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ECG Signals Classification Based on Wavelet Transform and Probabilistic Neural Networks
Iman Moazen,Mohamadreza Ahmadzadeh
Majlesi Journal of Electrical Engineering , 2009, DOI: 10.1234/mjee.v3i3.245
Abstract: In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG) signal classification. The proposed classifier is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Network (PNN). The novelty of this approach is that signal statistics, morphological analysis and DWT of the histogram of signal (density estimation) altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decomposed into sub-classes using DWT. A PNN is used to classify ECG signals using statistical discriminating features which are extracted from ECG and its sub-classes. Experimental results on five classes of ECG signals from MIT-BIH arrhythmia database show that the proposed method learns very fast, low computational complexity, and a very high performance compared to the previous methods.
Noise Cancellation in ECG Signals using Computationally  [PDF]
D V Rama Koti Reddy,Mohammad Zia Ur Rahman,Rafi Ahamed Shaik
Signal Processing : An International Journal , 2009,
Abstract: Several signed LMS based adaptive filters, which are computationally superior having multiplier free weight update loops are proposed for noise cancellation in the ECG signal. The adaptive filters essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: 60Hz power line interference, baseline wander, muscle noise and the motion artifact. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the signed regressor LMS algorithm is superior than conventional LMS algorithm, the performance of signed LMS and sign-sign LMS based realizations are comparable to that of the LMS based filtering techniques in terms of signal to noise ratio and computational complexity.
Classification of ECG Signals Using Extreme Learning Machine  [cached]
S. Karpagachelvi,M. Arthanari,M. Sivakumar
Computer and Information Science , 2010, DOI: 10.5539/cis.v4n1p42
Abstract: An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem the ELM classifier is used which works by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, they are the k-nearest neighbor classifier (kNN) and the radial basis function neural network classifier (RBF), with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared to traditional classifiers.
Using Artificial Neural Networks for ECG Signals Denoising
Zoltán Germán-Salló,Katalin Gy?rgy
Scientific Bulletin of the ''Petru Maior" University of T?rgu Mure? , 2010,
Abstract: The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG) signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1)th sample from n previous samples To train and adjust the network weights, the backpropagation (BP) algorithm was used. In this paper, prediction of ECG signals (as time series) using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated) in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.
An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding  [PDF]
Mohammed Abo-Zahhad,Sabah M. Ahmed,Ahmed Zakaria
Modelling and Simulation in Engineering , 2012, DOI: 10.1155/2012/742786
Abstract: This paper presents an efficient electrocardiogram (ECG) signals compression technique based on QRS detection, estimation, and 2D DWT coefficients thresholding. Firstly, the original ECG signal is preprocessed by detecting QRS complex, then the difference between the preprocessed ECG signal and the estimated QRS-complex waveform is estimated. 2D approaches utilize the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. The error signal is cut and aligned to form a 2-D matrix, then the 2-D matrix is wavelet transformed and the resulting wavelet coefficients are segmented into groups and thresholded. There are two grouping techniques proposed to segment the DWT coefficients. The threshold level of each group of coefficients is calculated based on entropy of coefficients. The resulted thresholded DWT coefficients are coded using the coding technique given in the work by (Abo-Zahhad and Rajoub, 2002). The compression algorithm is tested for 24 different records selected from the MIT-BIH Arrhythmia Database (MIT-BIH Arrhythmia Database). The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and low computational complexity in comparison with other methods. 1. Introduction The electrocardiogram (ECG) is an invaluable tool for diagnosis of heart diseases. ECG signals are usually sampled at 200–500 samples/s with 8–12?bits resolution. Considering long monitoring periods, compression is required to handle such vast amount of data. It can increase the capacity of databases where hundreds of thousands of ECG signals are stored for subsequent monitoring and evaluation. Other applications of ECG compression include transmission via telephone or mobile radio to an ECG center for further processing. In recent years, many schemes have been suggested for compression of ECG data [1–11]. These compression techniques can be broadly classified into three groups: direct data handling techniques [1–3], transformation-based techniques [3–11], and parameterized model-based techniques [9]. Our approach belongs to the second group. In general, transform techniques involve expanding a signal into a weighted sum of basis functions. The coefficients of this sum are properly encoded and stored or transmitted instead of the original data. The best transform is the one which requires the least number of basis functions to represent the input signal for a given mean-square error (MSE). Transform techniques include several wavelet-based compression methods. The good
Subband-Adaptive Shrinkage for Denoising of ECG Signals  [cached]
Poornachandra S,Kumaravel N
EURASIP Journal on Advances in Signal Processing , 2006,
Abstract: This paper describes subband dependent adaptive shrinkage function that generalizes hard and soft shrinkages proposed by Donoho and Johnstone (1994). The proposed new class of shrinkage function has continuous derivative, which has been simulated and tested with normal and abnormal ECG signals with added standard Gaussian noise using MATLAB. The recovered signal is visually pleasant compared with other existing shrinkage functions. The implication of the proposed shrinkage function in denoising and data compression is discussed.
