Academic literature on the topic 'Electrocardiogram decomposition'
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Journal articles on the topic "Electrocardiogram decomposition":
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Dissertations / Theses on the topic "Electrocardiogram decomposition":
Schenone, Elisa. "Reduced Order Models, Forward and Inverse Problems in Cardiac Electrophysiology." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066447/document.
This PhD thesis is dedicated to the investigation of the forward and the inverse problem of cardiac electrophysiology. Since the equations that describe the electrical activity of the heart can be very demanding from a computational point of view, a particular attention is paid to the reduced order methods and to their application to the electrophysiology models. First, we introduce the mathematical and numerical models of electrophysiology and we implement them to provide for simulations that are validated against various qualitative and quantitative criteria found in the medical literature. Since our model takes into account atria and ventricles, we are able to reproduce full cycle Electrocardiograms (ECG) in healthy configurations and also in the case of several pathologies. Then, several reduced order methods are investigated for the resolution of the electrophysiology equations. The Proper orthogonal Decomposition (POD) method is applied for the discretization of the electrophysiology equations in several configurations, as for instance the simulation of a myocardial infarction. Also, the method is used in order to solve some parameters identification problems such as the identification of an infarcted zone using the Electrocardiogram measures and for the efficient simulation of restitution curves. To circumvent some limitations of the POD method, a new reduced order method based on the Approximated Lax Pairs (ALP) is investigated. This method is applied to the forward and inverse problems. Finally, a new reduced order algorithm is proposed, based on the ALP and the Discrete Empirical Interpolation methods. This new approach significantly improves the efficiency of the original ALP algorithm and allow us to consider more complex models used in electrophysiology
Marinho, Ramos de Oliveira Pedro. "Modélisation Tensorielle de l'ECG pour l'Analyse de la Fibrillation Atriale Persistante." Thesis, Université Côte d'Azur, 2020. https://tel.archives-ouvertes.fr/tel-03177971.
Atrial Fibrillation (AF) is the most common sustained arrhythmia diagnosed in clinical practice, responsible for high hospitalization and death rates. Furthermore, the electrophysiological mechanisms underlying this cardiac rhythm disorder are not completely understood. A non-invasive and efficient strategy to study this challenging cardiac condition is analyzing the atrial activity (AA) from the Electrocardiogram (ECG). However, the AA during AF is masked by the ventricular activity (VA) in each heartbeat and often presents a very low amplitude, hampering its analysis. Throughout the years, signal processing methods have helped cardiologists in the study of AF by extracting the AA from the ECG. In particular, matrix-based blind source separation (BSS) methods have proven to be ecient AA extraction tools. However, some constraints need to be imposed to guarantee the uniqueness of such matrix factorization techniques that, although mathematically coherent, may lack physiological grounds and hinder results interpretation. In contrast, tensor decompositions can ensure uniqueness under more relaxed constraints. Particularly, the block term decomposition (BTD), recently proposed as a BSS technique, can be unique under some constraints over its matrix factors, easily satisfying in the mathematical and physiological sense. In addition, cardiac sources can be well modeled by specific mathematical functions that, when mapped into the structured matrix factors of BTD, present a link with their rank. Another advantage over matrix-based methods is that the tensor approach is able to extract AA from very short ECG recordings. The present doctoral thesis has its first focus on the investigation of the Hankel-BTD as an AA extraction tool in persistent AF episodes, with validation based on statistical experiments over a population of AF patients and several types of ECG segments. ECG recordings with a short interval between heartbeats and an AA with significantly low amplitude are challenging cases common in this stage of the arrhythmia. Such cases motivate the use of other tensor-based approach to estimate an AA signal with better quality, the Löwner-BTD. Such an approach is presented along a novel optimal strategy to ensure the Löwner structure that is implemented as a variant of a recently proposed robust algorithm for BTD computation. Another contribution is the model of persistent AF ECGs by a coupled Hankel-BTD, which shows some advantages in terms of improved AA extraction and reduced computational cost over its non-coupled counterpart. Further contributions focus on challenges that arise from the problem of AA extraction from AF ECGs, such as detecting the AA source among the other separated sources in real experiments, where the ground truth it's unknown. For this task, several approaches that use machine learning algorithms and neural networks are assessed, providing satisfactory accuracy. Another challenge that is dealt with is the difficulty in measuring the quality of AA estimation. Here, new indices for AA estimation quality from ECG recordings during AF are proposed and assessed. In summary, this PhD thesis provides the first thorough investigation of the application of tensor-based signal processing techniques to the analysis of atrial fibrillation, showing the interest of the tensor approach and its potential in the management and understanding of this challenging cardiac condition
Yang, Yingyu. "Analyse automatique de la fonction cardiaque par intelligence artificielle : approche multimodale pour un dispositif d'échocardiographie portable." Electronic Thesis or Diss., Université Côte d'Azur, 2023. http://www.theses.fr/2023COAZ4107.
