердили совпадение с теоретическими значениями вероятности ошибки идентификации сигнала. Манохин А. Е. Идентификация цифровых последовательностей с интегральным биспектральным преобразованием. Ural Radio Engineering Journal. 2023;7(1):56–71. DOI 10.15826/urej.2023.7.1.004

Identification of Digital Signals with Integrated Bispectral Conversion

Поступила: 31.10.2022. Принята в печать: 07.03.2023.Received: 31.10.2022. Accepted: 07.03.2023.В работе представлены алгоритмы идентификации сигналов и определения порога ложной идентификации на основе формирования интегрального биспектра и вычисления евклидового расстояния. Проведен аналитический расчет статистических характеристик в виде средней вероятности ошибки идентификации, ошибки идентификации известного сигнала и нового сигнала. Показаны преимущества биспектрального преобразования сигнала перед спектральной плотностью мощности в идентификации сигналов при их сильной взаимной корреляции (от 0,5 до 0,9). Выполнено математическое и компьютерное моделирование процедуры идентификации сигналов и формирования оптимального порога, позволяющего определить новый сигнал. Результаты моделирования подтвердили совпадение с теоретическими значениями вероятности ошибки идентификации сигнала.The paper presents algorithms for identifying signals and determining the threshold of false identification based on the integral bispectrum conversion and the Euclidean distance calculation. The analytical calculation of statistical characteristics in the form of the average probability of identification error, identification error of a known signal and a new signal is carried out. The advantages of bispectral signal conversion over spectral power density in identifying signals with their strong mutual correlation (from 0.5 to 0.9) are shown. Mathematical and computer modeling of the signal identification procedure and the optimal threshold for determining a new signal is performed. The simulation results confirmed the coincidence with the theoretical values of the probability of signal identification error

A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate

Driver stress is a major cause of car accidents and death worldwide. Furthermore, persistent stress is a health problem, contributing to hypertension and other diseases of the cardiovascular system. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions. In this paper, galvanic skin response is used to estimate the ground truth stress levels. A feature selection technique based on the minimal redundancy-maximal relevance method is then applied to multiple heart rate variability and breathing rate metrics to identify a novel and optimal combination for use in detecting stress. The support vector machine algorithm with a radial basis function kernel was used along with these features to reliably predict stress. The proposed method has achieved a high level of accuracy on the target dataset.Comment: In Proceedings of the 15th International Conference on Machine Vision (ICMV), Rome, Italy, 18-20 November 2022. arXiv admin note: text overlap with arXiv:2206.0322

National Conference on Electrical and Computer Engineering

The objective of the present study was to investigate the possible relationship between bispectral parameters extracted from surface EMG (sEMG) signals and muscle force and fatigue. Our hypothesis was that changes in motor unit recruitment during muscle contraction and fatigue, affect sEMG distribution and the degree of complexity and irregularity in the muscle. Thus, four features based on higher order spectra and cumulants were extracted from sEMG signal, recorded from biceps brachii muscle of a healthy female volunteer during rest, sustained (fatiguing) 50% MVC, 100% MVC and recovery. Results obtained from weighted center of bispectrum (WCOB) analysis showed that the values of f1m and f2m were higher during rest and recovery states, while they decreased during MVCs. However, when fatigue occurred, these parameters increased slightly, again. Moreover, entropy features, namely NBE and NBSE decreased with contraction compared to rest and recovery states, indicating less complexity of time series during MVCs. However, the changes were not significant during fatigue and during changes in MVC levels from 50% to 100%. On the other hand, test of non-Gaussianity based on negentropy showed the reverse pattern of WCOB, NBE and NBSE. In addition, contour maps of bispectrum enabled us to visually differentiate each trial. </p

Spatio-Spectral Coherence Analysis of ERP signals for Attentional Visual Stimulus

In this paper, the brain function in relation with human’s visual attention was investigated by means of coherence and bicoherence methods. Throughout experimentation with attentional visual stimulus ERP (Event Related Potential) data and synthesized simulated data with different combinations of parameters, it is demonstrated that bicoherence and coherence can be useful to reveal the phase synchronies between different frequency bands at fixed scalp location, and between different scalp locations at fixed frequency band, respectively. Both methods are also affected by time interval from the onset, and the level of white noise added. The phase coupled relationships among θ, δ, and α bands, and between frontal and central lobes were observed for attentional tasks, while those were little observable for inattentional tasks, which can show brain’s functional spatio-spectral differences associated with human’s attention.ope

