Single Channel Independent Component Analysis Research Articles

Estimation of central blood pressure waveform from femoral blood pressure waveform by blind sources separation.

Central blood pressure (cBP) is a better indicator of cardiovascular morbidity and mortality than peripheral BP (pBP). However, direct cBP measurement requires invasive techniques and indirect cBP measurement is based on rigid and empirical transfer functions applied to pBP. Thus, development of a personalized and well-validated method for non-invasive derivation of cBP from pBP is necessary to facilitate the clinical routine. The purpose of the present study was to develop a novel blind source separation tool to separate a single recording of pBP into their pressure waveforms composing its dynamics, to identify the compounds that lead to pressure waveform distortion at the periphery, and to estimate the cBP. The approach is patient-specific and extracts the underlying blind pressure waveforms in pBP without additional brachial cuff calibration or any a priori assumption on the arterial model. The intra-arterial femoral BPfe and intra-aortic pressure BPao were anonymized digital recordings from previous routine cardiac catheterizations of eight patients at the German Heart Centre Berlin. The underlying pressure waveforms in BPfe were extracted by the single-channel independent component analysis (SCICA). The accuracy of the SCICA model to estimate the whole cBP waveform was evaluated by the mean absolute error (MAE), the root mean square error (RMSE), the relative RMSE (RRMSE), and the intraclass correlation coefficient (ICC). The agreement between the intra-aortic and estimated parameters including systolic (SBP), diastolic (DBP), mean arterial pressure (MAP), and pulse pressure (PP) was evaluated by the regression and Bland-Altman analyses. The SCICA tool estimated the cBP waveform non-invasively from the intra-arterial BPfe with an MAE of 0.159 ± 1.629, an RMSE of 5.153 ± 0.957 mmHg, an RRMSE of 5.424 ± 1.304%, and an ICC of 0.94, as well as two waveforms contributing to morphological distortion at the femoral artery. The regression analysis showed a strong linear trend between the estimated and intra-aortic SBP, DBP, MAP, and PP with high coefficient of determination R2 of 0.98, 0.99, 0.99, and 0.97 respectively. The Bland-Altman plots demonstrated good agreement between estimated and intra-aortic parameters with a mean error and a standard deviation of difference of -0.54 ± 2.42 mmHg [95% confidence interval (CI): -5.28 to 4.20] for SBP, -1.97 ± 1.62 mmHg (95% CI: -5.14 to 1.20) for DBP, -1.49 ± 1.40 mmHg (95% CI: -4.25 to 1.26) for MAP, and 1.43 ± 2.79 mmHg (95% CI: -4.03 to 6.90) for PP. The SCICA approach is a powerful tool that identifies sources contributing to morphological distortion at peripheral arteries and estimates cBP.

A new robust approach for fault location in transmission lines using single channel independent component analysis

A new robust approach for fault location in transmission lines using single channel independent component analysis

A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. First, phase space reconstruction is performed on the vibration signal to make the proper input for ICA algorithm. The original is then decomposed into several component signals. The fractal dimension of each component signals is calculated to determine whether the signal should be considered noise. Noisy component signals are then processed by wavelet denoising. Finally, the output signal after noise reduction is reconstructed using the filtered “right” component signals. This paper uses the method to analyze real noisy vibration signal. Experimental results show the effectiveness of the proposed method.

Timing the opening and closure of the aortic valve using a phonocardiogram envelope: a performance test for systolic time intervals measurement

Objective. This work explored the reliability of using points on the heart sounds envelope as indicators of the opening and closure of the aortic valve (AVO, AVC) to measure the pre-ejection period (PEP) and the left ventricular ejection time (LVET). Approach. 36 phonocardiograms (PCGs) from healthy subjects and cardiovascular disease subjects were denoised using single-channel independent component analysis (SCICA) and, from the Hilbert envelopes, the positions of the S1 and S2 peaks were detected (pS1, pS2). Complementarily, the positions of the local maxima of S1 and S2 (mS1, mS2) and the points surrounding pS1 and pS2 (tS1, tS2) were obtained. Finally, the reliability of these points (and the corresponding PEP and LVET intervals) was evaluated by the calculation of three error indexes (ePEP, eLVET, and score) and by comparison to reference annotations provided by echocardiography using the Bland–Altman analysis and the paired T-test. Main results. The results indicated that, from a total of 920 and 341 heartbeats in the healthy and diseased groups, respectively, the timing points given by pS1 and pS2 (or mS1 and mS2) were unlikely to substitute for the reference annotations and, thus, are unreliable for measuring the PEP and LVET intervals in the PCG. The t-points evaluation, on the other hand, indicated that tS1 was likely to substitute for AVO and was thus reliable for measuring the PEP using the PCG, with median and interquartile ranges of 0.3(8.3) ms and −0.2(7.5) ms for each group. Future work will generate an envelope with higher temporal resolution, from where tS1 and tS2 can be more accurately detected to improve the PEP and LVET measurements on a larger dataset. Significance. The statistical tests revealed that the envelope of S1 is suitable for extracting a timing point from which the pre-ejection interval can be reliably quantified, and discarded the local maximum used in other studies.

Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity

The power distribution network is susceptible to several Power Quality (PQ) disturbances. Among those, the harmonic and interharmonic distortions should be highlighted due to their high proliferation. This work proposes the utilization of signal processing techniques to decompose the electrical voltage and/or current signals into its harmonic and interhamonic component waveforms through a Blind Source Separation (BSS) algorithm named Second Order Blind Identification (SOBI). This algorithm is normally applied to a multivariate data set, what implies in a necessity of multiple measurements in different points of the system that will be analyzed. However, Single-Channel Blind Source Separation (SCBSS) method will be proposed in this work to estimate the components via SOBI using only one measured signal point. The method works as a set of adaptive filters whose coefficients are blindly obtained via SOBI and is responsible for the components separation. An Exact Model Order (EMO) algorithm will be used to improve the performance of the SOBI algorithm in order to estimate the correct number of components to be separated. Also, the EMO will be helpful to reduce the computational complexity of the SOBI. The performance of the proposed SCBSS method will be compared to that of the SCICA (Single-Channel Independent Component Analysis) based on the well-known FastICA algorithm, which employs Higher Order Statistics (HOS). It will be shown that the proposed SCBSS overtakes the SCICA for harmonic and interharmonic decomposition in performance and in reduced computational complexity. Also, the proposed SCBSS method will be compared to EMO-ESPRIT algorithm, where will be shown that the SCBSS achieved better results in noisy and time-varying scenarios. Finally, the proposed SCBSS will be applied for the analysis of a voltage signal acquired from the simulation of a power system containing wind generation.

Suppression of periodic interference during tunnel seismic predictions via the Hankel-SVD-ICA method

Periodic interference is one of the most common interference sources in tunnel seismic prediction (TSP). This noise can deleteriously affect signal quality, and the signal can be completely drowned out by the noise in some cases, which can seriously affect seismic wave velocity estimations and TSP detection distance and detection accuracy. To solve this problem, a single-channel independent component analysis (SICA) based on the Hankel matrix and a singular value decomposition (Hankel-SVD-ICA) is proposed to suppress the periodic interferences. The traditional band-pass filters and notch filters are ineffective when seismic data and the periodic interference bandwidths overlap. In this study, we used TSP data to evaluate the Hankel-SVD-ICA method in detail. The Hankel-SVD-ICA was tested on synthetic recordings and field data, and the results showed that the method effectively separates the seismic data from data mixed with strong periodic interferences when seismic data and periodic interference bandwidths overlap. Qualitative and quantitative comparisons show that the Hankel-SVD-ICA provides better recordings than both the Hankel matrix singular value decomposition (HMSVD) and notch filters. The comparisons also show higher quality data and a more reliable P-wave velocity assessment when the Hankel-SVD-ICA is applied to TSP data, which significantly increases the detection distance and improves the lithological interpretation. Moreover, we study the Hankel-SVD-ICA method for frequency time-varying periodic interference suppression in TSP, and the results provide a good reference for suppressing periodic interference in other fields.

Harmonic separation from grid voltage using ensemble empirical-mode decomposition and independent component analysis

Summary Harmonics and subharmonics in power systems distort grid voltage, reduce the quality of power, and affect the security of the power grid. Rapid and accurate harmonic separation from grid voltage is the crucial technology to ensure that power systems operate safely and stably. The blind source separation method based on independent component analysis has been used to separate the components of grid voltage. As the grid voltage is acquired in only a single channel, harmonic separation from it is classed as a single-channel independent component analysis problem. Hence, this paper proposes a method for the harmonic separation of single-channel grid voltage that combines ensemble empirical-mode decomposition and FastICA. Compared with 2 traditional methods, the fast Fourier transform and the discrete wavelet transform, the proposed method is superior in that it does not require prior knowledge of the frequency of the original source, does not feature mode mixing, and is more robust against noise. Results obtained from both synthetic and real-life signals demonstrated the excellent performance of the proposed method.