According to the 2023 annual report of the World Heart Federation, cardiovascular diseases (CVD) accounted for nearly one third of all global deaths in 2021. Compared to high-income countries, more than 80% of CVD deaths occurred in low and middle-income countries. The inequitable distribution of CVD diagnosis and treatment resources still remains unresolved. In the face of this challenge, affordable point-of-care ultrasound (POCUS) devices demonstrate significant potential to improve the diagnosis of CVDs. Furthermore, by taking advantage of artificial intelligence (AI)-based tools, POCUS enables non-experts to help, thus largely improving the access to care, especially in less-served regions.The objective of this thesis is to develop robust and automatic algorithms to analyse cardiac function for POCUS devices, with a focus on echocardiography (ECHO) and electrocardiogram (ECG). Our first goal is to obtain explainable cardiac features from each single modality respectively. Our second goal is to explore a multi-modal approach by combining ECHO and ECG data.We start by presenting two novel deep learning (DL) frameworks for echocardiography segmentation and motion estimation tasks, respectively. By incorporating shape prior and motion prior into DL models, we demonstrate through extensive experiments that such prior can help improve the accuracy and generalises well on different unseen datasets. Furthermore, we are able to extract left ventricle ejection fraction (LVEF), global longitudinal strain (GLS) and other useful indices for myocardial infarction (MI) detection.Next, we propose an explainable DL model for unsupervised electrocardiogram decomposition. This model can extract interpretable information related to different ECG subwaves without manual annotation. We further apply those parameters to a linear classifier for myocardial infarction detection, which showed good generalisation across different datasets.Finally, we combine data from both modalities together for trustworthy multi-modal classification. Our approach employs decision-level fusion with uncertainty, allowing training with unpaired multi-modal data. We further evaluate the trained model using paired multi-modal data, showcasing the potential of multi-modal MI detection to surpass that from a single modality.Overall, our proposed robust and generalisable algorithms for ECHO and ECG analysis demonstrate significant potential for portable cardiac function analysis. We anticipate that our novel framework could be further validated using real-world portable devices. We envision that such advanced integrative tools may significantly contribute towards better identification of CVD patients
Samad, Sarah. "Contactless detection of cardiopulmonary activity for a person in different scenarios." Thesis, Rennes, INSA, 2017. http://www.theses.fr/2017ISAR0030/document.
Nowadays, contact-less monitoring patient's heartbeat using Doppler radar has attracted considerable interest of researchers, especially when the traditional electrocardiogram (ECG) measurements with fixed electrodes is not practical in some cases like infants at risk or sudden infant syndrome or burn victims. Due to the microwave sensitivity toward tiny movements, radar has been employed as a noninvasive monitoring system of human cardiopulmonary activity. According to Doppler effect, a constant frequency signal reflected off an object having a varying displacement will result in a reflected signal, but with a time varying phase. In our case, the object is the patient's chest; the reflected signal of the person's chest contains information about the heartbeat and respiration. The system is based on a vector network analyzer and 2 horn antennas. The S21 is computed using a vector network analyzer. The phase variation of S21 contains information about cardiopulmonary activity. Processing techniques are used to extract the heartbeat signal from the S21 phase. This thesis presents a comparative study in heartbeat detection, considering different radiated powers and frequencies. The radiated powers used are between 3 and -17 dBm and the operational frequencies used are 2.4, 5.8, 10 and 20 GHz. This helps to make a compromise between the minimum power emitted and the complexity of the measurement system. In addition, a comparative study of several signal processing methods is proposed to extract the best technique for heartbeat measurement and thus to extract its parameters. Processing techniques are based on wavelet transforms and conventional filtering in order to make a comparison between them. The parameter extracted in this thesis is the heartbeat rate HR. Measurements were performed simultaneously with a PC-based electrocardiograph to validate the heartbeat rate measurement. Since the person can move from a room to another inside his home, measurements from the four sides of the person and behind a wall are performed. In addition, a modeling approach based on cardio-respiratory measurement for a person who is walking forward is presented. Furthermore, a comparison between single and two-antenna microwave systems for a non-breathing person is carried out to test the accuracy of the single-antenna system relative to the two antenna microwave system. After that, measurements are performed using one antenna microwave system for a person who breathes normally
Chen, Ying-Hsiang, and 陳穎祥. "Subband Decomposition Methods for Electrocardiogram Beat Discrimination." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/13403671395600740821.
