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Journal articles on the topic 'Electrocardiogram decomposition'

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Published: 7 July 2024
Last updated: 7 July 2024

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1

REDIF, Soydan. "Fetal electrocardiogram estimation using polynomial eigenvalue decomposition." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 2483–97. http://dx.doi.org/10.3906/elk-1401-19.

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2

Sameni, R., C. Jutten, and M. B. Shamsollahi. "Multichannel Electrocardiogram Decomposition Using Periodic Component Analysis." IEEE Transactions on Biomedical Engineering 55, no. 8 (August 2008): 1935–40. http://dx.doi.org/10.1109/tbme.2008.919714.

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3

Malhotra, Vikas, and MandeepKaur Sandhu. "Electrocardiogram signals denoising using improved variational mode decomposition." Journal of Medical Signals & Sensors 11, no. 2 (2021): 100. http://dx.doi.org/10.4103/jmss.jmss_17_20.

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4

Suppappola, Seth, Ying Sun, and Salvatore A. Chiaramida. "Gaussian pulse decomposition: An intuitive model of electrocardiogram waveforms." Annals of Biomedical Engineering 25, no. 2 (March 1997): 252–60. http://dx.doi.org/10.1007/bf02648039.

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5

SUCHETHA, M., and N. KUMARAVEL. "CLASSIFICATION OF ARRHYTHMIA IN ELECTROCARDIOGRAM USING EMD BASED FEATURES AND SUPPORT VECTOR MACHINE WITH MARGIN SAMPLING." International Journal of Computational Intelligence and Applications 12, no. 03 (September 2013): 1350015. http://dx.doi.org/10.1142/s1469026813500156.

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Electrocardiogram (ECG) signals represent a useful information source about the rhythm and the functioning of the heart. Any disturbance in the heart's normal rhythmic contraction is called an arrhythmia. Analysis of Electrocardiogram signals is the most effective available method for diagnosing cardiac arrhythmias. Computer based classification of ECG provides higher accuracy and offer a potential of an affordable cardiac abnormality mass screening. The empirical mode decomposition is performed on various arrhythmia signals and different levels of intrinsic mode functions (IMF) are obtained. Singular value decomposition (SVD) is used to extract features from the IMF and classification is performed using support vector machine. This method is more efficient for classification of ECG signals and at the same time provides good generalization properties.
6

Proskurin, S. G. "Trigeminy electrocardiogram spectral characteristics study." CARDIOMETRY, no. 27 (May 4, 2023): 75–79. http://dx.doi.org/10.18137/cardiometry.2023.27.7679.

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This paper presents the results of a study in which the method of ECG decomposition in the time domain (DMTD) was applied, followed by a spectral analysis. A digital signal with trigeminy of the first lead of a standard electrocardiograph was processed. Using digital filtering in time domain, the electrocardiogram (ECG) was cleared of noise, what results the reduction of spurious components by 10-20%. To represent and classify the frequency characteristics throughout the entire processed cardiac signal, the QRS complexes were removed, P and T waves were left unchanged. Due to considerable influence on the spectral analysis sharp peaks of the ECG signal with small characteristic times of the leading and trailing edges, the obtained result differs considerably from the sum of the harmonic components of the smooth part of the signal. The spectral processing reveals peaks at multiple frequencies, 1.6 Hz, 3.2 Hz, 4.7 Hz, corresponding to a smooth function of P and T waves before the appearance of extra systoles. Based on the obtained data, the frequencies corresponding to the peaks of the cardiogram with a stable sinus rhythm were identified. The acquired data represent regular harmonics, which allow adequate quantitative ECG analysis.
7

Zhang, Xiaohong, Huiling Tong, Yanjun Deng, Mengjiao Lv, and Zhidong Zhao. "Electrocardiogram Human Identification System Based on Block Sparse Bayesian Decomposition." Journal of Medical Imaging and Health Informatics 7, no. 1 (February 1, 2017): 264–72. http://dx.doi.org/10.1166/jmihi.2017.2017.

