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Journal articles on the topic 'Electrocardiograms modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

DOERSCHUK, PETER C., ROBERT R. TENNEY, and ALAN S. WILLSKY. "Modelling electrocardiograms using interacting Markov chains." International Journal of Systems Science 21, no. 2 (February 1990): 257–83. http://dx.doi.org/10.1080/00207729008910361.

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2

Malík, Marek, and Thomas Cochrane. "A discrete simulation model of electrocardiograms." SIMULATION 45, no. 5 (November 1985): 242–50. http://dx.doi.org/10.1177/003754978504500503.

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3

Hurtado, Daniel E., and Ellen Kuhl. "Computational modelling of electrocardiograms: repolarisation and T-wave polarity in the human heart." Computer Methods in Biomechanics and Biomedical Engineering 17, no. 9 (October 31, 2012): 986–96. http://dx.doi.org/10.1080/10255842.2012.729582.

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4

Lin, C. C., C. M. Chen, I. F. Yang, and T. F. Yang. "Automatic optimum order selection of parametric modelling for the evaluation of abnormal intra-QRS signals in signal-averaged electrocardiograms." Medical & Biological Engineering & Computing 43, no. 2 (April 2005): 218–24. http://dx.doi.org/10.1007/bf02345958.

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5

Arini, Pedro David, Sergio Liberczuk, Javier Gustavo Mendieta, Martín Santa María, and Guillermo Claudio Bertrán. "Electrocardiogram Delineation in a Wistar Rat Experimental Model." Computational and Mathematical Methods in Medicine 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2185378.

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Background and Objectives. The extensive use of electrocardiogram (ECG) recordings during experimental protocols using small rodents requires an automatic delineation technique in the ECG with high performance. It has been shown that the wavelet transform (WT) based ECG delineator is a suitable tool to delineate electrocardiographic waveforms. The aim of this work is to implement and evaluate the ECG waves delineation in Wistar rats applying WT. We also describe the ECG signal of the Wistar rats giving the characteristics of its spectrum among other useful information. Methods. We evaluated a delineator based on WT in a Wistar rat electrocardiograms database which was annotated manually by experienced observers. Results. The delineation showed an “overall performance” such as sensitivity and a positive predictive value of 99.2% and 83.9% for P-wave, 100% and 99.9% for QRS complex, and 100% and 99.8% for T-wave, respectively. We also compared temporal analysis based ECG delineator with the WT based ECG delineator in RR interval, QRS duration, QT interval, and T-wave peak-to-end duration. The results showed that WT outperforms the temporal delineation technique in all parameters analyzed. Conclusions. Finally, we propose a WT based ECG delineator as a methodology to implement in a wide diversity of experimental ECG analyses using Wistar rats.
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OSAKA, MOTOHISA. "A MODIFIED CHUA CIRCUIT SIMULATES A V-SHAPED TROUGH IN AUTONOMIC ACTIVITY AS A PRECURSOR OF SUDDEN CARDIAC DEATH." International Journal of Bifurcation and Chaos 21, no. 09 (September 2011): 2713–22. http://dx.doi.org/10.1142/s0218127411030040.

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Recently we have reported that a previously unidentified V-trough of sympathetic nerve activity (SNA) is a potential precursor of lethal cardiac events by examining 24-hour ambulatory electrocardiograms in which such an event was recorded by chance. The V-trough was marked by three consecutive compartments: a small variation lasting two hours, an abrupt descent lasting 30 min and a sharp ascent for 40 min. We reported that the hemodynamics consisting of heart rate, SNA and blood pressure (BP) is modeled excellently by the modification of a known chaotic electrical circuit, Chua circuit. A V-trough of SNA appears by increasing the resistive element between SNA and BP in the circuit, which corresponds to the impaired regulation of BP by SNA. This finding is consistent with an acknowledged finding that the depressed baroreflex (reflex of BP by SNA) may trigger a lethal arrhythmia.
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KARAMANOS, K., S. NIKOLOPOULOS, K. HIZANIDIS, G. MANIS, A. ALEXANDRIDI, and S. NIKOLAKEAS. "BLOCK ENTROPY ANALYSIS OF HEART RATE VARIABILITY SIGNALS." International Journal of Bifurcation and Chaos 16, no. 07 (July 2006): 2093–101. http://dx.doi.org/10.1142/s0218127406015933.