The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems

Includes bibliographical references (leaves 128-129).The thesis examines the use of two time-frequency domain signal processing tools in its application to condition monitoring of electrical machine drive systems. The mathematical and signal processing tools which are explored are wavelet analysis and a non-stationary adaptive signal processing algorithm. Four specific applications are identified for the research. These applications were specifically chosen to encapsulate important issues in condition monitoring of variable speed drive systems. The main aim of the project is to highlight the need for fault detection during machine transients and to illustrate the effectiveness of incorporating and adapting these new class of algorithms to detect faults in electrical machine drive systems during non-stationary conditions

Entwicklung, Testung und Anwendung von Verfahren zur nichtlinearen Analyse von kortikalen Aktivierungs- und Deaktivierungsmustern im EEG

The aim of this work was the development of two different time-variant nonlinear methods (1) to quantify the nonlinear stability and (2) to detect and quantify quadratic phase couplings (QPC). Starting point of the methodical investigation of the nonlinear stability was the time-invariant estimation of the largest Lyapunov exponent, which was adapted on a pointwise investigation. An optimization of critical parameter of this estimation was performed using simulations and real data. Nonlinear portions of the investigated data were detected by a test of nonlinearity using a surrogate data approach. Starting point of the methodical investigation of QPC was the time-invariant, parametric estimation of the bispectrum. The time-variant approach was carried out by an estimation of AR parameters using the ”recursive instrumental variable” algorithm. The optimization of critical parameters of the estimation occured by data-driven simulations. A two-dimensional approach of optimization was used. The aim of the practical investigation of the EEG (and ECoG, respectively) was the combined application of both methods. Sleep pattern and the effect of vibroacustic stimulations (VAS) in the fetal ECoG, burst-interburst pattern (BIP) in the neonatal EEG and burst-suppression pattern (BSP) in the EEG of sedated patients were examined. The analysis of the fetal ECoG showed the occurrence of cyclic ECoG activity already at a very young gestational age and contributed essentially to the understanding of the time course of the development of characteristic sleep states. VAS during NREM sleep caused an arousal reaction in the fetal ECoG. The time-course of this arousal could be quantified by the nonlinear stability. Investigating the neonatal EEG during BIP, the existence of a 10-seconds rhythmicity in the time-course of QPC could be proven. High QPCs in the EEG of the sedated patients during BSP could be detected and quantified in its time course. The combined application of both nonlinear methods was able to contribute fundamentally to the amount of findings and could be utilized complementary in the interpretation of the results. keywords: time-variant nonlinear stability, time-variant parametric bispectrum , fetal ECoG, Burst-Interburst-Pattern, Burst-Suppression-PatternDas Ziel dieser Arbeit war die Entwicklung zweier zeitvarianter nichtlinearer Verfahren (1) zur Quantifizierung der nichtlinearen Stabilität und (2) zur Detektion und Quantifizierung von quadratischen Phasenkopplungen (QPC). Ausgangspunkt der methodischen Entwicklung zur nichtlinearen Stabilität war die zeitinvariante Schätzung des größten Lyapunovexponenten. Diese wurde auf eine punktweise Betrachtung adaptiert und eine Optimierung der für die Schätzung wesentlichen Parameter mittels Simulationen und realer Daten vorgenommen. Nichtlineare Signalanteile in den untersuchten Daten wurden mittels eines Nichtlinearitätstestes unter Nutzung surrogater Daten quantifiziert. Ausgangspunkt der methodischen Entwicklung zur Quanitifzierung von QPC war die zeitinvariante, parametrische Schätzung des Bispektrums. Der Übergang zu einer zeitvarianten Schätzung erfolgte über die Bestimmung von AR-Parametern mit dem ”recursive instrumental variable” Algorithmus. Die Optimierung der für diese Schätzung relevanten Parameter erfolgte über einen zweidimensionalen Ansatz mittels datengetriebener Simulationen. Das Ziel der Untersuchungen des EEGs (bzw. ECoGs) war die gemeinsame Anwendung beider Verfahren. Untersucht wurden Schlafmuster sowie die Auswirkungen vibroakustischer Stimulationen (VAS) im fetalen ECoG, Burst-Interburst-Muster (BIM) im neonatalen EEG und Burst-Suppression-Muster (BSM) im EEG von sedierten Patienten. Die Analyse des fetalen ECoGs konnte das Auftreten zyklischer ECoG-Aktivität schon ab einem sehr frühen Gestationsalter zeigen und wesentlich zum Grundverständnis der zeitlichen Abfolge der Entwicklung von Schlafstadien beitragen. Im ECoG während VAS konnte eine Arousalreaktion während des NREM-Schlafes festgestellt und durch den Zeitverlauf des Parameters für die Kennzeichnung der nichtlinearen Stabilität quantifiziert werden. Durch die Analyse des neonatalen EEGs während BIM konnte ein 10-Sekunden-Rhythmus des QPC-Verlaufs nachwiesen werden. Im EEG der sedierten Patienten während BSM konnten hohe QPC-Werte detektiert und zeitlich quantifiziert werden. Die gemeinsame Anwendung beider nichtlinearer Verfahren konnte dabei entscheidend zum Erkenntnisgewinn beitragen und ergänzend bei der Interpretation der Ergebnisse eingesetzt werden

