Top PDF Nonlinear filtering in ECG Signal Enhancement

Nonlinear filtering in ECG Signal Enhancement

Nonlinear filtering in ECG Signal Enhancement

Kalman filters are based on linear dynamical systems discretised in the time domain. They are modeled on a Markov chain built on linear operators perturbed by Gaussian noise. The state of the system is represented as a vector of real numbers. At each discrete time increment, a linear operator is applied to the state to generate the new state, with some noise mixed in, and optionally some information from the controls on the system if they are known. Then, another linear operator mixed with more noise generates the visible outputs from the hidden state. The Kalman filter may be regarded as analogous to the hidden Markov model, with the key difference that the hidden state variables take values in a continuous space (as opposed to a discrete state space as in the hidden Markov model). Additionally, the hidden Markov model can represent an arbitrary distribution for the next value of the state variables, in contrast to the Gaussian noise model that is used for the Kalman filter. There is a strong duality between the equations of the Kalman Filter and those of the hidden Markov model.
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Remoção da interferência de 60 hz no sinal de ECG usando filtro notch digital  / Removal of 60 Hz interference on the ECG signal using digital notch filter

Remoção da interferência de 60 hz no sinal de ECG usando filtro notch digital / Removal of 60 Hz interference on the ECG signal using digital notch filter

60 Hz AC interference can be a problem in any biopotential measurement situation. The source of such interference is the AC potential of the electrical power supply network that is inevitably present in any clinical situation, either for lighting the environment or as a source of supply for the measuring equipment. Electrical interference at 60 Hz can be difficult to detect visually on signals having non-regular waveforms such as EEG or EMG. Nevertheless, the interference is easily visible when present in signals with well-defined waveforms, such as the ECG (Electrocardiogram) signal. In any case, the power spectrum of the signal shall provide a clear indication of the presence of the network interference as a 60 Hz pulse. The harmonics, if present, appear as additional pulses in integral multiples of the fundamental frequency. In this work, a filtering technique is demonstrated, using a digital Notch filter, which removes the 60 Hz artifact from the ECG signal, increasing the reliability of the clinical diagnosis from its interpretation.
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COMPARISON OF ECG SIGNAL DENOISING ALGORITHMS IN EMD AND WAVELET DOMAINS

COMPARISON OF ECG SIGNAL DENOISING ALGORITHMS IN EMD AND WAVELET DOMAINS

Numerous methods have been reported to denoise ECG signals based on filter banks, principal component analysis (PCA), independent component analysis (ICA), neural networks (NNs), adaptive filtering, empirical mode decomposition (EMD), and wavelet transform [24]-[5]. The filter bank based denoising process smoothes the P and R amplitude of the ECG signal, and it is more sensitive to different levels of noise [22]. By exploiting PCA or ICA or NNs, a statistical model of the ECG signal and noise is first extracted and then, the in-band noise is removed by discarding the dimensions corresponding to the noise [8]-[34]. Although PCA, ICA and NNs based schemes are powerful for in-band noise filtering, the statistical model derived therein is not only fairly arbitrary but also extremely sensitive to small changes in either the signal or the noise unless the basis functions are trained on a global set of ECG beat types. In particular, one of the difficulties with the application of ICA is the determination of the order of the independent components (ICs). Thus for further processing, visual inspection is required, which is undesirable in routine clinical ECG analysis [35]. The limitations of the adaptive filtering based ECG denoising lies in the fact that a reference signal has to be additionally recorded together with the ECG [36].
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Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection

Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection

Hence, the development of novel, non-invasive, accurate and home-bound techniques based on a reduced and simplified set of biometric signals [13] may represent a feasible and accurate alternative method to PSG. In fact, this scenario may enable an affordable and ubiquitous recording of patient data in the comfort of their houses, offering the opportunity for broader and more effective monitoring of the population, using less resources of the public and/or private health systems. However, since the sleep apnea detection is based on a reduced set of signals, the use of signal processing techniques such as filtering, wrapping, feature extraction, and feature selection becomes critical and challenging regarding the design of specialized computerized systems. On the one hand, the wise selection of the most informative and/or rep- resentative features may lead to improve the overall accuracy of the system. On the other hand, methods for feature selection are prone to produce new suggested features which require further evaluation that may lead to additional computational costs.
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Retinal Image Enhancement Using Robust Inverse Diffusion Equation and Self-Similarity Filtering.

