Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Adaptive blind signal separation.

by Chi-Chiu Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 124-131).Abstract --- p.iAcknowledgments --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Blind Signal Separation Problem --- p.1Chapter 1.2 --- Contributions of this Thesis --- p.3Chapter 1.3 --- Applications of the Problem --- p.4Chapter 1.4 --- Organization of the Thesis --- p.5Chapter 2 --- The Blind Signal Separation Problem --- p.7Chapter 2.1 --- The General Blind Signal Separation Problem --- p.7Chapter 2.2 --- Convolutive Linear Mixing Process --- p.8Chapter 2.3 --- Instantaneous Linear Mixing Process --- p.9Chapter 2.4 --- Problem Definition and Assumptions in this Thesis --- p.9Chapter 3 --- Literature Review --- p.13Chapter 3.1 --- Previous Works on Blind Signal Separation with Instantaneous Mixture --- p.13Chapter 3.1.1 --- Algebraic Approaches --- p.14Chapter 3.1.2 --- Neural approaches --- p.15Chapter 3.2 --- Previous Works on Blind Signal Separation with Convolutive Mixture --- p.20Chapter 4 --- The Information-theoretic ICA Scheme --- p.22Chapter 4.1 --- The Bayesian YING-YANG Learning Scheme --- p.22Chapter 4.2 --- The Information-theoretic ICA Scheme --- p.25Chapter 4.2.1 --- Derivation of the cost function from YING-YANG Machine --- p.25Chapter 4.2.2 --- Connections to previous information-theoretic approaches --- p.26Chapter 4.2.3 --- Derivation of the Algorithms --- p.27Chapter 4.2.4 --- Roles and Constraints on the Nonlinearities --- p.30Chapter 4.3 --- Direction and Motivation for the Analysis of the Nonlinearity --- p.30Chapter 5 --- Properties of the Cost Function and the Algorithms --- p.32Chapter 5.1 --- Lemmas and Corollaries --- p.32Chapter 5.1.1 --- Singularity of J(V) --- p.33Chapter 5.1.2 --- Continuity of J(V) --- p.34Chapter 5.1.3 --- Behavior of J(V) along a radially outward line --- p.35Chapter 5.1.4 --- Impossibility of divergence of the information-theoretic ICA al- gorithms with a large class of nonlinearities --- p.36Chapter 5.1.5 --- Number and stability of correct solutions in the 2-channel case --- p.37Chapter 5.1.6 --- Scale for the equilibrium points --- p.39Chapter 5.1.7 --- Absence of local maximum of J(V) --- p.43Chapter 6 --- The Algorithms with Cubic Nonlinearity --- p.44Chapter 6.1 --- The Cubic Nonlinearity --- p.44Chapter 6.2 --- Theoretical Results on the 2-Channel Case --- p.46Chapter 6.2.1 --- Equilibrium points --- p.46Chapter 6.2.2 --- Stability of the equilibrium points --- p.49Chapter 6.2.3 --- An alternative proof for the stability of the equilibrium points --- p.50Chapter 6.2.4 --- Convergence Analysis --- p.52Chapter 6.3 --- Experiments on the 2-Channel Case --- p.53Chapter 6.3.1 --- Experiments on two sub-Gaussian sources --- p.54Chapter 6.3.2 --- Experiments on two super-Gaussian sources --- p.55Chapter 6.3.3 --- Experiments on one super-Gaussian source and one sub-Gaussian source which are globally sub-Gaussian --- p.57Chapter 6.3.4 --- Experiments on one super-Gaussian source and one sub-Gaussian source which are globally super-Gaussian --- p.59Chapter 6.3.5 --- Experiments on asymmetric exponentially distributed signals .。 --- p.60Chapter 6.3.6 --- Demonstration on exactly and nearly singular initial points --- p.61Chapter 6.4 --- Theoretical Results on the 3-Channel Case --- p.63Chapter 6.4.1 --- Equilibrium points --- p.63Chapter 6.4.2 --- Stability --- p.66Chapter 6.5 --- Experiments on the 3-Channel Case --- p.66Chapter 6.5.1 --- Experiments on three pairwise globally sub-Gaussian sources --- p.67Chapter 6.5.2 --- Experiments on three sources consisting of globally sub-Gaussian and globally super-Gaussian pairs --- p.67Chapter 6.5.3 --- Experiments on three pairwise globally super-Gaussian sources --- p.69Chapter 7 --- Nonlinearity and Separation Capability --- p.71Chapter 7.1 --- Theoretical Argument --- p.71Chapter 7.1.1 --- Nonlinearities that strictly match the source distribution --- p.72Chapter 7.1.2 --- Nonlinearities that loosely match the source distribution --- p.72Chapter 7.2 --- Experiment Verification --- p.76Chapter 7.2.1 --- Experiments on reversed sigmoid --- p.76Chapter 7.2.2 --- Experiments on the cubic root nonlinearity --- p.77Chapter 7.2.3 --- Experimental verification of Theorem 2 --- p.77Chapter 7.2.4 --- Experiments on the MMI algorithm --- p.78Chapter 8 --- Implementation with Mixture of Densities --- p.80Chapter 8.1 --- Implementation of the Information-theoretic ICA scheme with Mixture of Densities --- p.80Chapter 8.1.1 --- The mixture of densities --- p.81Chapter 8.1.2 --- Derivation of the algorithms --- p.82Chapter 8.2 --- Experimental Verification on the Nonlinearity Adaptation --- p.84Chapter 8.2.1 --- Experiment 1: Two channels of sub-Gaussian sources --- p.84Chapter 8.2.2 --- Experiment 2: Two channels of super-Gaussian sources --- p.85Chapter 8.2.3 --- Experiment 3: Three channels of different signals --- p.89Chapter 8.3 --- Seeking the Simplest Workable Mixtures of Densities ......... .。 --- p.91Chapter 8.3.1 --- Number of components --- p.91Chapter 8.3.2 --- Mixture of two densities with only biases changeable --- p.93Chapter 9 --- ICA with Non-Kullback Cost Function --- p.97Chapter 9.1 --- Derivation of ICA Algorithms from Non-Kullback Separation Functionals --- p.97Chapter 9.1.1 --- Positive Convex Divergence --- p.97Chapter 9.1.2 --- Lp Divergence --- p.100Chapter 9.1.3 --- De-correlation Index --- p.102Chapter 9.2 --- Experiments on the ICA Algorithm Based on Positive Convex Divergence --- p.103Chapter 9.2.1 --- Experiments on the algorithm with fixed nonlinearities --- p.103Chapter 9.2.2 --- Experiments on the algorithm with mixture of densities --- p.106Chapter 10 --- Conclusions --- p.107Chapter A --- Proof for Stability of the Equilibrium Points of the Algorithm with Cubic Nonlinearity on Two Channels of Signals --- p.110Chapter A.1 --- Stability of Solution Group A --- p.110Chapter A.2 --- Stability of Solution Group B --- p.111Chapter B --- Proof for Stability of the Equilibrium Points of the Algorithm with Cubic Nonlinearity on Three Channels of Signals --- p.119Chapter C --- Proof for Theorem2 --- p.122Bibliography --- p.12

A frequency-based BSS technique for speech source separation.