Progress on Fabric Electrodes Used in ECG Signals Monitoring  [PDF]
Zhen Liu, Xiaoxia Liu
Journal of Textile Science and Technology (JTST) , 2015, DOI: 10.4236/jtst.2015.13012
Abstract: Wearable monitoring system is designed for skin stimulation of conductive adhesive, prolonged physiological monitoring and biocompatibility, whose core is fabric electrodes and it can feedback physiological status by analysis of abnormal electrocardiogram (ECG). Fabric electrode is a sensor to collect biological signals based on textile materials including signals acquisition, processing systems and information feedback platform and so on. In this paper, the design methods and classification of medical electrodes would be introduced. It also sorted out the principle of biological electrical signals, the design methods and characteristics of different material and different structure electrodes from the point of dry electrodes and wet electrodes. There are many methods that can be used to prepare fabric electrodes. They are mainly metal plating, conductive polymer coating, magnetron sputtering, gas phase deposition and impregnation. Besides, they select the appropriate substrate, conductive medium and composite way to get light fabric electrodes which have high conductivity, good conformability. From the perspective of biological signal acquisition by fabric electrodes, this paper also sorted out the influence and approaches of biological signals and the way to feedback the physiological condition of human. As a new generation of bio-signal acquisition material, fabric electrode has met the requirements of the development of modern medicine. Fabric electrode is different from traditional conductive materials in the characteristics of comfort, intelligence, convenience, accuracy and so on.
Normalization of complexity measures based on Surrogate Approach and its application to ECG signal analysis
基于替代数据思想的复杂度归一化方法及其在心电信号分析中的应用

CHEN Wen-wei,RUAN Jiong,GU Fan-ji,
陈文伟
,阮炯,顾凡及

生物物理学报 , 2006,
Abstract: Normalization of complexity measures based on surrogate approach overcomes the drawback of sensitivity of complexity measures to window length and sampling rate of signals.In this article,the authors normalized approximate entropy(ApEn) and C0 complexity that were most suitable for complexity analysis of biomedical signals.An application to ECG signals can reflect the difference between some heart diseases effectively.Meanwhile,through the comparison of kinds of complexity measures,it can be found that C0 complexity and approximate entropy(ApEn) have the weakest sensitivity to window length,so it can be used for signal analysis of short length data.
Removal of Embedded Artefacts in ECG Signals by Independent Component Analysi
Akingbade Kayode Francis and Michael O. Kolawole
International Journal of Engineering Research , 2014,
Abstract: Routinely recorded Electrocardiograms (ECGs) are often corrupted by artefacts; these artefacts make the visual interpretation and analysis of the ECG signal difficult. This paper presents a model, dynamic in structure, sufficiently suitable for removing the ECG artefacts caused by embedded objects in the body using independent component analysis technique. By simulation, the model is able to detect and remove extraneous noises in the conductive paths and discern essential nodes of ECG that are useful to clinicians. Our study, also demonstrates that convolutive ICA can be regarded as a useful tool for accurately estimating the effects of embedded object in the patients on ECG signals
Multichannel ECG and Noise Modeling: Application to Maternal and Fetal ECG Signals  [cached]
Reza Sameni,Gari D. Clifford,Christian Jutten,Mohammad B. Shamsollahi
EURASIP Journal on Advances in Signal Processing , 2007, DOI: 10.1155/2007/43407
Abstract: A three-dimensional dynamic model of the electrical activity of the heart is presented. The model is based on the single dipole model of the heart and is later related to the body surface potentials through a linear model which accounts for the temporal movements and rotations of the cardiac dipole, together with a realistic ECG noise model. The proposed model is also generalized to maternal and fetal ECG mixtures recorded from the abdomen of pregnant women in single and multiple pregnancies. The applicability of the model for the evaluation of signal processing algorithms is illustrated using independent component analysis. Considering the difficulties and limitations of recording long-term ECG data, especially from pregnant women, the model described in this paper may serve as an effective means of simulation and analysis of a wide range of ECGs, including adults and fetuses.
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