Retinal Image Enhancement Using Robust Inverse Diffusion Equation and Self-Similarity Filtering.

Early diagnosis and early treatment is an effective method for the control of diabetic retinop- athy [5, 6]. Fundus imaging by digital fundus camera is a standard diagnostic mode in ophthal- mology, which captures the intensity of light reflected from the retinal surface in three different wavelength ranges [7, 8]. By reason of imaging mechanism and system of fundus retina imaging itself, and the disturbance of various noise in image formation process, one often obtain noisy and blurry retinal image with nonuniform and distorted illumination, which is difficult to inter- pret medically and to process subsequently [7, 9–11]. Thus, it is indispensable to remove noise and disturbances, to improve signal-to-noise rate of image, to adjust image contrast and to enhance vessels and fine details of retinal image data [9, 12, 13]. By above image preprocessing, useful information in retinal image is highlighted, while useless one is weakened or removed, to make the result more suitable to clinical diagnosis and treatment [3, 7, 9, 14–17].
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Periodic Noise Suppression from ECG Signal using  Novel Adaptive Filtering Techniques

Periodic Noise Suppression from ECG Signal using Novel Adaptive Filtering Techniques

Conventionally used ANCs basically require two inputs: primary input and reference input. For an ANC to be feasible, the reference input must be correlated with the noise part of the primary input in order to cancel the noise therein. However, there are cases in which commonly used ANCs are limited in use. For example, if off-line processing of ECG recordings is required, the recordings themselves are the only data available for processing. Moreover, in the case when reference input cannot be well-correlated with the noise part of the primary input, ANC cannot perform well as the case with a well correlated reference input. In such cases, alternative approaches other than an ANC must be pursued. Figure 1 shows the example of conventional model of adaptive noise canceller with reference input.
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The Combined Effect of Filters in ECG Signals for Pre-Processing

The Combined Effect of Filters in ECG Signals for Pre-Processing

Abstract – The ECG signal is abruptly changing and continuous in nature. The heart disease such as paroxysmal of heart, arrhythmia diagnosing, are related with the intelligent health care decision this ECG signal need to be pre-process accurately for further action on it such as extracting the features, wavelet decomposition, distribution of QRS complexes in ECG recordings and related information such as heart rate and RR interval, classification of the signal by using various classifiers etc. Filters plays very important role in analyzing the low frequency components in ECG signal. The biomedical signals are of low frequency, the removal of power line interference and baseline wander is a very important step at the pre-processing stage of ECG. In these paper we deal with the study of Median filtering and FIR (Finite Impulse Response)filtering of ECG signals under noisy condition.
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Iterative detection of multicode DS-CDMA signals with strong nonlinear distortion effects

Iterative detection of multicode DS-CDMA signals with strong nonlinear distortion effects

A simple and promising method for reducing the PMEPR of DS-CDMA signals it to employ a nonlinear clipping operation in the time-domain followed by a linear, frequency-domain filtering operation so as to generate a low-PMEPR version of the DS-CDMA signal, occupying the same bandwidth of the corresponding conventional DS-CDMA signal [8], [9] (similar techniques have also been proposed for reducing the envelope fluctuations of orthogonal frequency division multi- plexing (OFDM) signals [10]). However, the filtering operation produces some envelope fluctuations regrowth, limiting the achievable PMEPR [9]. As with OFDM schemes [11], [12], by repeating the clipping and filtering procedures we can reduce the PMEPR regrowth in multicode DS-CDMA schemes [13]. However, nonlinear distortion levels increase when we repeat the clipping and filtering procedures, leading to performance degradation. This performance degradation can be particularly high when we have different powers assigned to different spreading codes, especially for the spreading codes with lower power [14]. A scenario where this effect might be significant is for multi-resolution broadcasting systems [15], [16], where we transmit simultaneously several parallel data streams with different powers so as to have different error protections. For DS-CDMA systems, this can be achieved by assigning to each resolution a subset of the available spreading codes and a different power to each subset (i.e., the spreading codes with higher power have higher error protection and, therefore, are associated to the basic (lower) resolution).
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Analytical Evaluation of Nonlinear Effects on OFDMA Signals