Application of Higher-Order Statistics and Subspace-Based Techniques to the Analysis and Diagnosis of Electrocardiogram Signals

The first and main contribution of this research work is the higher-order statistics (HOS)-based non-linear analysis and subsequent diagnosis of abnormal electrocardiogram (ECG) signals, particularly myocardial ischaemia. In the time domain; the second-, third-, and the fourth-order cumulants have been used in the analysis. In the frequency domain; up to the tenth-order polyspectra have been exploited. This HOS-based analysis of normal and ischaemic electrocardiogram signals has led to the identification of certain key discriminant features for the two physiological states of the heart. These features are then fed to different backpropagation-based multiple layer perceptrons for classification. The second contribution is a proposed new methodology to discriminate patients with angina pectoris or with old myocardial infarction (MI) during the first 60 seconds of stress test (or in some cases using rest ECG). It is based on the pseudo-spectral Multiple Signal Classification (MUSIC) and has the potential of being highly sensitive diagnostic signal processing tool. The third contribution is the development of a novel higher-order statistics, high-resolution estimator for quadratically coupled frequencies based on subspace spectral estimation. Extensive studies of cumulants, bispectra and bicoherence-squared of normal and ischaemic ECG signals collected from MIT and ST-T European databases has enabled us to see key discriminant features in both the third- and fourth-order cumulant domains. In the frequency domain, the polyspectral study has been extended to the lOth-order poly spectra. By calculating one-dimensional polyspectrum slices using an algorithm developed by Zhou and Giannakis (1995) a considerable reduction in the CPU time has been achieved. Furthermore, Zhou’s algorithm has been further extended to estimate the polycoherency slices which are used to characterise non-linearities in normal and ischaemic ECG signals. An important finding in this thesis is the decrease of the order of non-linearity representing the electrocardiogram signals of ischaemic patients. This thesis also includes the results of a pilot study involving eighteen healthy subjects (MIT database) and confirmed that the ECG signal is non-Gaussian, cyclostationary and quasi periodic. Combined spectral and bispectral analysis of the signal revealed that there are unique harmonic characteristics for the P-wave, QRS complex and T-wave and other frequencies due to harmonic interactions. In this work three linear and one non-linear adaptive filtering/predictions techniques have been applied to noisy ECG signals and their respective performances appraised. It is shown that the Kalman filter gives the best mean-square error MSE error but its comparatively long execution time and problems arising from ill-conditioning of the state-error covariance matrix render it of limited use in ECG applications. It is also shown that the LMS-based quadratic and cubic Volterra filters are the most superior for the ECG signal prediction. For ECG classifications; three multi-layer perceptrons employing back-propagation and modified back-propagation algorithms, and using two sets from the higher-order most discriminant features as their inputs, have yielded fairly high classification rates