Sequential blind source extraction for quasi-periodic signals with time-varying period

A novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of a heart sound signal from real-world lung sound recordings. Separation results confirm the utility of the introduced approach, and listening tests are employed to further corroborate the results

Time-Varying Modeling of Glottal Source and Vocal Tract and Sequential Bayesian Estimation of Model Parameters for Speech Synthesis

abstract: Speech is generated by articulators acting on a phonatory source. Identification of this phonatory source and articulatory geometry are individually challenging and ill-posed problems, called speech separation and articulatory inversion, respectively. There exists a trade-off between decomposition and recovered articulatory geometry due to multiple possible mappings between an articulatory configuration and the speech produced. However, if measurements are obtained only from a microphone sensor, they lack any invasive insight and add additional challenge to an already difficult problem. A joint non-invasive estimation strategy that couples articulatory and phonatory knowledge would lead to better articulatory speech synthesis. In this thesis, a joint estimation strategy for speech separation and articulatory geometry recovery is studied. Unlike previous periodic/aperiodic decomposition methods that use stationary speech models within a frame, the proposed model presents a non-stationary speech decomposition method. A parametric glottal source model and an articulatory vocal tract response are represented in a dynamic state space formulation. The unknown parameters of the speech generation components are estimated using sequential Monte Carlo methods under some specific assumptions. The proposed approach is compared with other glottal inverse filtering methods, including iterative adaptive inverse filtering, state-space inverse filtering, and the quasi-closed phase method.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Extraction et débruitage de signaux ECG du foetus.

Les malformations cardiaques congénitales sont la première cause de décès liés à une anomalie congénitale. L electrocardiogramme du fœtus (ECGf), qui est censé contenir beaucoup plus d informations par rapport aux méthodes échographiques conventionnelles, peut être mesuré e par des électrodes sur l abdomen de la mère. Cependant, il est tres faible et mélangé avec plusieurs sources de bruit et interférence y compris l ECG de la mère (ECGm) dont le niveau est très fort. Dans les études précédentes, plusieurs méthodes ont été proposées pour l extraction de l ECGf à partir des signaux enregistrés par des électrodes placées à la surface du corps de la mère. Cependant, ces méthodes nécessitent un nombre de capteurs important, et s avèrent inefficaces avec un ou deux capteurs. Dans cette étude trois approches innovantes reposant sur une paramétrisation algébrique, statistique ou par variables d état sont proposées. Ces trois méthodes mettent en œuvre des modélisations différentes de la quasi-périodicité du signal cardiaque. Dans la première approche, le signal cardiaque et sa variabilité sont modélisés par un filtre de Kalman. Dans la seconde approche, le signal est découpé en fenêtres selon les battements, et l empilage constitue un tenseur dont on cherchera la décomposition. Dans la troisième approche, le signal n est pas modélisé directement, mais il est considéré comme un processus Gaussien, caractérisé par ses statistiques à l ordre deux. Dans les différentes modèles, contrairement aux études précédentes, l ECGm et le (ou les) ECGf sont modélisés explicitement. Les performances des méthodes proposées, qui utilisent un nombre minimum de capteurs, sont évaluées sur des données synthétiques et des enregistrements réels, y compris les signaux cardiaques des fœtus jumeaux.Congenital heart defects are the leading cause of birth defect-related deaths. The fetal electrocardiogram (fECG), which is believed to contain much more information as compared with conventional sonographic methods, can be measured by placing electrodes on the mother s abdomen. However, it has very low power and is mixed with several sources of noise and interference, including the strong maternal ECG (mECG). In previous studies, several methods have been proposed for the extraction of fECG signals recorded from the maternal body surface. However, these methods require a large number of sensors, and are ineffective with only one or two sensors. In this study, state modeling, statistical and deterministic approaches are proposed for capturing weak traces of fetal cardiac signals. These three methods implement different models of the quasi-periodicity of the cardiac signal. In the first approach, the heart rate and its variability are modeled by a Kalman filter. In the second approach, the signal is divided into windows according to the beats. Stacking the windows constructs a tensor that is then decomposed. In a third approach, the signal is not directly modeled, but it is considered as a Gaussian process characterized by its second order statistics. In all the different proposed methods, unlike previous studies, mECG and fECG(s) are explicitly modeled. The performances of the proposed methods, which utilize a minimal number of electrodes, are assessed on synthetic data and actual recordings including twin fetal cardiac signals.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Signal processing techniques for extracting signals with periodic structure : applications to biomedical signals

In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the signal is proposed. The proposed method is utilized in localizing heart sounds in respiratory signals, which is an essential pre-processing step in most of the heart sound cancellation methods. Artificially mixed and real respiratory signals are used for evaluating the method. It is shown that the performance of the proposed method is superior to those of the other methods in terms of false detection. More over, the execution time is significantly lower than that of the method ranked second in performance. For multichannel data, the problem is tackled using two approaches. First, it is assumed that the sources are periodic and the statistical characteristics of periodic sources are exploited in developing a method to effectively choose the appropriate delays in which the diagonalization takes place. In the second approach it is assumed that the sources of interest are cyclostationary. Necessary and sufficient conditions for extractability of the sources are mathematically proved and the extraction algorithms are proposed. Ballistocardiogram (BCG) artifact is considered as the sum of a number of independent cyclostationary components having the same cycle frequency. The proposed method, called cyclostationary source extraction (CSE), is able to extract these components without much destructive effect on the background electroencephalogram (EEG