Analytical Evaluation of Nonlinear Effects on OFDMA Signals

have been proposed to reduce the envelope fluctuations of OFDMA signals namely through suitable pre-processing schemes [9]–[11]. As an alternative, we can employ clipping and filtering techniques, already shown to be effective for conventional OFDM signals [12]–[14], as well as MC-CDMA (Multi-Carrier Code Division Multiple Access) schemes [15]. The performance evaluation of multicarrier schemes with nonlinear transmission (either due to an imperfectly linear amplification or due to suitable signal processing schemes to reduce the envelope fluctuations of the transmitted signals), usually resorts to Monte-Carlo simulations that require a long computation time; heuristic, "semi-analytical", approaches have also been proposed so as to evaluate nonlinear distortion effects (see, e.g., [16]). When the number of subcarriers is high we can take advantage of the Gaussian nature of the transmitted signals to characterize statistically the transmitted signals [12], [15], [17]–[20]. The nonlinear devices considered in [12], [15], [17], [18] can be regarded as bandpass memory- less nonlinearities [21] ([15] considers MC-CDMA signals and [12], [17], [18] consider OFDM signals). [19], [20] consider quantization effects on OFDM signals, i.e., the nonlinear devices can be regarded as I-Q memoryless nonlinearities.
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STATISTICAL CHARACTERISTICS INVESTIGATION OF PREDICTION ERRORS FOR INTERFEROMETRIC SIGNAL IN THE ALGORITHM OF NONLINEAR KALMAN FILTERING

STATISTICAL CHARACTERISTICS INVESTIGATION OF PREDICTION ERRORS FOR INTERFEROMETRIC SIGNAL IN THE ALGORITHM OF NONLINEAR KALMAN FILTERING

Basic peculiarities of nonlinear Kalman filtering algorithm applied to processing of interferometric signals are considered. Analytical estimates determining statistical characteristics of signal values prediction errors were obtained and analysis of errors histograms taking into account variations of different parameters of interferometric signal was carried out. Modeling of the signal prediction procedure with known fixed parameters and variable parameters of signal in the algorithm of nonlinear Kalman filtering was performed. Numerical estimates of prediction errors for interferometric signal values were obtained by formation and analysis of the errors histograms under the influence of additive noise and random variations of amplitude and frequency of interferometric signal. Nonlinear Kalman filter is shown to provide processing of signals with randomly variable parameters, however, it does not take into account directly the linearization error of harmonic function representing interferometric signal that is a filtering error source. The main drawback of the linear prediction consists in non-Gaussian statistics of prediction errors including cases of random deviations of signal amplitude and/or frequency. When implementing stochastic filtering of interferometric signals, it is reasonable to use prediction procedures based on local statistics of a signal and its parameters taken into account.
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Approximate nonlinear filtering for a two-dimensional diffusion with one-dimensional observations in a low noise channel

Approximate nonlinear filtering for a two-dimensional diffusion with one-dimensional observations in a low noise channel

Abstract. The asymptotic behavior of a nonlinear continuous time filtering problem is studied when the variance of the observation noise tends to 0. We suppose that the signal is a two-dimensional process from which only one of the components is noisy and that a one-dimensional function of this signal, depending only on the unnoisy component, is observed in a low noise channel. An approximate filter is considered in order to solve this problem. Under some detectability assumptions, we prove that the filtering error converges to 0, and an upper bound for the convergence rate is given. The efficiency of the approximate filter is compared with the efficiency of the optimal filter, and the order of magnitude of the error between the two filters, as the observation noise vanishes, is obtained.
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Wavelet packet transform based ECG signal filtering implemented in reconfigurable hardware structure