Eye blinks artefacts detection in a single EEG channel

In clinical conditions, the EEG measurements are mainly influenced by muscles and ocular movements, especially eye blinks. In this work, we present a new method to detect and reject eye blinks from a single-channel EEG signal. In an offline use mode, the proposed approach is based on statistical computations. It aims, in a first time, at estimating the pure EEG data interval. In a second time, we seek to improve the errors that may occur during the first step by using the Fisher-Snedecor test. By using ROC performance metrics, kappa coefficient and signal-to-artefact ratio (SAR), the proposed method is compared to an expert detection and to single-channel independent component analysis (SC-ICA), one of the widely used and robust methods for artefacts rejection. Experimental results show large values even when using the expert's annotation and the comparison to the SC-ICA method. This reflects the efficiency of the proposed method in detecting and rejecting blinks from a single-channel EEG signal.

Eye blinks artefacts detection in a single EEG channel

In clinical conditions, the EEG measurements are mainly influenced by muscles and ocular movements, especially eye blinks. In this work, we present a new method to detect and reject eye blinks from a single-channel EEG signal. In an offline use mode, the proposed approach is based on statistical computations. It aims, in a first time, at estimating the pure EEG data interval. In a second time, we seek to improve the errors that may occur during the first step by using the Fisher-Snedecor test. By using ROC performance metrics, kappa coefficient and signal-to-artefact ratio (SAR), the proposed method is compared to an expert detection and to single-channel independent component analysis (SC-ICA), one of the widely used and robust methods for artefacts rejection. Experimental results show large values even when using the expert's annotation and the comparison to the SC-ICA method. This reflects the efficiency of the proposed method in detecting and rejecting blinks from a single-channel EEG signal.

Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique used to measure changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) in the brain. In this study, we present a decomposition approach based on single-channel independent component analysis (scICA) to investigate the contribution of physiological noise to fNIRS signals during rest. Single-channel ICA is an underdetermined decomposition method, which separates a single time series into components containing nonredundant spectral information. Using scICA, fNIRS signals from a total of 17 subjects were decomposed into the constituent physiological components. The percentage contribution of the classes of physiology to the fNIRS signals including low-frequency (LF) fluctuations, respiration, and cardiac oscillations was estimated using spectral domain classification methods. Our results show that LF oscillations accounted for 40% to 55% of total power of both the oxy-Hb and deoxy-Hb signals. Respiration and its harmonics accounted for 10% to 30% of the power, and cardiac pulsations and cardio-respiratory components accounted for 10% to 30%. We describe this scICA method for decomposing fNIRS signals, which unlike other approaches to spatial covariance reduction is applicable to both single- or multiple-channel fNIRS signals and discuss how this approach allows functionally distinct sources of noise with disjoint spectral support to be separated from obscuring systemic physiology.

A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics

Methods of utilizing independent component analysis (ICA) give little guidance about practical considerations for separating single-channel real-world data, in which most of them are nonlinear, nonstationary, and even chaotic in many fields. To solve this problem, a three-step method is provided in this paper. In the first step, the measured signal which is assumed to be piecewise higher order stationary time series is introduced and divided into a series of higher order stationary segments by applying a modified segmentation algorithm. Then the state space is reconstructed and the single-channel signal is transformed into a pseudo multiple input multiple output (MIMO) mode using a method of nonlinear analysis based on the high order statistics (HOS). In the last step, ICA is performed on the pseudo MIMO data to decompose the single channel recording into its underlying independent components (ICs) and the interested ICs are then extracted. Finally, the effectiveness and excellence of the higher order single-channel ICA (SCICA) method are validated with measured data throughout experiments. Also, the proposed method in this paper is proved to be more robust under different SNR and/or embedding dimension via explicit formulae and simulations.

