ECG Signal Enhancement Method Based on Independent Component Analysis and Its Application

Decomposing linear mixtures or superpositions into their components is a problem occurring in many different branches of science, such as telecommunications, Seismology, and biomedical signal analysis. Blind source separation (BSS) in particular, deals with the case where neither the sources, nor the mixing matrix or process of mixing are known, the only available data are the mixed signals. The standard approach to BSS is Independent Component Analysis (ICA). In fact ICA is a statistical technique that represents a multidimensional random vector as a linear combination of nongaussian random variables - ‘independent components’-that are as independent as possible. In 3D seismic surveys involved in exploration operations, recorded time series in each dimension are taken to be independent in nature and behavior, which is a direct result of physical response of materials into which seismic waves penetrate. But as an observation one dimension is sometimes contaminated up to one fifth by information from another dimension, resulting an increase in SNR. Here we have applied FAST-ica algorithm to a 3D seismic record sample to extract least dependent recordings for all three spatial dimensions. In order to test the reliability of the decomposition we used mutual information (MI) transfer between signals to confirm the result of outputs from FAST-ica algorithm as, least dependent components, leading to more accurate interpretations of petrophysical parameters.

Independent component analysis (ICA) is a computational method for separating independent-source signals from a mixed-signal series. The common ICA method cannot be directly used for separating when there is only one sensor collecting the signals. To solve the problem of separating single-channel mixed signals, a novel method based on ICA and the convolutional wavelet packet is proposed to divide a blind source. The method provides a new approach for blind source separation when the number of original signals is limited. First, the sub-band data are obtained by a non-downsampling convolutional wavelet packet. Because the convolutional wavelet packet is not down-sampled, the lengths of different sub-band sequences are the same as the original signal and correspond to a certain frequency band. Second, to decrease the influence of edge frequency aliasing, each sub-band of the wavelet packet is processing using frequency division. Using preprocessing, the authors are able to create a multiple-channel mixed-signal series that can be used to obtain the inputs of the ICA. The method proposed in this paper was applied to blind source separation (BBS) using a simulated sine signal series; real signal series collected from a motor vibration testing platform are also used in testing. The results show that the method produces results in blind source separation that are equivalent to using multiple mixed signals directly.

In this paper, we propose a novel method of removal of baseline wander noise from a noisy ECG signal. We have shown that the original (clean) ECG signal can be modeled as a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order fractional Brownian motion (fBm) process, while the baseline wander noise can be modeled as a 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> order fBm process. We use the eigenvectors of the auto-covariance matrix of the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order fBm process characterizing the original ECG signal to design an M-channel uniformly decimated filterbank. Noisy ECG signal is fed to this filterbank and the subband signals are processed for removal of baseline wander noise. Performance comparison with other baseline wander removal methods shows the efficacy of the proposed method.

Modulation classification (MC) of a signal is one of the major tasks of an intelligent receiver in various civilian and military applications. For mixed signals, it is a big challenge to recognize their modulation types. In this paper, a novel MC algorithm based on independent component analysis (ICA) is proposed. The proposed algorithm can separate mixed signals and identify modulation types for demodulation simultaneously under block fading channel. The design of the algorithm essentially involves two steps, namely, the separation of mixed signals through ICA algorithm and MC for the separated signals. Depending on ICA algorithm chosen in the first step, statistically independent signals are separated in block fading channel. Regarding the second step, higher-order cumulants are chosen as a promising approach for MC of the separated signals. Through the algorithm, the problem of the MC of the mixed signals can be solved and the mixed signals can be demodulated. Furthermore, simulation results are provided to verify the performance and robustness of the algorithm.

The recent work in the author's research group on using independent component analysis (ICA) for the analysis and prediction of financial time series is reviewed. ICA belongs to the group of linear transform methods, with the goal to make a transform from the observed signals into a signal space in which the signals are statistically independent. Sometimes independence can be attained, especially in blind source separation in which the observed signals are assumed to be linear mixtures of independent source components. Then the goal of ICA is to invert the unknown mixing operation. Even when independence is not possible, as is often the case in financial time series, the ICA transformation produces useful component signals whose dependence is reduced, and that are nonGaussian with a density allowing sparse coding. The ICA transformation is also related to the temporal structure of the found signals as measured by Kolmogorov complexity or its approximations. The signals are structured and hence may be easier to interpret and predict. After discussing the ICA criterion within the context of linear signal transforms, the FastICA algorithm is reviewed. It is a computationally efficient method for finding a subset or all of the component signals. Then, two financial applications are covered: decomposition of parallel financial time series of weekly sales into basic factors, and prediction of mixture time series by linear combinations of predictions on the ICA components.

Integer-to-Integer Wavelet Transform Based ECG Steganography for Securing Patient Confidential Information

Independent Component Analysis in Frequency Domain and Its Application in Structural Vibration Signal Separation

Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegative data. This method has been applied for blind source separation (BSS) when the sources are nonnegative. This paper presents a new NMF method for independent component analysis (ICA), which is useful for BSS without the nonnegativity constraint. Using this method, we transform the sources by their cumulative distribution functions (CDFs) and perform the nonparametric quantization to construct a nonnegative matrix where each entry represents the joint probability density of two transformed signals. The NMF procedure is accordingly realized to find the ICA demixing matrix. The independence between sources is maximized towards attaining the uniformity in the joint probability density. In the experiments on the separation of signal and music signals, we show the effectiveness of the proposed NMF-ICA compared to the infomax ICA and FastICA algorithms.

Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.

Independent Component Analysis (ICA) is used to separate the relatively independent signals from the source. Gravity tide signal is a mixed signal which is caused by the moon and the sun, so ICA is applied to separate gravity tide signal in this paper. Because of the slowly convergence speed and local search for the target in ICA, Particle Swarm Optimization which could find target globally and quickly is proposed to search the optical target in ICA. Simulation results show that the method proposed in this paper solves the problem presented in ICA and separates gravity tide signal into three parts. Every part represents the signal which is corresponding to the theory frequencies of the harmonic component in gravity tide signal. So the method proposed in the paper can divide the signal into three parts which is corresponding to harmonic component automatically.

The immense scope in the field of biomedical signal processing independent component analysis (ICA ) is gaining momentum due to huge data base requirement for quality testing. The diagnosis of patient is based on visual observation of recorded ECG signals may not be accurate. Many attempts have been carried out to remove noise or artifacts such as interference (few Hz), electromagnetic emission, muscle activities and others from ECG signal. To achieve better understanding, ICA algorithms helps in analyzing ECG signals.This paper describes some algorithms of ICA in brief, such as Fast-ICA, Kernel-ICA, MS -ICA, JADE, EGLD-ICA, Robust ICA etc. The quality & performance of some of the ICA algorithms are tested and analysis of each can be done with respect to noise/artifacts, SIR(signal interference ratio), PI(performance index). In the conclusion tries giving selection type of ICA algorithm for different ECG database.

Aiming at the problem of electromagnetic interference signal separation, we propose a single-channel blind source separation method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA). Firstly, decompose the mixed interference signal by CEEMDAN to obtain a series of intrinsic mode functions (IMF). Secondly, combine them into a new multi-dimensional signal and solve its correlation matrix, and use singular value decomposition to obtain the eigenvalues of the matrix, and then use it to estimate the source number. Thirdly, select those IMFs whose correlation coefficients with the observed signal are bigger and regard them together with the original signal after denoising as new observed signals. Finally, recover the source signals by fast independent component analysis (Fast-ICA). We also extend our method to multi-channel BBS, and experiment results show that our method can eliminate unnecessary noise in the signal and perform well.

In this paper, we proposed a new signal processing algorithm based on independent component analysis (ICA) for single channel ECG data. For the application ICA to single channel data, mixed (multi-channel) signals are constructed by adding some delay to original data. By ICA, signal enhancement is acquired. For validation of usefulness of this signal, QRS complex detection was accompanied. In QRS detection process, Hilbert transform and wavelet transform were used and good QRS detection efficacy was obtained. Furthermore, a signal, which could not be filtered properly using existing algorithm, also had better signal enhancement. In future, we need to study on the algorithm optimization and simplification.

Independent Component Analysis (ICA) is a promising tool for the analysis of functional magnetic resonance imaging (fMRI) time series. In these studies, mostly assumed is a spatially independent component map of fMRI data (spatial ICA). In this paper, we assume that the temporal courses of the signal and noises are independent within a Tiny spatial domain (temporal ICA). Then with fast-ICA algorithm, spatially neighboring fMRI data were blindly separated into several temporal courses and were preassumed to be formed by a signal time course and several noise time courses where the signal has the largest correlation coefficient with the reference signal. The final functional imaging was completed for the signals obtained from each voxel. Simulations showed that compared with the spatial ICA method, the new temporal ICA method is more effective than the spatial ICA in detecting weak signal in a fMRI dataset. As background noise, the simulations include simulated Gaussian noise and fMRI data without stimulation. Finally, vivo fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex and that evoked by auditory stimuli are mainly in the region of the primary temporal cortex.

The extraction of Fetal ECG (FECG) is still a difficult task in non-invasive approach since the frequency components of dominant maternal ECG and fetal ECG signals are generally overlapped. Besides, the baseline wander and high-frequency noise make the clear FECG difficult to be extracted. In this paper, we proposed a new combination of independent component analysis (ICA), improved complete ensemble empirical mode decomposition (ICEEMDAN), and wavelet shrinkage (WS) de-noising (ICA-ICEEMDAN-WS) to extract FECG while reducing the noise. ICA algorithm as the first step of our proposed method separated the mixed abdominal ECG signal to obtain the noisy FECG. Then for further denoisng and obtain higher SNR, noisy FECG was decomposed by the newly proposed ICEEMDAN that is more accurate for non-linear and non-stationary bio-signal processing. The informative extracted components were then determined based on the statistical significance test. WS de-noising removed the remained noisy subcomponents and finally the baseline wander was reduced by partial reconstruction. The performance of ICA-ICEEMDAN-WS method was evaluated using simulated and real data sets. Our results showed that our proposed method outperformed ICA-EEMD-WS, the recently proposed algorithm based on ensemble empirical mode decomposition