國立中正大學
電機工程所
97
Electrocardiogram (ECG) beat discrimination plays an important role in the clinical diagnosis of heart diseases. Although many ECG beat classification methods have been provided in the literature, there still leave room for improvement in view of different issues. Several significant issues, including recognition rates, noise-resistibility, and feature dimension, are considered in the dissertation for the development of an effective and efficient ECG beat classifier. In Chapter II, the discrete wavelet transformation is employed to decompose the ECG signals into different subband components. Statistical and morphological features are extracted to characterize the ECG signals. A probabilistic neural network (PNN) proceeds to discriminate different pathological heartbeat types. The results demonstrate that it provides a promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all heartbeat types in this study. In Chapter III, higher order statistics is recruited to accompany with the discrete wavelet decomposition to characterize the ECG signals as an attempt to elevate the noise-resistibility of the heartbeat discrimination. A feed-forward back-propagation neural network (FFBNN) is employed as classifier. More than 97.5% discrimination rate is achieved with a more complicated experimental profile in which multiple beat types are selected from each of the records for study. In Chapter IV, four nonlinear feature selection methods including Relief-F, two nonlinear correlation based filters (NCBFs), and symmetrical uncertainty feature-class only (SUFCO), are utilized to reduce the dimension of features mentioned in Chapter III. The results demonstrate that two NCBFs based on both feature-feature and feature-class correlation measures outperform the other methods. As high as 96.34% accuracies can be retained even with only eight features. At last, comparison between the proposed methods in Chapter II with another ECG beat discrimination based on independent component analysis and support vector machine (ICA-SVM) method is demonstrated in Chapter V. The results show that both ECG beat classification methods are insensitive to the stationary artifacts including white Gaussian noise and power line interference. The proposed method is especially tolerant to non-stationary artifacts baseline wander and muscle artifacts when compared to ICA-SVM. More than 90% accuracy can be retained with the proposed method even when the SNR is decreases to 10 dB.
Liu, Fang-Tsen, and 劉芳岑. "Subband Decomposition Methods for two leads Electrocardiogram Beat Discrimination." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/42676652558894815641.
國立中正大學
電機工程所
98
Electrocardiogram (ECG) beat discrimination plays an important role in the clinical diagnosis of heart diseases. Although many ECG beat classification methods have been provided in the literature, there still leave room for improvement in view of different issues. The purpose of this study is to add the second lead to the system and study the influence on the recognition rates and the ability to tolerate noises. The discrete wavelet transformation is employed to decompose the ECG signals into different subband components in the first stage, and higher order statistics is recruited to accompany with the discrete wavelet decomposition to characterize the ECG signals as an attempt to elevate the noise-resistibility of heartbeat discrimination. A feed –forward back-propagation neural network (FFBNN) is employed as classifier. We select multiple beat types form records for study. When compared with the system that uses one the first lead, the second lead enhances the recognition rate from 97.5% to 98.1%. We also study of the ability of the two-lead system in resisting noise of different kinds. More than 97.4% accuracy than be retained with the two-lead system even when the SNR is decreases to 10 dB. The results show that the second lead ECG’s information used in the proposed method does enhance the noise-tolerant.
El, Fiky Ahmed Osama. "Correlation of Respiratory Signals and Electrocardiogram Signals via Empirical Mode Decomposition." Thesis, 2011. http://hdl.handle.net/10754/136671.
林祥瑋. "Noise Filtering of Electrocardiogram Using Empirical Mode Decomposition and Least Square Method." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/hw6ed6.
國立臺灣師範大學
機電科技研究所
97
Electrocardiogram (ECG) has played an important role to diagnose cardiovascular diseases. It often corrupted by interferences introduced by the measurement device. These interferences presented in the signal can lead to the feature of waveforms and frequency bands which can not be recognized and retrieved. These are difficulties for diagnosing symptoms of cardiovascular diseases to clinicians. There are plenty kind of interferences of ECG signals including power line interference, baseline drift and Electromyography, EMG, etc. Thus, the de-noising of ECG is an extremely significant issue. In this paper, a de-noising algorithm based on Empirical Mode Decomposition (EMD) and least square method is proposed to filter the interference of ECG signals. EMD is applied to decompose a signal into a set of oscillatory functions from high frequency to low frequency known as intrinsic mode functions (IMFs) by the sifting process. The interference-free signal is reconstructed by the selected IMFs based on least mean square criterion. Several artificial signals are used as to test the feasibility of the proposed method. Numerical results demonstrate the superiority of the proposed method. This method is also applied to some cases of Arrhythmias from the MIT/BIH Arrhythmias database. Using a set of digital filters’ combination proceed to QRS waves inspections. The simulation results show to conform QRS wave inspections to the symptoms of Arrhythmias and prove the feasibility of the proposed method for processing the ECG signals.