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8

Jannah, N., S. Hadjiloucas, F. Hwang, and R. K. H. Galvão. "Smart-phone based electrocardiogram wavelet decomposition and neural network classification." Journal of Physics: Conference Series 450 (June 26, 2013): 012019. http://dx.doi.org/10.1088/1742-6596/450/1/012019.

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9

KUMARI, R. SHANTHA SELVA, and V. SADASIVAM. "DE-NOISING AND BASELINE WANDERING REMOVAL OF ELECTROCARDIOGRAM USING DOUBLE DENSITY DISCRETE WAVELET." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 03 (May 2007): 399–415. http://dx.doi.org/10.1142/s0219691307001823.

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In this paper, an off-line double density discrete wavelet transform based de-noising and baseline wandering removal methods are proposed. Different levels decomposition is used depending upon the noise level, so as to give a better result. When the noise level is low, three levels decomposition is used. When the noise level is medium, four levels decomposition is used. When the noise level is high, five levels decomposition is used. Soft threshold technique is applied to each set of wavelet detail coefficients with different noise level. Donoho's estimator is used as a threshold for each set of wavelet detail coefficients. The results are compared with other classical filters and improvement of signal to noise ratio is discussed. Using the proposed method the output signal to noise ratio is 19.7628 dB for an input signal to noise ratio of -7.11 dB. This is much higher than other methods available in the literature. Baseline wandering removal is done by using double density discrete wavelet approximation coefficients of the whole signal. This is an unsupervised method allowing the process to be used in off-line automatic analysis of electrocardiogram. The results are more accurate than other methods with less effort.
10

Padhy, Sibasankar, and Samarendra Dandapat. "Exploiting multi‐lead electrocardiogram correlations using robust third‐order tensor decomposition." Healthcare Technology Letters 2, no. 5 (September 2015): 112–17. http://dx.doi.org/10.1049/htl.2015.0020.

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11

Gupta, Praveen, Kamalesh Kumar Sharma, and Shiv Dutt Joshi. "Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition." Healthcare Technology Letters 2, no. 6 (November 26, 2015): 164–66. http://dx.doi.org/10.1049/htl.2015.0029.

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12

Han, G., B. Lin, and Z. Xu. "Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview." Journal of Instrumentation 12, no. 03 (March 10, 2017): P03010. http://dx.doi.org/10.1088/1748-0221/12/03/p03010.

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13

Xue, Joel. "New morphology features of pediatric long-QT electrocardiogram by signal decomposition." Journal of Electrocardiology 38, no. 4 (October 2005): 38–39. http://dx.doi.org/10.1016/j.jelectrocard.2005.06.053.

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14

Toulni, Youssef, Nsiri Benayad, and Belhoussine Drissi Taoufiq. "Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (December 1, 2021): 960. http://dx.doi.org/10.11591/ijai.v10.i4.pp960-970.

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The electrocardiography allowed us to make a diagnosis of several cardiovascular diseases by representing the electrical activity of the heart over time; this representation is called the electrocardiogram (ECG) signal. In this study we have proposed a model based on the processing of the ECG signal by the wavelet decomposition using discrete wavelet transform (DWT). This decomposition firstly makes it possible to denoise the signal then to extract the statistical features from the approximation coefficients of the denoised signal and finally to classify the data obtained in a support vector machine (SVM) classifier with cross validation for more credibility. After having tested this model with different mother wavelets at different scales, the accuracies at the fourth scale are high and the best accuracy obtained is 87.50%.
15

Tian, Xiaoying, Yongshuai Li, Huan Zhou, Xiang Li, Lisha Chen, and Xuming Zhang. "Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means." Sensors 16, no. 10 (September 25, 2016): 1584. http://dx.doi.org/10.3390/s16101584.

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16

Barbosa, P. R. B., J. Barbosa-Filho, C. A. M. de Sa, E. C. Barbosa, and J. Nadal. "Reduction of electromyographic noise in the signal-averaged electrocardiogram by spectral decomposition." IEEE Transactions on Biomedical Engineering 50, no. 1 (January 2003): 114–17. http://dx.doi.org/10.1109/tbme.2002.805465.

