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Cardiac signals are often corrupted by artefacts like power line interference (PLI) which may mislead the cardiologists to correctly diagnose the critical cardiac diseases. The cardiac signals like high resolution electrocardiogram (HRECG), ultra-high frequency ECG (UHF-ECG) and intracardiac electrograms are the specialized techniques in which higher frequency component of interest up to 1 KHz are observed. Therefore, a state space recursive least square (SSRLS) adaptive algorithm is applied for the removal of PLI and its harmonics. The SSRLS algorithm is an effective approach which extracts the desired cardiac signals from the observed signal without any need of reference signal. However, SSRLS is inherited computational heavy algorithm; therefore, filtration of increased number of PLI harmonics bestow an adverse impact on the execution time of the algorithm. In this paper, a parallel distributed SSRLS (PD-SSRLS) algorithm is introduced which runs the computationally expensive SSRLS adaptive algorithm parallely. The proposed architecture efficiently removes the PLI along with its harmonics even the time alignment among the contributing nodes is not the same. Furthermore, the proposed PD-SSRLS scheme provides less computational cost as compared to sequentially operated SSRLS algorithm. A comparison has been drawn between the proposed PD-SSRLS algorithm and sequentially operated SSRLS algorithm in term of qualitative and quantitative performances. The simulation results show that the proposed PD-SSRLS architecture provides almost same qualitative and quantitative performances than that of sequentially operated SSRLS algorithm with less computational cost. [ABSTRACT FROM AUTHOR]

Motion artefacts in electrocardiographic (ECG) signal are suppressed mainly by adaptive noise cancellation and wavelet denoising. While the former requires a motion sensor in addition to ECG electrodes, the latter removes some of the desired low-frequency components in the signal. In this paper spectral trimming technique is being introduced for suppressing the motion artefacts in stress electrocardiography. In this method, Fourier spectral coefficients up to 1.221 Hz of noisy signal are trimmed on the basis of template derived from resting ECG signal in the same subject. The proposed spectral trimming technique has yielded the lowest value of mean ± standard deviation for root mean square error (18.92 ± 8.71) and highest value of the signal to noise ratio (6.439 ± 4.266) as compared to other three methods, namely adaptive noise cancellation, wavelet decomposition and adaptive line enhancement with compatible value of correlation coefficient. Subsequently, spectral trimming technique has been implemented in real-time (deferred by 8.2 s) application for stress electrocardiography. Spectral trimming technique thus offers a method of choice for motion artefact suppression in offline as well as deferred online applications. This method takes care of the limitations of conventional methods such as adaptive noise cancellation or wavelet denoising for suppressing motion artefacts in stress electrocardiography. [ABSTRACT FROM AUTHOR]

In this paper, a new optimal variable step size-adaptive regularized-affine projection algorithm (OVSS-AR-APA) is proposed and compared with existing variable step size APA family of algorithms. Instead of having a constant regularization parameter, the modified sigmoid function variation is used to choose the optimum regularization parameter in the proposed algorithm. Here, the algorithm dynamically adjusts the regularization parameter according to input noise variations. Also, the exponentially weighted value of the error with variable regularization parameter is used for making the step size parameter variable. The existing and proposed algorithms are implemented for noise cancellation from audio signals at various input SNR levels for different filter orders. It is observed from simulations that the proposed algorithm outperforms the existing algorithms in terms of SNR improvements, MSE and convergence rate at all input SNR levels for different filter orders with moderate computational complexity. The OVSS-AR-APA algorithm shows a maximum of 21.54 dB of improvement in SNR at -20dB input SNR level at filter order 5. [ABSTRACT FROM AUTHOR]

Signal de-noising has been sparked and given a great attention by signal processing community since its applications are found in a diverse range of digital signal processing and computer vision problems. To improve signal quality, phase, frequency and power are the important features that should be preserved during the de-noising process. Adaptive filters have been widely used for this purpose due to their ability to cancel out noise signal from the corrupted one precisely. This paper presents a robust adaptive estimator for solving the problem of signal noise cancellation, based on a new adaptive algorithm derived from a new constrained optimization. Simulation results evaluated using MATLAB show that the proposed algorithm is appropriate for several forms of signals contaminated by diverse levels of noise power. The performance of the proposed algorithm is illustrated to be preferable in terms of the power signal to noise ratio, mean square error and time of speed convergence of filter parameters. It is compared to other conventional approaches such as least mean square and normalized least mean square algorithms with various values of white noise power, variance. It exhibits lower steady-state error and faster convergent time than the other implementations. Finally, an efficient performance is achieved comparable with recursive least square and affine projection algorithms. [ABSTRACT FROM AUTHOR]