Καραγιάννης, Αλέξανδρος. "Μέθοδοι για ανίχνευση και χαρακτηρισμό βιοσημάτων σε θορυβώδεις χρονοσειρές με βάση το μετασχηματισμό Hilbert-Huang." Thesis, 2010. http://nemertes.lis.upatras.gr/jspui/handle/10889/4533.
This diploma thesis entitled "Methods for Identification and Characterization of Biosignals in Noise corrupted Time Series based on Hilbert-Huang Transform " studies issues concerning biomedical signal analysis. There is a review of the available techniques and methods for biomedical signal analysis pointing at certain characteristics of biomedical time series such as non stationarity, the non linearity of the underlying physical process and the need for the adaptive nature of the analysis method. One method that meets these requirements is considered to be the Empirical Mode Decomposition (EMD) which decomposes a signal into a set of components (IMFs) that a subset of them is believed to have a physical meaning. Application of Hilbert Transform on these IMFs provides the instantaneous frequencies and forms the time-frequency distribution of the signal. Issues studied are related to the statistical significance of the IMFs, denoising of biomedical signals, characteristics extraction and feature selection out of the electrocardiogram as well as the performance of the method. Particularly, the performance of empirical mode decomposition is considered to be a critical parameter especially in the case of implementation on nodes of wireless sensor networks or generally embedded systems due to the limited amount of resources available onboard. Modeling method's performance and demand for resources is a significant task facilitating the optimum resource management and task execution schedule of these systems.
YE, GUANG-CI, and 葉光騏. "Application of Empirical Mode Decomposition Method and Extension Neural Network Type-3 to Disease Diagnosis of Electrocardiograms." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/79yyuz.
國立勤益科技大學
電機工程系
107
This study proposes combining Extension Neural Network type-3 (ENN-3) with Chaos Theory and Empirical Mode Decomposition (EMD) for electrocardiography (ECG) recognition. The ECG signals are measured and extracted by using a developed hardware measuring circuit and LabVIEW human-machine interface, and then the stored ECG signals are decomposed by EMD into high frequency and low frequency. The low frequency signal is screened out by using the largest root-mean-square deviation, and the chaos dynamic error scatter map is formed by using master and slave chaotic systems, so as to obtain the chaos eye coordinates of a specific ECG signal, which are identified by ENN-3. With 50 research subjects, 25 datapoints are derived from actual measurements of a signal acquisition circuit, and the other 25 datapoints are provided by the medical center of MIT-BIH. The analysis results show that the accuracy of this method at ECG identification and classification is as high as 100%. This method is then compared with the traditional back-propagation neural network. In terms of learning times, the traditional back-propagation neural network must learn 10,000 times to reach 99% accuracy, whereas the method proposed in this study can achieve a higher recognition rate only after 1,000 times. The ECG autodiagnosis system designed herein can classify arrhythmia and diagnose diseases effectively, reducing the error rate of manual identification.
Book chapters on the topic "Electrocardiogram decomposition":
Warrick, P. A., and M. Altuve. "Classification of Abdominal Fetal Electrocardiogram Recordings using Karhunen-Loève Decomposition." In IFMBE Proceedings, 1072–75. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_261.
Yang, Yingyu, Marie Rocher, Pamela Moceri, and Maxime Sermesant. "Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection." In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, 221–32. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23443-9_21.
Fujita, Hamido, Vidya K. Sudarshan, Muhammad Adam, Shu Lih Oh, Jen Hong Tan, Yuki Hagiwara, Kuang Chua Chua, Kok Poo Chua, and U. Rajendra Acharya. "Characterization of Cardiovascular Diseases Using Wavelet Packet Decomposition and Nonlinear Measures of Electrocardiogram Signal." In Advances in Artificial Intelligence: From Theory to Practice, 259–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60042-0_30.