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17

Yao, Wen Po, Jun Chang Zhao, Zheng Zhong Zheng, Tie Bing Liu, Hong Xing Liu, and Jun Wang. "Fetal Electrocardiogram Extraction Based on Modified Robust Independent Component Analysis." Advanced Materials Research 749 (August 2013): 250–53. http://dx.doi.org/10.4028/www.scientific.net/amr.749.250.

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Fetal electrocardiogram (FECG) separation gets widely attention due to its clinical significance. In the paper, we proposed an improved robust independent component analysis for fetal ECG separation. Firstly, wavelet decomposition was applied to fetal ECG to get the relevant parameters. Then, the RobustICA was used to separate the mixed signals. Compared to robust independent component analysis, computing speed of the improved algorithm increased by an average of 15 percent while minimum mean square error fluctuations 0.0008, which indicated that this algorithm could be effectively used in clinical fetal ECG monitoring.
18

Li, Guo Jun, Xiao Jie Hao, Hui Zhong, and Xiao Na Zhou. "Separating Nonstationary Powerline Interference from ECG Using Empirical Mode Decomposition." Applied Mechanics and Materials 155-156 (February 2012): 736–40. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.736.

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Powerline interference (PLI) is a significant source of noise in Electrocardiogram (ECG). It often exhibits variations in frequency and amplitude along with relatively lower level than that of ECG signal in battery-operated ECG system, whose separation remains a challenging task. The use of masking signal-aided empirical mode decomposition is presented to deal with this problem in this study. Simulation results show that our method can effectively decompose the time-varying PLI into a single intrinsic mode function (IMF) at various interference levels.
19

Chung, Iau-Quen, Jen-Te Yu, and Wei-Chi Hu. "Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion." Sensors 21, no. 4 (February 8, 2021): 1184. http://dx.doi.org/10.3390/s21041184.

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Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.
20

Achamma T Varghese. "Integrating Morphological Characteristics with Empirical Mode Decomposition for Robust ECG Signal Classification." Communications on Applied Nonlinear Analysis 31, no. 2s (June 1, 2024): 532–45. http://dx.doi.org/10.52783/cana.v31.665.

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Hyperkalemia, a critical concern, is the primary cause of sudden cardiac deaths in patients with chronic kidney disease (CKD). Traditionally, blood tests serve as the gold standard for hyperkalemia detection. Electrocardiogram (ECG) signals offer a non-invasive means to assess cardiac activity and identify hyperkalemia in CKD patients. Hyperkalemia often presents ECG changes such as elevated T-waves, changes in P-wave morphology, prolonged PR intervals, widened QRS complexes, and, in severe instances, the onset of ventricular arrhythmias and sinusoidal waves. This study proposes a method for the classification of ECG signals for hyperkalaemia using a feature set extracted from electrocardiogram (ECG) signals. Our approach integrates morphological attributes, including P-wave amplitude, T-wave amplitude, QRS interval, PR interval, and ST depression, with spectral attributes such as total power, spectral entropy, variance, skewness, and singular values extracted from Intrinsic Mode Functions obtained through empirical mode decomposition., aiming to capture both structural and frequency domain information inherent in ECG signals. Morphological features provide insights into cardiac abnormalities associated with hyperkalemia and spectral features extracted from IMF, offer valuable information regarding the frequency distribution and complexity of ECG signals. The performance of three classifiers—Kernel Naïve Bayes (KNB), AdaBoost Ensemble Classifier, and Artificial Neural Networks (ANN) is assessed using the extracted features. Among these classifiers, AdaBoost Ensemble Classifier demonstrated the most favorable classification results with sensitivity of 97.7, specificity of 98.84 and accuracy of 98.3%. These findings align with existing state-of-the-art approaches for hyperkalemia classification.
21

Suchetha, M., and N. Kumaravel. "Empirical Mode Decomposition-based Subtraction Techniques for 50 Hz Interference Reduction from Electrocardiogram." IETE Journal of Research 59, no. 1 (2013): 55. http://dx.doi.org/10.4103/0377-2063.110631.