Wavelet packet transform based ECG signal filtering implemented in reconfigurable hardware structure

The extraction of parameters from a noisy electrocardiogram (ECG) remains an important task for the biomedical engineering community. The ECG is the representation of the potential difference between different points on a body surface that originate from the electrical activity of human heart and it’s considered as a nonstationary signal. Time- frequency transforms as Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT) are usually known as very useful in non-stationary signal’s analysis due to their multiresolution capabilities. Wavelet Transform (WT) based filtering is now a common practice for denoising of signals having multiresolution characteristics such as the ECG signal [8], [2], [3].
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An iterative detection technique for DS-CDMA signals with strong nonlinear distortion effects

An iterative detection technique for DS-CDMA signals with strong nonlinear distortion effects

A promising class of nonlinear signal processing schemes for reducing the envelope fluctuations (and, inherently, the PMEPR) of DS-CDMA signals was proposed in [8]. These schemes combine a nonlinear operation in the time-domain, followed by a linear, frequency-domain filtering operation to reduce significantly the PMEPR of the transmitted signals, while maintaining the spectral occupation of the corresponding conventional DS-CDMA signals. However, the performance degradation due to the nonlinear distortion effects on the transmitted signals can be relatively high, especially when a low PMEPR is intended and/or different powers are assigned to different spreading codes.
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Enfarte agudo do miocárdio com supra-ST: avaliação do protocolo de via verde coronária

Enfarte agudo do miocárdio com supra-ST: avaliação do protocolo de via verde coronária

Gustavo Pratas Norte | Mestrado Integrado em Medicina | Universidade da Beira Interior 19 doentes triados com DT foi de 14 minutos (P25-75 6,0-38,0) e dos doentes com EAMCST associado a DT foi de 6,5 minutos (P25–75 3,0-14,0), sendo que apenas 41,2% dos doentes com DT e 48,2% dos doentes com EAMCST associado a DT fizeram o ECG até aos 10 minutos. Num estudo realizado por Diercks, Kirk(8), apenas 30,8% dos doentes que foram admitidos no serviço de urgência com DT e 40,9% dos doentes com EAMCST associado a DT realizaram o ECG em menos de 10 minutos. Estas diferenças indicam um incumprimento dos tempo-alvo para ECG preconizados no protocolo interno de VVC do CHCB, assim como nas guidelines internacionais e nacionais(4-6). Para a melhoria destes indicadores, deve investir-se na triagem da DT para aumentar o número de doentes que realizam ECG e que o fazem num tempo inferior a 10 minutos.
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ECG arrhythmia classification based on optimum-path forest

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate car- diac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph- based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowl- edge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater perfor- mance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.
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Towards the improvement of cloud microphysical retrievals using simultaneous Doppler and polarimetric radar measurements

Towards the improvement of cloud microphysical retrievals using simultaneous Doppler and polarimetric radar measurements

There is a body of literature in atmospheric science that considers cloud microphysical parameterizations as one of the main sources of error in operational climate and weather forecast models (e.g. Cantrell and Heymsfield, 2005; K¨archer and Koop, 2005). Microphysical processes deal with small spatial and temporal scales (smaller than a few cen- timeters and up to an hour) which are not directly resolved in global circulation models (GCMs). Therefore, the param- eterization of microphysical processes at scales resolved in GCMs (down to 100 kilometers and down to a few hours) is based on many assumptions which are currently not opti- mal at providing accurate descriptions of cloud microphysi- cal properties. Parameters characterizing microphysical pro- cesses are cloud particle phases, their main orientations, their habits as well as their size distributions. A better understand- ing of such small scale processes and their characterization at large scales are of great importance in order to improve mi- crophysical parameterizations. This can be achieved from the knowledge we gain from a proper use of cloud observation sensors (Shupe et al., 2008).
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PRONY FILTERING OF SEISMIC DATA: MATHEMATICAL AND PHYSICAL MODELLING

