Joint Improved Fast Independent Component Analysis and Singular Value Decomposition for Fetal Electrocardiogram Extraction.

For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization.

Fetal heart rate estimation using fractional Fourier transform and wavelet analysis

Objective: Noninvasive fetal electrocardiography is emerging as a low-cost and high-accuracy technology for fetal cardiac monitoring. Signal processing techniques have been used over the past fifty years in this domain. The current major challenges of this domain, addressed in this study are (1) fetal electrocardiogram (fECG) extraction from few numbers of maternal abdominal channels in low signal-to-noise ratios; (2) online fECG extraction; (3) automatic and online signal quality assessment and channel selection; and (4) accurate and robust fetal R-peak detection and ECG parameter extraction. Approach: Based on the theory of cyclostationarity, auxiliary maternal ECG channel(s) are synthetically constructed and augmented with the input channels. The augmented data are used to develop a robust multichannel source separation algorithm for online/offline fECG extraction, from as few as a single channel, and an accurate fetal R-peak detector using a two-pass matched filter. Several robust signal quality indexes (SQI) and a voting strategy are also proposed for automatic fetal signal quality assessment. Main results: It is shown that the fECG and the fetal R-peaks can be accurately extracted from standard online available datasets, for which classical source separation methods (requiring many channels) had previously failed. The signal quality indexes fully automate the extraction and channel selection procedure. Finally, the proposed R-peak detector is highly robust to background noise and residual maternal R-peak components. Significance: The proposed methods for fECG extraction, R-peak detection and automatic channel selection are evaluated (visually and numerically), on two online available datasets and compared with recently developed algorithms. The proposed algorithm is statistically shown to outperform the benchmarks in terms of average and standard deviation.

Fetal Electrocardiogram (fECG) extracted from the non invasively recorded abdominal electrocardiogram signal of pregnant women during early weeks of pregnancy is an effective diagnostic tool used by clinicians to regularly evaluate the fetus health status. This abdomen Electrocardiogram (AECG) which carries clinically vital information is a complex mixture of fetal ECG, maternal ECG (mECG) interferences and other noises. In this paper, an ease to use method based on adaptive filtering is proposed to extract high quality fetal heart beat signal and further diagnose any abnormalities. The method uses Kalman filter (KF), Least Mean Square (LMS) algorithm based on adaptive noise cancellation to extract fECG. The extracted fECG is steady state error signal which is further amplified, preprocessed to make it noise free signal and further the fetal heart beat is found by detecting and counting the number of R-R peaks based on threshold value, then the abnormality classified as bradycardia with heart rate 113, tachycardia with 223 beats per minute respectively are detected. This paper provides a complete model to extract fECG, find the heart rate of fECG signal which is used in automatic characterization and classification of normal and abnormal classes.

Objective: The utility of fetal heart rate (FHR) monitoring can only be achieved with an acquisition sampling rate that preserves the underlying physiological information on the millisecond time scale (1000 Hz rather than 4 Hz). For such acquisition, fetal ECG (fECG) is required, rather than the ultrasound to derive FHR. We tested one recently developed algorithm, SAVER, and two widely applied algorithms to extract fECG from a single-channel maternal ECG signal recorded over the xyphoid process rather than the routine abdominal signal. Approach: At 126dG, ECG was attached to near-term ewe and fetal shoulders, manubrium and xyphoid processes (n = 12). fECG served as the ground-truth to which the fetal ECG signal extracted from the simultaneously-acquired maternal ECG was compared. All fetuses were in good health during surgery (pH 7.29 ± 0.03, pO2 33.2 ± 8.4, pCO2 56.0 ± 7.8, O2Sat 78.3 ± 7.6, lactate 2.8 ± 0.6, BE −0.3 ± 2.4). Main result: In all animals, single lead fECG extraction algorithm could not extract fECG from the maternal ECG signal over the xyphoid process with the F1 less than 50%. Significance: The applied fECG extraction algorithms might be unsuitable for the maternal ECG signal over the xyphoid process, or the latter does not contain strong enough fECG signal, although the lead is near the mother’s abdomen. Fetal sheep model is widely used to mimic various fetal conditions, yet ECG recordings in a public data set form are not available to test the predictive ability of fECG and FHR. We are making this data set openly available to other researchers to foster non-invasive fECG acquisition in this animal model.

Analysis of the fetal ECG (fECG) allows physicians to detect changes in the well-being state of the fetus. But when assessing the fECG through the abdominal signals (ADS), its very low amplitude causes a problem, as the fECG representation in the ADS is buried in a mixture of other signals with stronger energy. Different methods have been proposed in the past to extract the transabdominal fECG for instantaneous fetal heart rate (fHR) computation; four representatives of them are selected for an accurate comparison of their performance in fECG extraction and in fHR estimation. A model for the ADS including all the possible disturbances is developed within this study to generate simulated data as they are required for the quantitative comparison of the algorithms. Their performances and limits considering both the enhancement of the fECG and the ability to preserve fECG morphology are analyzed using the simulated data. The results clearly show that linear methods for maternal ECG removal provide better results with respect to the extraction of the fECG morphology. The algorithms are then tested on real ADS data recorded during labor. Finally, the advantage of considering linear methods for ADS processing is discussed.