Zeng, Kehan, Jun Huang, Zhen Tan, and Mingchui Dong. "White Noise Energy and SNR Estimation Based on Haar Wavelet Decomposition for Heart Sound and Electrocardiogram Signals." In Lecture Notes in Electrical Engineering, 589–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55038-6_92.
Garnaik, Sarmila, Nikhilesh Chandra Rout, and Kabiraj Sethi. "Noise Reduction in Electrocardiogram Signal Using Hybrid Methods of Empirical Mode Decomposition with Wavelet Transform and Non-local Means Algorithm." In Advances in Intelligent Systems and Computing, 639–48. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8055-5_57.
Abdou, Abdoul-Dalibou, Ndeye Fatou Ngom, and Oumar Niang. "Classification and Prediction of Arrhythmias from Electrocardiograms Patterns Based on Empirical Mode Decomposition and Neural Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 174–84. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16042-5_17.
Dliou, Azzedine, Samir Elouaham, Rachid Latif, and Mostafa Laaboubi. "Combination of the CEEM Decomposition with Adaptive Noise and Periodogram Technique for ECG Signals Analysis." In Practical Applications of Electrocardiogram. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.86007.
Yadu, Gitika, Suraj Kumar Nayak, Debasisha Panigrahi, Anilesh Dey, and Kunal Pal. "Wavelet Packet Analysis of ECG signals to Understand the Effect of a Motivating Song on Heart of Indian Male Volunteers." In Expert System Techniques in Biomedical Science Practice, 168–92. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5149-2.ch008.
Bajaj, Nikesh. "Wavelets for EEG Analysis." In Wavelet Theory [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94398.
Pandey, Anukul, Butta Singh, Barjinder Singh Saini, and Neetu Sood. "Nonlinear Complexity Sorting Approach for 2D ECG Data Compression." In Computational Tools and Techniques for Biomedical Signal Processing, 1–21. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0660-7.ch001.
Conference papers on the topic "Electrocardiogram decomposition":
Verma, Ashish, Pratik, and Gayadhar Pradhan. "Electrocardiogram denoising using Wavelet decomposition and EMD domain filtering." In TENCON 2016 - 2016 IEEE Region 10 Conference. IEEE, 2016. http://dx.doi.org/10.1109/tencon.2016.7848414.
Paithane, A. N., and D. S. Bormane. "Electrocardiogram signal analysis using empirical mode decomposition and Hilbert spectrum." In 2015 International Conference on Pervasive Computing (ICPC). IEEE, 2015. http://dx.doi.org/10.1109/pervasive.2015.7087042.
Sharma, L. N. "Denoising pathological multilead electrocardiogram signals using multiscale singular value decomposition." In 2014 12th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2014). IEEE, 2014. http://dx.doi.org/10.1109/ictke.2014.7001525.
Youssef, Sherin M. "Analysis of non-stationary electrocardiogram signals using iterative wavelet decomposition." In 2011 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2011. http://dx.doi.org/10.1109/icma.2011.5985818.
Bouabida, Z., Z. E. Hadj Slimane, and F. Bereksi Reguig. "Detection of QRS complex in electrocardiogram signal by the empirical mode decomposition." In 2011 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA). IEEE, 2011. http://dx.doi.org/10.1109/wosspa.2011.5931472.
Sekar, Prithi, and E. Rajinikanth. "Denoising and extraction of electrocardiogram signal using Ensemble Pragmatic Mode Decomposition (EPMD)." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754229.
Deb, Pratik, Mohammad Nooruddin, and Md Shajahan Badshah. "Detection of Abnormal Electrocardiogram (ECG) Using Wavelet Decomposition and Support Vector Machine (SVM)." In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 2019. http://dx.doi.org/10.1109/icasert.2019.8934588.
Rodriguez, Richmond Roi B., Ruby Jane A. Mapolon, and Rosula S. J. Reyes. "A Non-intrusive Single Channel Abdominal Fetal Electrocardiogram Monitor Using Singular Value Decomposition." In 2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE). IEEE, 2021. http://dx.doi.org/10.1109/icecie52348.2021.9664665.
Lilienthal, Jannis, and Waltenegus Dargie. "Extraction of Motion Artifacts from the Measurements of a Wireless Electrocardiogram using Tensor Decomposition." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011290.
Nguyen, Duc-Hieu, Minh-Tuan Nguyen, and Hai-Chau Le. "An Efficient Electrocardiogram R-peak Detection Exploiting Ensemble Empirical Mode Decomposition and Hilbert Transform." In 2022 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2022. http://dx.doi.org/10.1109/atc55345.2022.9942984.