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22

Fernandes, Dylan Royce, and Suchetha M. "FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF EMPIRICAL MODE DECOMPOSITION ALGORITHM FOR ELECTROCARDIOGRAM PROCESSING." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 77. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19569.

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The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. Therefore, good quality ECG signal is required. Hilbert-Huang transform (HHT) is a method to analyze non-stationary and non-linear signals. Empirical mode decomposition (EMD) is the core of HHT. EMD breaks down signals into smaller number of components. These components form a complete and nearly orthogonal basis for the original signal. This algorithm is implemented on field-programmable gate array using the process of extrema generation, envelope generation, and stopping criterion.
23

S, Jerritta, M. Murugappan, Khairunizam Wan, and Sazali Yaacob. "Electrocardiogram-based emotion recognition system using empirical mode decomposition and discrete Fourier transform." Expert Systems 31, no. 2 (March 13, 2013): 110–20. http://dx.doi.org/10.1111/exsy.12014.

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24

Centeno-Bautista, Manuel A., Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Juan P. Amezquita-Sanchez, David Granados-Lieberman, and Martin Valtierra-Rodriguez. "Electrocardiogram Analysis by Means of Empirical Mode Decomposition-Based Methods and Convolutional Neural Networks for Sudden Cardiac Death Detection." Applied Sciences 13, no. 6 (March 10, 2023): 3569. http://dx.doi.org/10.3390/app13063569.

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Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart’s electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal into its intrinsic mode functions (IMFs) and a convolutional neural network (CNN) for automatic diagnosis is proposed. Results for the ensemble empirical mode decomposition (EEMD) method and the empirical mode decomposition (EMD) method are also compared. Results demonstrate that the combination of the CEEMD and the CNN is a potential solution for SCD prediction since 97.5% of accuracy is achieved up to 30 min in advance of the SCD event.
25

Thakran, Snekha. "A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6773–82. http://dx.doi.org/10.3233/jifs-191518.

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The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed into a true intrinsic mode function (IMFs) with the help of EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter).
26

Mohguen, W., and S. Bouguezel. "Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition." Engineering, Technology & Applied Science Research 11, no. 5 (October 12, 2021): 7536–41. http://dx.doi.org/10.48084/etasr.4302.

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In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.
27

Hao, Jingyu, Yuyao Yang, Zhuhuang Zhou, and Shuicai Wu. "Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition." Sensors 22, no. 10 (May 12, 2022): 3705. http://dx.doi.org/10.3390/s22103705.

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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.
28

Mathur, Mridul Kumar, and Gyan Prakash Bissa. "Combined Principal Component Analysis and Compression of Lead Electrocardiogram signal using Singular Value Decomposition." International Journal of Computer Trends and Technology 45, no. 1 (March 25, 2017): 4–9. http://dx.doi.org/10.14445/22312803/ijctt-v45p102.

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29

Jebaraj, Jenitta, and Rajeswari Arumugam. "Ensemble empirical mode decomposition‐based optimised power line interference removal algorithm for electrocardiogram signal." IET Signal Processing 10, no. 6 (August 2016): 583–91. http://dx.doi.org/10.1049/iet-spr.2015.0292.

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30

Yi, G. "Predictive value of wavelet decomposition of the signal-averaged electrocardiogram in idiopathic dilated cardiomyopathy." European Heart Journal 21, no. 12 (June 15, 2000): 1015–22. http://dx.doi.org/10.1053/euhj.1999.2009.

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31

Banerjee, Soumyendu, and Girish Kumar Singh. "Quality Aware Compression of Multilead Electrocardiogram Signal using 2-mode Tucker Decomposition and Steganography." Biomedical Signal Processing and Control 64 (February 2021): 102230. http://dx.doi.org/10.1016/j.bspc.2020.102230.

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32

Yu, Wei, Qiang Han, Jing Jing Ma, and Pei Xie. "A New Method for Biomedical Signal Processing with EMD and ICA Approach." Advanced Materials Research 546-547 (July 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.548.