Exploiting periodicity to extract the atrial activity in atrial arrhythmias

[EN] Atrial fibrillation disorders are one of the main arrhythmias of the elderly. The atrial and ventricular activities are decoupled during an atrial fibrillation episode, and very rapid and irregular waves replace the usual atrial P-wave in a normal sinus rhythm electrocardiogram (ECG). The estimation of these wavelets is a must for clinical analysis. We propose a new approach to this problem focused on the quasiperiodicity of these wavelets. Atrial activity is characterized by a main atrial rhythm in the interval 3-12 Hz. It enables us to establish the problem as the separation of the original sources from the instantaneous linear combination of them recorded in the ECG or the extraction of only the atrial component exploiting the quasiperiodic feature of the atrial signal. This methodology implies the previous estimation of such main atrial period. We present two algorithms that separate and extract the atrial rhythm starting from a prior estimation of the main atrial frequency. The first one is an algebraic method based on the maximization of a cost function that measures the periodicity. The other one is an adaptive algorithm that exploits the decorrelation of the atrial and other signals diagonalizing the correlation matrices at multiple lags of the period of atrial activity. The algorithms are applied successfully to synthetic and real data. In simulated ECGs, the average correlation index obtained was 0.811 and 0.847, respectively. In real ECGs, the accuracy of the results was validated using spectral and temporal parameters. The average peak frequency and spectral concentration obtained were 5.550 and 5.554 Hz and 56.3 and 54.4%, respectively, and the kurtosis was 0.266 and 0.695. For validation purposes, we compared the proposed algorithms with established methods, obtaining better results for simulated and real registers.This paper is in part supported by the Valencia Regional Government (Generalitat Valenciana) through project GV/2010/002 (Conselleria d'Educacio) and by the Universidad Politecnica de Valencia under grant no. PAID-06-09-003-382.Llinares Llopis, R.; Igual García, J. (2011). Exploiting periodicity to extract the atrial activity in atrial arrhythmias. EURASIP Journal on Advances in Signal Processing. 1(134):1-16. doi:10.1186/1687-6180-2011-134S1161134Rieta J, Castells F, Sanchez C, Zarzoso V, Millet J: IEEE Trans Biomed Eng. 2004,51(7):1176. 10.1109/TBME.2004.827272Fuster V, Ryden L, Asinger R, et al.: Circulation. 2001, 104: 2118.Sörnmo L, Stridh M, Husser D, Bollmann A, Olsson S: Philos Trans A. 2009,367(1887):235. 10.1098/rsta.2008.0162Bollmann A, Husser D, Mainardi L, Lombardi F, Langley P, Murray A, Rieta J, Millet J, Olsson S, Stridh M, Sörnmo L: Europace. 2006,8(11):911. 10.1093/europace/eul113Stridh M, Sornmo L, Meurling C, Olsson S: IEEE Trans Biomed Eng. 2004,51(1):100. 10.1109/TBME.2003.820331Asano Y, Saito J, Matsumoto K, Kaneko K, Yamamoto T, Uchida M: Am J Cardiol. 1992,69(12):1033. 10.1016/0002-9149(92)90859-WStambler B, Wood M, Ellenbogen K: Circulation. 1997,96(12):4298.Manios E, Kanoupakis E, Chlouverakis G, Kaleboubas M, Mavrakis H, Vardas P: Cardiovasc Res. 2000,47(2):244. 10.1016/S0008-6363(00)00100-0Stridh M, Sornmo L: IEEE Trans Biomed Eng. 2001,48(1):105. 10.1109/10.900266Castells F, Igual J, Rieta J, Sanchez C, Millet J: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03). 2003., 5:Castells F, Rieta J, Millet J, Zarzoso V: IEEE Trans Biomed Eng. 2005,52(2):258. 10.1109/TBME.2004.840473Petrutiu S, Ng J, Nijm G, Al-Angari H, Swiryn S, Sahakian A: IEEE Eng Med Biol Mag. 2006,25(6):24.Stridh M, Bollmann A, Olsson S, Sornmo L: IEEE Eng Med Biol Mag. 2006,25(6):31.Langley P, Bourke J, Murray A: Computers in Cardiology. 2000.Sassi R, Corino V, Mainardi L: Ann Biomed Eng. 2009,37(10):2082-921. 10.1007/s10439-009-9757-3Llinares R, Igual J, Salazar A, Camacho A: Digit Signal Process. 2011,21(2):391. 10.1016/j.dsp.2010.06.005Sameni R, Jutten C, Shamsollahi M: IEEE Trans Biomed Eng. 2008,55(8):1935.Li X: IEEE Signal Process Lett. 2006,14(1):58.Llinares R, Igual J, Miró-Borrás J: Comput Biol Med. 2010,40(11-12):943. 10.1016/j.compbiomed.2010.10.006Belouchrani A, Abed-Meraim K, Cardoso J, Moulines E: IEEE Trans Signal Process. 1997,45(2):434. 10.1109/78.554307Lemay M, Vesin J, van Oosterom A, Jacquemet V, Kappenberger L: IEEE Trans Biomed Eng. 2007,54(3):542.Alcaraz R, Rieta J: Physiol Meas. 2008,29(12):1351. 10.1088/0967-3334/29/12/00