Ngan Lai Yin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 95-100).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Blind Signal Separation (BSS) Methods --- p.4Chapter 1.2 --- Objectives of the Thesis --- p.6Chapter 1.3 --- Thesis Outline --- p.8Chapter 2 --- Blind Adaptive Frequency-Shift (BA-FRESH) Filter --- p.9Chapter 2.1 --- Cyclostationarity Properties --- p.10Chapter 2.2 --- Frequency-Shift (FRESH) Filter --- p.11Chapter 2.3 --- Blind Adaptive FRESH Filter --- p.12Chapter 2.4 --- Reduced-Rank BA-FRESH Filter --- p.14Chapter 2.4.1 --- CSP Method --- p.14Chapter 2.4.2 --- PCA Method --- p.14Chapter 2.4.3 --- Appropriate Choice of Rank --- p.14Chapter 2.5 --- Signal Extraction of Spectrally Overlapped Signals --- p.16Chapter 2.5.1 --- Simulation 1: A Fixed Rank --- p.17Chapter 2.5.2 --- Simulation 2: A Variable Rank --- p.18Chapter 2.6 --- Signal Separation of Speech Signals --- p.20Chapter 2.7 --- Chapter Summary --- p.22Chapter 3 --- Reverberant Environment --- p.23Chapter 3.1 --- Small Room Acoustics Model --- p.23Chapter 3.2 --- Effects of Reverberation to Speech Recognition --- p.27Chapter 3.2.1 --- Short Impulse Response --- p.27Chapter 3.2.2 --- Small Room Impulse Response Modelled by Image Method --- p.32Chapter 3.3 --- Chapter Summary --- p.34Chapter 4 --- Information Theoretic Approach for Signal Separation --- p.35Chapter 4.1 --- Independent Component Analysis (ICA) --- p.35Chapter 4.1.1 --- Kullback-Leibler (K-L) Divergence --- p.37Chapter 4.2 --- Information Maximization (Infomax) --- p.39Chapter 4.2.1 --- Stochastic Gradient Descent and Stability Problem --- p.41Chapter 4.2.2 --- Infomax and ICA --- p.41Chapter 4.2.3 --- Infomax and Maximum Likelihood --- p.42Chapter 4.3 --- Signal Separation by Infomax --- p.43Chapter 4.4 --- Chapter Summary --- p.45Chapter 5 --- Blind Signal Separation (BSS) in Frequency Domain --- p.47Chapter 5.1 --- Convolutive Mixing System --- p.48Chapter 5.2 --- Infomax in Frequency Domain --- p.52Chapter 5.3 --- Adaptation Algorithms --- p.54Chapter 5.3.1 --- Standard Gradient Method --- p.54Chapter 5.3.2 --- Natural Gradient Method --- p.55Chapter 5.3.3 --- Convergence Performance --- p.56Chapter 5.4 --- Subband Adaptation --- p.57Chapter 5.5 --- Energy Weighting --- p.59Chapter 5.6 --- The Permutation Problem --- p.61Chapter 5.7 --- Performance Evaluation --- p.63Chapter 5.7.1 --- De-reverberation Performance Factor --- p.63Chapter 5.7.2 --- De-Noise Performance Factor --- p.63Chapter 5.7.3 --- Spectral Signal-to-noise Ratio (SNR) --- p.65Chapter 5.8 --- Chapter Summary --- p.65Chapter 6 --- Simulation Results and Performance Analysis --- p.67Chapter 6.1 --- Small Room Acoustics Modelled by Image Method --- p.67Chapter 6.2 --- Signal Sources --- p.68Chapter 6.2.1 --- Cantonese Speech --- p.69Chapter 6.2.2 --- Noise --- p.69Chapter 6.3 --- De-Noise and De-Reverberation Performance Analysis --- p.69Chapter 6.3.1 --- Speech and White Noise --- p.73Chapter 6.3.2 --- Speech and Voice Babble Noise --- p.76Chapter 6.3.3 --- Two Female Speeches --- p.79Chapter 6.4 --- Recognition Accuracy Performance Analysis --- p.83Chapter 6.4.1 --- Speech and White Noise --- p.83Chapter 6.4.2 --- Speech and Voice Babble Noise --- p.84Chapter 6.4.3 --- Two Cantonese Speeches --- p.85Chapter 6.5 --- Chapter Summary --- p.87Chapter 7 --- Conclusions and Suggestions for Future Research --- p.88Chapter 7.1 --- Conclusions --- p.88Chapter 7.2 --- Suggestions for Future Research --- p.91Appendices --- p.92A The Proof of Stability Conditions for Stochastic Gradient De- scent Algorithm (Ref. (4.15)) --- p.92Bibliography --- p.9

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features