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Faint signal extraction is always a difficult issue in biomedical signal processing field, because the desired signal is often submerged in several relatively large signals or noises. A novel faint signal processing method based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is developed to enhance the sensitivity and reliability of faint signal detection. This novel method includes two major steps, which is, firstly the decomposition of the biomedical composite signal using EMD, then the classification or extraction of the desired faint signal component through ICA. This paper explored the working principles and the performance of this novel signal processing method under the specific biomedical environment of fetal electrocardiogram extraction (FECG). The experimental results show that the proposed method has better extraction effect and quality compared with traditional ICA methods.
33

Rao, Dr B. Rama. "EMD AND WAVELET-BASED ECG SIGNAL DENOISING AND QRS COMPLEX DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 11, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30586.

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This project presents a novel method for noise removal and QRS complex detection in electrocardiogram (ECG) signals, employing Empirical Mode Decomposition (EMD), windowing, and wavelet thresholding techniques. Noise is added to the ECG signal, and EMD is applied to obtain denoised components. Local minima detection and peak matching identify QRS complex boundaries. Windowing isolates the QRS regions, followed by wavelet thresholding for further denoising. Evaluation metrics like improved Signal-to-Noise Ratio, Mean Square Error, and Percent Root Mean Square Difference are calculated at different input SNR values, demonstrating the method's effectiveness in preserving the QRS complex while removing noise.. Keywords: ECG signal processing, QRS complex detection, Empirical Mode Decomposition (EMD), Windowing, Wavelet thresholding, QRS complex extraction, Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Percent Root Mean Square Difference (PRD).
34

Sarangi, Animesh, Bal Gopal Mishra, and Satyabhama Dash. "Singular Spectrum Analysis Based EMG Artifact Removal from ECG Signal." YMER Digital 21, no. 08 (August 11, 2022): 400–407. http://dx.doi.org/10.37896/ymer21.08/36.

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Electromyogram (EMG) or muscle artifacts frequently affect electrocardiogram (ECG) readings. These artifacts make the required information in the ECG signal difficult to see. In this study, we introduced the singular spectrum analysis (SSA), a powerful subspace-based method for removing EMG artifacts from ECG data. In order to effectively extract the desired component from the tainted ECG data, we presented a new grouping approach and set a threshold. First, a process known as embedding converts a single channel signal into several channels of signals or data. The orthogonal eigenvectors are then calculated using singular value decomposition(SVD) from the multichannel data's covariance matrix. A threshold is selected to locate these eigenvectors, which are utilized to generate the required subspace. After locating the subspace, the multichannel data is simply projected into it, followed by a method called diagonal averaging which will create the original time series and extract the ECG signals. Keywords: Electrocardiogram, EMG artifact, Singular Spectrum Analysis, Embedding, SVD, Mobility.
35

Belle, Ashwin, Rosalyn Hobson Hargraves, and Kayvan Najarian. "An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram." Computational and Mathematical Methods in Medicine 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/528781.

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This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.
36

Liu, Shing-Hong, Cheng-Hsiung Hsieh, Wenxi Chen, and Tan-Hsu Tan. "ECG Noise Cancellation Based on Grey Spectral Noise Estimation." Sensors 19, no. 4 (February 15, 2019): 798. http://dx.doi.org/10.3390/s19040798.

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In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.
37

Ahmad Khorsheed, Eman. "Detection of Abnormal electrocardiograms Based on Various Feature Extraction methods." Academic Journal of Nawroz University 12, no. 3 (July 2, 2023): 111–19. http://dx.doi.org/10.25007/ajnu.v12n3a1818.