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In this paper we present a novel approach to the analysis of Heat Rate Variability (HRV) data, by coarse-graining analysis using the estimation of Block Entropies with the technique of lumping. HRV time series are generated from long recordings of Electrocardiograms (ECGs) and are then filtered in order to produce a coarse-grained symbolic dynamics. Block Entropy analysis is applied to these dynamics in order to examine its coarse-grained statistics. Our data set is comprised of two subsets, one of healthy subjects and another of Coronary Artery Disease (CAD) patients. It is found that Entropy analysis provides a quick and efficient tool for the differentiation of these series according to subject category. Healthy subjects provided more complex statistics compared to patients; specifically, the healthy data files provided higher values of block Entropies compared to patient ones. We also compare these results with the Correlation Dimension Estimation in order to establish coherency. We believe that this analysis may provide a useful statistical method towards the better understanding of the human cardiac system.
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Ţarălungă, Dragoş-Daniel, Georgeta-Mihaela Ungureanu, Ilinca Gussi, Rodica Strungaru, and Werner Wolf. "Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics." Computational and Mathematical Methods in Medicine 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/239060.

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Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.
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9

Olier, Ivan, Sandra Ortega-Martorell, Mark Pieroni, and Gregory Y. H. Lip. "How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management." Cardiovascular Research 117, no. 7 (May 12, 2021): 1700–1717. http://dx.doi.org/10.1093/cvr/cvab169.

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Abstract There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
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10

Augustyniak, Piotr. "Time–frequency modelling and discrimination of noise in the electrocardiogram." Physiological Measurement 24, no. 3 (July 2, 2003): 753–67. http://dx.doi.org/10.1088/0967-3334/24/3/311.

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11

Razumov, Artem A., Konstantin S. Ushenin, Ksenia A. Butova, and Olga E. Solovyova. "The study of the influence of heart ventricular wall thickness on pseudo-ECG." Russian Journal of Numerical Analysis and Mathematical Modelling 33, no. 5 (November 27, 2018): 301–13. http://dx.doi.org/10.1515/rnam-2018-0025.

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Abstract Electrocardiogram is a widespread method of diagnosis of heart diseases. Nevertheless, there are still issues related to connection of some physiological features of themyocardium with patterns observed on the electrocardiogram. In ourworkwe studied the effect of ventricular remodelling, i.e., thickening ofwalls of ventricles typical for hypertrophic cardiomyopathy (HCM), on the pseudo-electrocardiogram on the surface of a volume conductor during myocardial activation from different sources. A model of two ventricles of the heart was developed for this purpose allowing us to vary ventricular geometry. The volume conductor surrounding the heart was a cubic homogeneous volume conductor. Simulation of a pseudo-electrocardiogram was performed by using a realistic ionic model of cardiomyocytes of the ventricles of the human heart and the bidomain model of the myocardium [15]. The zone of initial activation in the model was given on a part of the subendocardial surface or at one or two points corresponding to positions of electrodes of most common implantable devices. In the course of the study we revealed an inversion of the T-wave when changing the thickness of the left ventricle wall regardless of changes of properties of cardiomyocytes or myocardium conductivity. A linear dependence between the wall thickness of the left ventricle and peak amplitudes and integrals under QRS complex and T wave of the electrocardiogram was shown. We have qualitatively shown that with a change in the wall thickness of the left ventricle the pseudo-electrocardiogram changes stronger in the case of activation from one point than in activation from two points or activation of the entire subendocardium.
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12

Pei, Liuqing, Xinlai Dai, and Baodong Li. "Chaotic synchronization system and electrocardiogram." Communications in Nonlinear Science and Numerical Simulation 2, no. 1 (January 1997): 17–22. http://dx.doi.org/10.1016/s1007-5704(97)90031-9.

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13

Asma, Tinouna, Ghanai Mouna, Mohammed Assam, and Chafaa Kheireddine. "Efficient Filtering Framework for Electrocardiogram Denoising." International Journal Bioautomation 23, no. 4 (December 2019): 403–20. http://dx.doi.org/10.7546/ijba.2019.23.4.000548.

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14

Gundersen, Kenneth, and Jan Terje Kvaløy. "Modelling the relationship between electrocardiogram characteristics and cardiopulmonary resuscitation quality during cardiac arrest." Journal of Electrocardiology 44, no. 2 (March 2011): e12. http://dx.doi.org/10.1016/j.jelectrocard.2010.12.037.

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15

Corrias, A., X. Jie, L. Romero, M. J. Bishop, M. Bernabeu, E. Pueyo, and B. Rodriguez. "Arrhythmic risk biomarkers for the assessment of drug cardiotoxicity: from experiments to computer simulations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1921 (June 28, 2010): 3001–25. http://dx.doi.org/10.1098/rsta.2010.0083.