A method based on independent component analysis for single and multiple power quality disturbance classification

This paper proposes a method based on single channel independent component analysis for single and multiple power quality disturbance classification. The proposed method decouples the power system signal into its independent components, which are classified by specialized classifiers. The classifier outputs are combined by using a logic that gives the final classification. Five classes of single disturbances and twelve of multiple disturbances are considered and a classification efficiency above 97% is achieved for each event class. Both qualitative and quantitative analysis elucidate the efficiency of the proposed method. Results are obtained from both simulated and experimental signals.

A phase space reconstruction based single channel ICA algorithm and its application in dam deformation analysis

Separating noise, systematic errors and deformation components that are caused by different forces such as water pressure and temperature should be identified from dam deformation monitoring data to facilitate effective dam deformation analysis and consequently determine the potential dam structural damage. As source signals are assumed to be mutually independent, independent component analysis (ICA) can be used to separate the source signals from their mixtures. In addition to signal independence, another requirement of ICA theory is that the number of mixtures should not be less than that of source signals. Unfortunately, in most cases of dam deformation monitoring, only a single channel of observed displacement data is available for each monitoring point. Therefore, ICA with single channel data input (called single channel ICA) is necessary in the application of deformation analysis. In this paper, we apply a phase space reconstruction based single channel ICA (PSR-ICA) algorithm to de-noise deformation monitoring data and separate deformation components introduced by different forces from these de-noised data. A numerical simulation is conducted, and results indicate that PSR-ICA is an efficient tool not only for denoising data but also for separating deformation components caused by different forces. PSR-ICA is then further utilised to process the displacement monitoring data of Wuqiangxi Dam. Results indicate that the two extracted main displacement components are nearly consistent with the displacement components; temperature and water level are used as variables and are computed by using a regression model. Both numerical simulated and real life dams demonstrate that PSR-ICA is an effective tool for separating deformation components by different causative forces and is therefore beneficial to dam deformation analysis.

A Single-Channel ICA-R Method for Speech Signal Denoising combining EMD and Wavelet

According to the problem of speech signal denoising, we propose a novel method in this paper, which combines empirical mode decomposition (EMD), wavelet threshold denoising and independent component analysis with reference (ICA-R). Because there is only one mixed recording, it is a single-channel independent component analysis (SCICA) problem in fact, which is hard to solve by traditional ICA methods. EMD is exploited to expand the single-channel received signal into several intrinsic mode functions (IMFs) in advance, therefore traditional ICA of multi-dimension becomes applicable. First, the received signal is segmented to reduce the processing delay. Secondly, wavelet thresholding is applied to the noise-dominated IMFs. Finally, fast ICA-R is introduced to extract the object speech component from the processed IMFs, whose reference signal is constructed by assembling the high-order IMFs. The simulations are carried out under different noise levels and the performance of the proposed method is compared with EMD, wavelet thresholding, EMD-wavelet and EMD-ICA approaches. Simulation results indicate that the proposed method exhibit superior denoising performance especially when signal-to-noise ratio is low, with a half shorter running time.

Separation and analyzing of harmonics and inter-harmonics based on single channel independent component analysis

Summary The paper proposes a new method to separate and analyze harmonics and inter-harmonics on the basis of single channel blind source separation. Without any prior knowledge, a multichannel signal was acquired through a cycle-spinning method. After detecting and reducing dimension using principal component analysis, Independent Component Analysis is then applied to separate the independent harmonic and inter-harmonic sources. The independent parts are then clustered according to their frequency by using k-means, and mapped back to the observation domain. matlab simulations indicate that, as long as the sources are reasonably spectrally disjointed, the proposed method can effectively separate out the independent harmonics and inter-harmonics; thus, we can analyze them with fast Fourier transform-based method respectively. Simulation results show that the proposed algorithm has better estimation accuracy under high level of noise and is well suited for small-amplitude harmonic or inter-harmonic detection and analyzing. Copyright © 2013 John Wiley & Sons, Ltd.