In this paper, a unified algebraic transformation approach is presented for designing parallel recursive and adaptive digital filters and singular value decomposition (SVD) algorithms. The approach is based on the explorations of some algebraic properties of the target algorithms' representations. Several typical modern digital signal processing examples are presented to illustrate the applications of the technique. They include the cascaded orthogonal recursive digital filter, the Givens rotation-based adaptive inverse QR algorithm for channel equalization, and the QR decomposition-based SVD algorithms. All three examples exhibit similar throughput constraints. There exist long feedback loops in the algorithms' signal flow graph representation, and the critical path is proportional to the size of the problem. Applying the proposed algebraic transformation techniques, parallel architectures are obtained for all three examples. For cascade orthogonal recursive filter, retiming transformation and orthogonal matrix decompositions (or pseudo-commutativity) are applied to obtain parallel filter architectures with critical path of five Givens rotations. For adaptive inverse QR algorithm, the commutativity and associativity of the matrix multiplications are applied to obtain parallel architectures with critical path of either four Givens rotations or three Givens rotations plus two multiply-add operations, whichever turns out to be larger. For SVD algorithms, retiming and associativity of the matrix multiplications are applied to derive parallel architectures with critical path of eight Givens rotations. The critical paths of all parallel architectures are independent of the problem size as compared with being proportional to the problem size in the original sequential algorithms. Parallelism is achieved at the expense of slight increase (or the same for the SVD case) in the algorithms' computational complexity.

Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

The convergence of fast independent component analysis (FastICA) algorithm based on the Newton iterative method was depended on initial value. So the different initial values could result in the different convergence speeds. To deal with this problem, this paper is proposed an improved FastICA algorithm based on symmetric orthogonalization. The algorithm selected initial value randomly, and used serial orthogonalization to get the suitable initial separating matrix firstly. Then it used symmetric orthogonalization to get the separating matrix. Finally, it could get the separated signals. Simulation results show that the proposed algorithm has faster convergence speed than the original and another improved FastICA algorithm with the same signal separation accuracy.

Singular Value Decomposition (SVD) algorithm is a good method in Collaborative Filtering (CF) algorithm. But the traditional SVD method faces the matrix sparse problem, people solve this problem by Latent Factor Model (LMF). The data imbalances problem also troubles us. The algorithm of this paper combines Slope One and SVD algorithm to solve the data imbalances problem. First, we use Slope One algorithm to deal with the users who have few rating records. Then this kind of user will have more rating records. And we can use new data set to train the SVD model to predict the final results. The experiment results show that our algorithm is better than the traditional Slope One and SVD algorithm.

Fetal ECG (FECG) monitoring using abdominal maternal signals is a non-invasive technique that allows early detection of changes in fetal wellbeing. Several other signal components have stronger energy than the FECG, the most important being maternal ECG (MECG) and, especially during labor, uterine EMG. This study proposes a new method to subtract MECG after detecting and removing abdominal signal segments with high-amplitude variations due to uterine contractions. The method removes MECG from abdominal signals using an approximation of the current MECG segment based on a linear combination of previous MECG segments aligned on the R-peak. The coefficients of the linear model are computed so that the squared error of the approximation over the whole current segment is minimized. Abdominal signal segments strongly affected by uterine contractions are detected by applying median filtering. The methods proposed are tested on real abdominal data recorded during labor, with FECG recorded using scalp electrodes synchronously recorded for comparison.

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

A method based on a sparse auto-encoder (SAE) network for the estimation of spreading sequences in long-code direct-sequence code-division multiple access (LC-DS-CDMA) signals is proposed. First, a network classification model based on SAE and softmax classifier is established. Next, the effectiveness of the proposed method is verified by estimating Walsh sequences and m sequences. To estimate the spreading sequences, the LC-DS-CDMA signal is divided into fragments. Then, each user’s spreading sequence is separated by the fast independent component analysis (Fast-ICA) algorithm, and the amplitude fuzziness is eliminated by the delay-and-multiply method. Finally, the spreading sequences are estimated by the SAE model. Experimental results showed that the proposed algorithm could effectively estimate the spreading sequences of LC-DS-CDMA signals. Compared to the existing matching algorithm and Fast-ICA algorithm, the estimation time required by the proposed algorithm was shorter, and its estimation performance at low signal-to-noise ratios was superior.

To solve the problem that vibration signal is easily affected by noise, which leads to the difficulty of fault feature extraction. This paper proposes a method for rolling bearing fault feature extraction which based on Cubic spline interpolation Intrinsic Time-scale Decomposition (CITD) algorithm and Fast Independent Component Analysis (FastICA) algorithm. Firstly, CITD method was used to decompose the original fault vibration signal, and a series of Proper Rotation (PR) components were obtained. Then the components with abundant fault information are selected by kurtosis criterion to reconstruct the observed signal, and the remaining components are reconstructed to obtain the virtual noise channel signal. FastICA algorithm is used to reduce the noise of reconstructed signal. The Teager-Kaiser Energy Operator (TKEO) is used to demodulate the signal after noise reduction. Finally, FFT transform is applied to the demodulated signal, the fault feature information of the original signal is extracted. Through comparative experiments, the results show that the proposed method can extract the fundamental frequency and frequency doubling characteristic information of rolling bearing faults more clearly and effectively.

This paper proposes a new method to extract fetal ECG from multi-channel signals. The fetal ECG is extracted in phase space, which is constructed by a pregnant woman's multiple thoracic and abdominal ECG signals. Based on corresponding points in phase space, we adopt least square algorithm to estimate the maternal components of the abdominal signals and extract the fetal signals by subtracting the estimated maternal signals from the abdominal signals. Compared with traditional separation method based on information fusion of multi-channel signals, such as Independent Component Analysis (ICA), this method could be realized more easily and effectively.