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In this paper, we illustrate how advanced computational modelling and simulation can be used to investigate drug-induced effects on cardiac electrophysiology and on specific biomarkers of pro-arrhythmic risk. To do so, we first perform a thorough literature review of proposed arrhythmic risk biomarkers from the ionic to the electrocardiogram levels. The review highlights the variety of proposed biomarkers, the complexity of the mechanisms of drug-induced pro-arrhythmia and the existence of significant animal species differences in drug-induced effects on cardiac electrophysiology. Predicting drug-induced pro-arrhythmic risk solely using experiments is challenging both preclinically and clinically, as attested by the rise in the cost of releasing new compounds to the market. Computational modelling and simulation has significantly contributed to the understanding of cardiac electrophysiology and arrhythmias over the last 40 years. In the second part of this paper, we illustrate how state-of-the-art open source computational modelling and simulation tools can be used to simulate multi-scale effects of drug-induced ion channel block in ventricular electrophysiology at the cellular, tissue and whole ventricular levels for different animal species. We believe that the use of computational modelling and simulation in combination with experimental techniques could be a powerful tool for the assessment of drug safety pharmacology.
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16

Signes, María Teresa, Higinio Mora, and Juan Manuel García. "A computational framework based on behavioural modelling: Application to the matching of electrocardiogram (ECG) recordings." Mathematical and Computer Modelling 54, no. 7-8 (October 2011): 1644–49. http://dx.doi.org/10.1016/j.mcm.2011.01.021.

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17

Pervolaraki, E., S. Hodgson, A. V. Holden, and A. P. Benson. "Towards computational modelling of the human foetal electrocardiogram: normal sinus rhythm and congenital heart block." Europace 16, no. 5 (May 1, 2014): 758–65. http://dx.doi.org/10.1093/europace/eut377.

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18

Assam Ouali, Mohammed, Asma Tinouna, Mouna Ghanai, and Kheireddine Chafaa. "Electrocardiogram Signal Denoising by Hilbert Transform and Synchronous Detection." International Journal Bioautomation 24, no. 4 (December 2020): 323–36. http://dx.doi.org/10.7546/ijba.2020.24.4.000549.

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An efficient method for Electrocardiogram (ECG) signal denoising based on synchronous detection and Hilbert transform techniques is presented. The goal of the method is to decompose a noisy ECG signal into two components classified according to their energy: (1) component with high energy representing the dominant component which is the clean ECG signal, and (2) component with low energy representing the sub-dominant component which is the contaminant noise. The investigated approach is validated through out some experimentations on MIT-BIH ECG database. Experimental results show that random noises can be effectively suppressed from ECG signals.
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19

Dolinsky, Pavol, Imrich Andras, Linus Michaeli, and Jan Saliga. "An ECG signal model based on a parametric description of the characteristic waves." ACTA IMEKO 9, no. 2 (June 30, 2020): 3. http://dx.doi.org/10.21014/acta_imeko.v9i2.760.

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This article introduces a new electrocardiogram (ECG) signal model based on geometric signal properties. Instead of the artificial functions used in common ECG models, the proposed model is based on the modelling of real ECG signals divided into time segments. Each segment has been modelled using simple geometrical forms. The final ECG signal model is represented by the sequence of parameters of the base functions. Parameter variations allow for the generation of different waveforms for each subsequent heartbeat without mixing up the PQRST waves order. Two basic models utilize slightly modified elementary functions, which are computationally simple. A combination of both models allows for the modelling of irregularities in the consecutive heartbeats of the specific ECG waveforms. Respiratory, noise, and powerline interference can be added in order to make the generated ECG signal more realistic. The model parameters are estimated by differential evolution optimization and a comparison between the modelled ECG and the acquired signal. The proposed models are tested by the database included in the LabVIEW Biomedical Toolkit and ECG records in the MIT-BIH arrhythmia database.
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20

ZHAO, YI, JUNFENG SUN, and MICHAEL SMALL. "EVIDENCE CONSISTENT WITH DETERMINISTIC CHAOS IN HUMAN CARDIAC DATA: SURROGATE AND NONLINEAR DYNAMICAL MODELING." International Journal of Bifurcation and Chaos 18, no. 01 (January 2008): 141–60. http://dx.doi.org/10.1142/s0218127408020197.