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed. [ABSTRACT FROM AUTHOR]

Identifying the signals of defective rolling element bearings of industrial machines in the presence of external vibrations is a difficult task. Therefore, in this article, attempt has been done to improve the defect detection of rolling element bearings in the presence of external vibrations using adaptive noise cancellation (ANC), self-adaptive noise cancellation (SANC), and mathematical multiscale morphology (MMM). Circular defects (diameter = 400 µm), on either of the races of the test bearings, have been artificially created and external random vibrations have been imparted to the defective test bearings using an electromechanical shaker in the experimentation reported herein. The defective bearing signal-to-external noise (vibration) ratio has been significantly enhanced after the application of ANC, SANC, and MMM. This resulted in the clear identification of defect frequencies in the vibration spectrum. It is essential to mention here that in ANC technique the least mean square (LMS) algorithm with and without signum function has been used. However, in MMM technique, triangular structural elements are utilized during the closing operation and bottom-hat transform (BHat). In comparison to the LMS algorithm without signum function, the LMS algorithm with signum function has been proved more effective and efficient in minimizing the error. Authors noticed that the MMM filter is much effective for noise removal. [ABSTRACT FROM AUTHOR]

Abstract: A common method to eliminate unwanted power line interference in neurobiology laboratories where sensitive electronic signals are measured is with a notch filter. However a fixed-frequency notch filter cannot remove all power line noise contamination since inherent frequency and phase variations exist in the contaminating signal. One way to overcome the limitations of a fixed-frequency notch filter is with adaptive noise cancellation. Adaptive noise cancellation is an active approach that uses feedback to create a signal that when summed with the contaminated signal destructively interferes with the noise component leaving only the desired signal. We have implemented an optimized least mean square adaptive noise cancellation algorithm on a low-cost 16MHz, 8-bit microcontroller to adaptively cancel periodic 60 Hz noise. In our implementation, we achieve between 20 and 25dB of cancellation of the fundamental 60Hz noise component. [Copyright &y& Elsevier]

Abstract: Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularisation factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions. [Copyright &y& Elsevier]

The fetal electrocardiogram (fECG) contains important information regarding the health of the fetus. However, the fECG obtained noninvasively from the abdominal surface electrical recordings of a pregnant woman are dominated by strong interference from the maternal electrocardiogram (mECG). In this paper, based on the H ∞ principle, two adaptive algorithms are proposed for the extraction of fECG from the trans-abdominal recordings of pregnant women. The motivation behind the application of H ∞ techniques is the fact that they are robust with respect to model uncertainties and lack of statistical information regarding noise. The proposed algorithms are applied to simulated as well as real multichannel ECG recordings and their performances are compared to that of the well-known least-mean-square (LMS) adaptive algorithm. It is found that the proposed H ∞ based algorithms perform superior to the LMS algorithm in extracting the fECG signal. [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm with high computational efficiency is proposed for the filter adaptation in a feedforward active noise control system. The proposed algorithm Zero Forcing Block Adaptive Filter (ZF-BAF) performs filter adaptation on a block-by-block basis in the frequency domain. Filtering is performed in the time domain on a sample-by-sample basis. Working in the frequency domain permits us to get sub-linear complexity, whereas filtering in the time domain minimizes the latency. Furthermore, computational burden is tunable to meet specific requirements about adaptation speed and processing load. No other parameter tuning according to the working condition is required. Computer simulations, performed in different realistic cases against other high-performing time and frequency-domain algorithms, show that achievable performances are comparable, or even better, with those of the algorithms perfectly tuned for each specific case. Robustness exhibited in the tests suggests that performances are expected to be even better in a wide range of real cases where it is impossible to know a priori how to tune the algorithms. [ABSTRACT FROM AUTHOR]

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm. [ABSTRACT FROM AUTHOR]