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Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart obtained by placing various electrodes on specific areas of the subject's body surface. Abnormalities in a patient's ECG signal may indicate cardiac diseases that require immediate medical attention. As a result, detecting an abnormal ECG is critical for the patient's benefit. This work develops a method for classifying ECG signals as normal or abnormal. In this paper, we propose a method for detecting cardiac arrhythmias in electrocardiograms (ECG). In the first stage, the proposal focuses on various feature extractor methods. The Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and Improved complete ensemble empirical mode decomposition with adaptive noise were the feature extraction techniques evaluated (ICEEMDAN). The PCA method is then used to reduce the number of features. Finally, for classification, the Support Vector Machine (SVM) was used, which was trained using the features extracted in the first stage. The proposed models are tested using datasets from MIT-BIH arrhythmia and PTB Diagnostics. The experimental results show that using 3-PCs with the DWT method produces better results than the other methods, which achieve 98.7% in terms of accuracy.
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Liu, Qingze, Aiping Liu, Xu Zhang, Xiang Chen, Ruobing Qian, and Xun Chen. "Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis." Journal of Healthcare Engineering 2019 (December 31, 2019): 1–13. http://dx.doi.org/10.1155/2019/4159676.

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Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.
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Pradhan, Bikash K., Maciej Jarzębski, Anna Gramza-Michałowska, and Kunal Pal. "Automated Detection of Caffeinated Coffee-Induced Short-Term Effects on ECG Signals Using EMD, DWT, and WPD." Nutrients 14, no. 4 (February 19, 2022): 885. http://dx.doi.org/10.3390/nu14040885.

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The effect of coffee (caffeinated) on electro-cardiac activity is not yet sufficiently researched. In the current study, the occurrence of coffee-induced short-term changes in electrocardiogram (ECG) signals was examined. Further, a machine learning model that can efficiently detect coffee-induced alterations in cardiac activity is proposed. The ECG signals were decomposed using three different joint time–frequency decomposition methods: empirical mode decomposition, discrete wavelet transforms, and wavelet packet decomposition with varying decomposition parameters. Various statistical and entropy-based features were computed from the decomposed coefficients. The statistical significance of these features was computed using Wilcoxon’s signed-rank (WSR) test for significance testing. The results of the WSR tests infer a significant change in many of these parameters after the consumption of coffee (caffeinated). Further, the analysis of the frequency bands of the decomposed coefficients reveals that most of the significant change was localized in the lower frequency band (<22.5 Hz). Herein, the performance of nine machine learning models is compared and a gradient-boosted tree classifier is proposed as the best model. The results suggest that the gradient-boosted tree (GBT) model that was developed using a db2 mother wavelet at level 2 decomposition shows the highest mean classification accuracy of 78%. The outcome of the current study will open up new possibilities in detecting the effects of drugs, various food products, and alcohol on cardiac functionality.
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Guaragnella, Cataldo, Maria Rizzi, and Agostino Giorgio. "Marginal Component Analysis of ECG Signals for Beat-to-Beat Detection of Ventricular Late Potentials." Electronics 8, no. 9 (September 6, 2019): 1000. http://dx.doi.org/10.3390/electronics8091000.

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Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection.
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Ziani, Said, Mohamed El Ghmary, and Achmad Rizal. "Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 17 (December 15, 2023): 170–77. http://dx.doi.org/10.3991/ijoe.v19i17.42141.

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This research article presents a novel methodology for effectively extracting the fetal electrocardiogram (FECG) from a single-channel signal acquired on the maternal abdomen. The signal comprises a mixture of the FECG, maternal electrocardiogram (MECG), and ambient noise. The central concept involves projecting the signal into higher-dimensional spaces and leveraging the assumption of statistical independence among the constituent components to achieve their separation from the mixture. To accomplish this, singular value decomposition (SVD) is initially applied to the spectrogram, followed by an iterative application of independent component analysis (ICA) on the principal components. The SVD technique contributes to the enhanced separability of each individual component, while ICA facilitates the promotion of statistical independence between the fetal and maternal ECGs. Furthermore, we refine and customize the aforementioned approach specifically for ECG signals by incorporating knowledge of the frequency distribution of the MECG and other inherent ECG characteristics. The effectiveness of the proposed methodology is validated through comprehensive experimental studies, demonstrating its superior accuracy and performance compared to existing techniques.
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Banerjee, Soumyendu, and Girish Kumar Singh. "Agent-based beat-by-beat compression of 12-lead electrocardiogram signal using adaptive Fourier decomposition." Biomedical Signal Processing and Control 75 (May 2022): 103628. http://dx.doi.org/10.1016/j.bspc.2022.103628.