Antenatal surveillance through estimates of the sources underlying the abdominal phonogram: a preliminary study

Today, it is generally accepted that current methods for biophysical antenatal surveillance do not facilitate a comprehensive and reliable assessment of foetal well-being and that continuing research into alternative methods is necessary to improve antenatal monitoring procedures. In our research, attention has been paid to the abdominal phonogram, a signal that is recorded by positioning an acoustic sensor on the maternal womb and contains valuable information about foetal status, but which is hidden by maternal and environmental sources. To recover such information, previous work has used single-channel independent component analysis (SCICA) on the abdominal phonogram and successfully retrieved estimates of the foetal phonocardiogram, the maternal phonocardiogram, the maternal respirogram and noise. The availability of these estimates made it possible for the current study to focus on their evaluation as sources for antenatal surveillance purposes. To this end, the foetal heart rate (FHR), the foetal heart sounds morphology, the maternal heart rate (MHR) and the maternal breathing rate (MBR) were collected from the estimates retrieved from a dataset of 25 abdominal phonograms. Next, these parameters were compared with reference values to quantify the significance of the physiological information extracted from the estimates. As a result, it has been seen that the instantaneous FHR, the instantaneous MHR and the MBR collected from the estimates consistently followed the trends given by the reference signals, which is a promising outcome for this preliminary study. Thus, as far as this study has gone, it can be said that the independent traces retrieved by SCICA from the abdominal phonogram are likely to become valuable sources of information for well-being surveillance, both foetal and maternal.

Online learning algorithms for principal component analysis applied on single-lead ECGs

This article evaluates several adaptive approaches to solve the principal component analysis (PCA) problem applied on single-lead ECGs. Recent studies have shown that the principal components can indicate morphologically or environmentally induced changes in the ECG signal and can be used to extract other vital information such as respiratory activity. Special interest is focused on the convergence behavior of the selected gradient algorithms, which is a major criterion for the usability of the gained results. As the right choice of learning rates is very data dependant and subject to movement artifacts, a new measurement system was designed, which uses acceleration data to improve the performance of the online algorithms. As the results of PCA seem very promising, we propose to apply a single-channel independent component analysis (SCICA) as a second step, which is investigated in this paper as well.

On the interpretation of the independent components underlying the abdominal phonogram: a study of their physiological relevance

Recorded by positioning a sensitive acoustic sensor over the maternal womb, the abdominal phonogram is a signal that contains valuable information for foetal surveillance (e.g. heart rate), which is hidden by maternal and environmental sources. To recover such information, previous work used single-channel independent component analysis (SCICA) to separate the abdominal phonogram into statistically independent components (ICs) that, once acquired, must be objectively associated with the real sources underlying the abdominal phonogram—either physiological or environmental. This is a typical challenge for blind source separation methodologies and requires further research on the signals of interest to find a suitable solution. Here, we have conducted a joint study on 75 sets of ICs by means of statistical, spectral, complexity and time-structure analysis methods. As a result, valuable and consistent characteristics of the components separated from the abdominal phonogram by SCICA have been revealed: (1) the ICs are spectrally disjoint and sorted according to their frequency content, (2) only the ICs with lower frequency content present strong regular patterns and (3) such regular patterns are driven by well-known physiological processes given by the maternal breathing rate, the maternal heart rate and the foetal heart rate. This information was so promising that it has been used in current work for automatic classification of ICs and recovery of the traces of the physiological sources underlying the abdominal phonogram. Future work will look for the extraction of information useful for surveillance (e.g. heart rate), not only about foetal well-being, but also about maternal condition.

Single channel surface electromyography blind recognition model based on watermarking

A single channel electromyography blind recognition model based on watermarking is proposed in this paper. Single Channel Independent Component Analysis is adopted to avoid complicated circuit connection and the unreliability of hardware and reduce the noise which accompanied with surface Electromyography (sEMG) signals. Embedded watermarking is applied to solve the problem of blind source separation disorder. A self adaptive neural network and some eigenvectors are applied in sEMG features classification. From the classification results, hand gestures can be recognized. In consideration of time-scale synchronization attack, the host sEMG signals are transformed into wavelet domain and the synchronization codes are embedded. The experiment results show that the model proposed in this paper is penetrable against most common signal processing, and can recognize the hand gesture accurately.

Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis

In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis (ICA)]. However, only a few techniques have been reported for analyses of single-channel recordings. Examples are single-channel ICA (SCICA) and wavelet-ICA (WICA), which all have certain limitations. In this paper, we propose a new method for a single-channel signal decomposition. This method combines empirical-mode decomposition with ICA. We compare the separation performance of our algorithm with SCICA and WICA through simulations, and we show that our method outperforms the other two, especially for high noise-to-signal ratios. The performance of the new algorithm was also demonstrated in two real-life applications.