PRONY FILTERING OF SEISMIC DATA: MATHEMATICAL AND PHYSICAL MODELLING

The research of Prony filtering method performed in the simula- tion experiments allowed us to explore its features and clarify the range of problems, where the method can be used effectively. They show that if the real data (observations of the wave field) contains high-frequency components corresponding to seismic objects, then the Prony filtering allows us to identify them quite accurately. In this case the objects allocated in the wave field (for example, reflections from fixed horizon) can have rather good resolution in the temporal and spatial coordinates. During filtering process there can be changes in the shape of the extracted signal leading to its tension as well as to the appearance of additional sharp dis- tortions. At the same time, the methodological techniques devel- oped for determination of the optimal filtering parameters allow us to improve the accuracy of signal components extraction. Com- parison of the results of Prony and band-pass filtering demon- strates significant advantages of the first over the second in the separation and analysis of signal components with different fre- quencies. This provides a good opportunity for the method to de- termine the frequency sensitive effects and localization of regions with different damping/dispersion of seismic energy. The results
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The effect of an aerobic training program on the electrical remodeling of the heart: high-frequency components of the signal-averaged electrocardiogram are predictors of the maximal aerobic power

The effect of an aerobic training program on the electrical remodeling of the heart: high-frequency components of the signal-averaged electrocardiogram are predictors of the maximal aerobic power

The study protocol was in accordance with the principles of the Helsinki Declara- tion and was approved by the Ethics Com- mittee in Clinical Research of the Laranjeiras National Institute of Cardiology, Rio de Ja- neiro, RJ, Brazil, and all subjects provided written informed consent before enrollment. The study population was recruited from January to July 2004 and comprised 36 sub- jects divided into two groups: 18 trained long-distance runners (athletes) and 18 healthy untrained subjects (controls). The sample size used in the present study was calculated from numerical variables as de- scribed by Smith et al. (6) and Raineri et al. (10). From reference data, the sampling pro- cedure was based on the difference of the 40-µV terminal (LAS40, ms) of at least 10 ms, with values of α = 0.05 and ß = 0.1, on the vector magnitude, filtered by a 4-pole bi- directional bandpass Butterworth filter with cut-off frequencies at 40 and 250 Hz, and using XYZ Frank orthogonal leads. The sub- jects participating in the present study were slightly younger but had similar anthropo- metric characteristics and gender distribu- tion as those in the studies used as reference in the sampling procedure (6,10). Inclusion criteria were volunteers over 18 and less than 40 years old, in good mental and physi- cal health, without a previous history of cardiovascular disease or systemic arterial hypertension. Subjects less than 18 and more than 40 years old, with a previous history of diabetes mellitus, thyroid dysfunction or liver disease, alcohol consumers, or tobacco smok- ers, or those currently taking any medicine were excluded.
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J. Braz. Chem. Soc.  vol.26 número12

J. Braz. Chem. Soc. vol.26 número12

multiphoton absorption and ionization, caused by fs-laser exposure, lead to free electron generation and photoreduction reactions. When kHz repetition rate laser is applied, such effects are observed in the absorption spectrum through color centers, defects and other induced electronic states. Then, this preferential light absorption gives rise to the plasmon band after the suitable annealing. On the other hand, metallic nanoparticles readily precipitate when thermal effects associated with high repletion rates are presents, and the SPR band is observed even without heat treatment. An detailed study about the ionic species and clusters of silver induced by femtosecond laser was recently reported. 53
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Braz. J. Chem. Eng.  vol.22 número1

Braz. J. Chem. Eng. vol.22 número1

It is important to highlight that the cut size obtained during operation of the hydrocyclone results not only from the action of the centrifugal field but also from the solid material carried by the downward stream fluid. Therefore, for the purpose of comparison with other hydrocyclones, the cut size due exclusively to centrifugal separation should be defined. This definition corresponds to the so-called reduced cut size ( d ' 50 ), in which the flow-splitting effect (dead flux effect) is discounted (Svarovsky, 1984).

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