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Manikandan, Suchetha, and Kumaravel Natesan. "A Novel Approach for the Reduction of 50Hz Noise in Electrocardiogram Using Variational Mode Decomposition." Current Signal Transduction Therapy 12, no. 1 (May 8, 2017): 39–48. http://dx.doi.org/10.2174/1574362412666170307092351.

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Saini, Indu, Dilbag Singh, and Arun Khosla. "Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine." Computers & Electrical Engineering 40, no. 5 (July 2014): 1774–87. http://dx.doi.org/10.1016/j.compeleceng.2014.04.004.

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Mandala, Satria, Annisa Rizki Pratiwi Wibowo, Adiwijaya, Suyanto, Mohd Soperi Mohd Zahid, and Ardian Rizal. "The Effects of Daubechies Wavelet Basis Function (DWBF) and Decomposition Level on the Performance of Artificial Intelligence-Based Atrial Fibrillation (AF) Detection Based on Electrocardiogram (ECG) Signals." Applied Sciences 13, no. 5 (February 27, 2023): 3036. http://dx.doi.org/10.3390/app13053036.

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This research studies the effects of both Daubechies wavelet basis function (DWBF) and decomposition level (DL) on the performance of detecting atrial fibrillation (AF) based on electrocardiograms (ECGs). ECG signals (consisting of 23 AF data and 18 normal data from MIT-BIH) were decomposed at various levels using several types of DWBF to obtain four wavelet coefficient features (WCFs), namely, minimum (min), maximum (max), mean, and standard deviation (stdev). These features were then classified to detect the presence of AF using a support vector machine (SVM) classifier. Distribution of training and testing data for the SVM uses the 5-fold cross-validation (CV) principle to produce optimum detection performance. In this study, AF detection performance is measured and analyzed based on accuracy, sensitivity, and specificity metrics. The results of the analysis show that accuracy tends to decrease with increases in the decomposition level. In addition, it becomes stable in various types of DWBF. For both sensitivity and specificity, the results of the analysis show that increasing the decomposition level also causes a decrease in both sensitivity and specificity. However, unlike the accuracy, changing the DWBF type causes both two metrics to fluctuate over a wider range. The statistical results also indicate that the highest AF accuracy detection (i.e., 94.17%) is obtained at the Daubechies 2 (DB2) function with a decomposition level of 4, whereas the highest sensitivity, 97.57%, occurs when the AF detection uses DB6 with a decomposition level of 2. Finally, DB2 with decomposition level 4 results in 96.750% for specificity. The finding of this study is that selecting the appropriate DL has a more significant effect than DWBF on AF detection using WCF.
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PATIL, G. M., K. SUBBA RAO, U. C. NIRANJAN, and K. SATYANARAYAN. "EVALUATION OF QRS COMPLEX BASED ON DWT COEFFICIENTS ANALYSIS USING DAUBECHIES WAVELETS FOR DETECTION OF MYOCARDIAL ISCHAEMIA." Journal of Mechanics in Medicine and Biology 10, no. 02 (June 2010): 273–90. http://dx.doi.org/10.1142/s0219519410003356.

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This paper presents a new approach in the field of electrocardiogram (ECG) feature extraction system based on the discrete wavelet transform (DWT) coefficients using Daubechies Wavelets. Real ECG signals recorded in lead II configuration are chosen for processing. The ECG signal was acquired by a battery operated, portable ECG data acquisition and signal processing module. In the second step the ECG signal was denoised using soft thresholding with Symlet4 wavelet. Further denoising was achieved by removing the corresponding wavelet coefficients at higher levels of decomposition. Later the ECG data files were converted to .txt files and subsequently to. mat files before being imported into the Matlab 7.4.0 environment for the computation of the decomposition coefficients. The QRS complexes were grouped as normal or myocardial ischaemic ones based on these decomposition coefficients. The algorithm developed by us was evaluated with control database comprising 120 records and validated using 60 records making up test database. By using the DWT coefficients, we have successfully achieved the myocardial ischaemia detection rates up to 97.5% with the technique developed by us for control data and up to 100% for validation test data.
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Oh, Seungrok, and Young-Seok Choi. "Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals." Electronics 13, no. 4 (February 8, 2024): 695. http://dx.doi.org/10.3390/electronics13040695.

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Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns in ECG signals. Here, we present an efficient method to detect abnormal electrocardiogram (ECG) signals associated with VF by measuring orthogonality between intrinsic mode functions (IMFs) derived from a data-driven decomposition method, namely, ensemble empirical mode decomposition (EEMD). The proposed method incorporates the decomposition of the ECG signal into its IMFs using EEMD, followed by the computation of the angles between subsequent IMFs, especially low-order IMFs, as the features to discriminate normal and abnormal ECG patterns. The proposed method was validated through experiments using a public MIT-BIH ECG dataset for its effectiveness in detecting VF ECG signals compared to conventional methods. The proposed method achieves a sensitivity of 99.22%, a specificity of 99.37%, and an accuracy of 99.28% with a 3 s ECG window and a support vector machine (SVM) with a linear kernel, which performs better than existing VF detection methods. The capability of the proposed method can provide a perspective approach for the real-time and practical computer-aided diagnosis of VF.
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Schanze, Thomas. "On the use of singular value decomposition for QRS detection and ECG denoising." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 77–80. http://dx.doi.org/10.1515/cdbme-2022-1021.

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Abstract QRS detection is a pre-processing step to detect the heartbeat in an electrocardiogram (ECG) for subsequent rhythm classification. However, measured ECG waveforms may differ as a result of intrinsic variability or due to artefacts or noise. If the signals are distorted, then this often leads to difficulties in QRS detection. Of course, a high QRS detection performance is an important part of an ECG analysis algorithm, and furthermore, it must work even for highly noisy signals. Singular value decompositon (SVD) is the factorization of a matrix into the product three matrices. SVD allows us to find important components of data and, thus, can be used for dimension reduction or denoising. We introduce SVD based methods for QRS detection and ECG denoising, especially for short unknown signal segments, and show application results.
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Aqil, Mounaim, Atman Jbari, and Abdennasser Bourouhou. "ECG Signal Denoising by Discrete Wavelet Transform." International Journal of Online Engineering (iJOE) 13, no. 09 (September 22, 2017): 51. http://dx.doi.org/10.3991/ijoe.v13i09.7159.

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<p>The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). In the other side the performances of denoising process considerably influence the operations that follow. These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio. This is why the optimal selection of denoising parameters is strongly recommended. The aim of this work is to define the optimal wavelet function to use in DWT decomposition for a specific case of ECG denoising. The choice of the appropriate threshold method giving the best performances is also presented in this work. Finally the criterion of selection of levels in which the DWT decomposition must be performed is carried on this paper. This study is applied on the electromyography (EMG), baseline drift and power line interference (PLI) noises.</p>
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Dosko, S. I., S. A. Sheptunov, V. M. Utencov, A. Yu Spasenov, and K. V. Kucherov. "Modern modal analysis methods of biomedical signals for biomedical-engineering data-measuring systems." Quality. Innovation. Education, no. 4 (2020): 40–52. http://dx.doi.org/10.31145/1999-513x-2020-4-40-52.

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This article considers a review and analysis of modern methods of modal analysis of biomedical signals using for biomedical-engineering data measuring systems. The results of the combined use of parametric and empirical methods to eliminate noise distortions arising from the registration of signals and to identify informative components of biomedical signals of various physical natures with the aim of analyzing their structure are presented. The results of visualization of the dynamics of changes in the modal parameters of the cardiocycles of the electrocardiogram and seismocardiogram are considered. The use of modal decomposition to evaluate regulatory processes in the analysis of heart rate variability is demonstrated.

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