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Whether or not the human cardiac system is chaotic has long been a subject of interest in the application of nonlinear time series analysis. The surrogate data method, which identifies an observed time series against three common kinds of hypotheses, does not provide sufficient evidence to confirm the existence of deterministic chaotic dynamics in cardiac time series, such as electrocardiogram data and pulse pressure propagation data. Moreover, these methods fail to exclude all but the most trivial hypothesis of linear noise. We present a recently suggested fourth algorithm for testing the hypotheses of a noise driven periodic orbit to decide whether these signals are consistent with deterministic chaos. Of course, we cannot exclude all other alternatives but our test is certainly stronger than the those applied previously. The algorithmic complexity is used as the discriminating statistic of the surrogate data method. We then perform nonlinear modeling for the short-term prediction between ECG and pulse data to provide further evidence that they conform to deterministic processes. We demonstrate the application of these methods to human electrocardiogram recordings and blood pressure propagation in the fingertip of seven healthy subjects. Our results indicate that bounded aperiodic determinism exists in both ECG and pulse time series. The addition of (the inevitable) dynamic noise means that it is not possible to conclude the underlying system is chaotic.
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Masouleh, Marzieh Faridi, Mohammad Ali Afshar Kazemi, Mahmood Alborzi, and Abbas Toloie Eshlaghy. "Identification of electrocardiogram signals using internet of things based on combinatory classification." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 03 (September 2017): 1750035. http://dx.doi.org/10.1142/s1793962317500350.

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Combination of computer sciences and electronics has resulted in one of the most remarkable technologies of the recent years called internet of things, considered as a challenge in electronic health systems for taking care of patients. Internet of things presents a promising paradigm for management of digital identification in the form of service customization. The effect of internet of things on healthcare is still in its preliminary stages and requires a substantial development. Various equipment and services are developed and utilized for health systems by providing different things to establish communication and information provision to users at any conditions or places. In this paper, attempts have been made to detect electrocardiogram (ECG) signal through a wireless simple sensing network of body using internet of things operating based on classification and feature extraction.
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22

Vargas, Regis Nunes, Antônio Cláudio Paschoarelli Veiga, and Raquel Romes Linhares. "Atrial fibrillation detection by DFA and SDCST methods." Model Assisted Statistics and Applications 16, no. 3 (August 27, 2021): 189–96. http://dx.doi.org/10.3233/mas-210532.

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Many cardiac disorders were diagnosed by analyzing an electrocardiogram signal, in particular, atrial fibrillation. We join the SDCST method with the Detrended Fluctuation Analysis (DFA) and the backpropagation net to identify atrial fibrillation in one hundred ECG signals obtained from Physionet Challenge 2017 database. The accuracy of the proposed classifier parameter is 97% for the training set and 95% for the test set.
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Yao, Guoliang, Xiaobo Mao, Nan Li, Huaxing Xu, Xiangyang Xu, Yi Jiao, and Jinhong Ni. "Interpretation of Electrocardiogram Heartbeat by CNN and GRU." Computational and Mathematical Methods in Medicine 2021 (August 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/6534942.

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The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.
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24

Benson, A. P., B. Hayes-Gill, A. V. Holden, and E. Pervolaraki. "Towards computational modelling of the human foetal electrocardiogram: normal sinus rhythm: AV conduction block and re-entrant tachycardia." Journal of Electrocardiology 46, no. 4 (July 2013): e24. http://dx.doi.org/10.1016/j.jelectrocard.2013.05.086.

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25

Kahlmann, Willi, Emanuel Poremba, Danila Potyagaylo, Olaf Dössel, and Axel Loewe. "Modelling of patient-specific Purkinje activation based on measured ECGs." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 171–74. http://dx.doi.org/10.1515/cdbme-2017-0177.

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AbstractThe Purkinje system is part of the fast-conducting ventricular excitation system. The anatomy of the Purkinje system varies from person to person and imposes a unique excitation pattern on the ventricular myocardium, which defines the morphology of the QRS complex of the ECG to a large degree. While it cannot be imaged in-vivo, it plays an important role for personalizing computer simulations of cardiac electrophysiology. Here, we present a new method to automatically model and customize the Purkinje system based on the measured electrocardiogram (ECG) of a patient. A graphbased algorithm was developed to generate Purkinje systems based on the parameters fibre density, minimal distance from the atrium, conduction velocity, and position and timing of excitation sources mimicking the bundle branches. Based on the resulting stimulation profile, the activation times of the ventricles were calculated using the fast marching approach. Predescribed action potentials and a finite element lead field matrix were employed to obtain surface ECG signals. The root mean square error (RMSE) between the simulated and measured QRS complexes of the ECGs was used as cost function to perform optimization of the Purkinje parameters. One complete evaluation from Purkinje tree generation to the simulated ECG could be computed in about 10 seconds on a standard desktop computer. The measured ECG of the patient used to build the anatomical model was matched via parallel simplex optimization with a remaining RMSE of 4.05 mV in about 16 hours. The approach presented here allows to tailor the structure of the Purkinje system through the measured ECG in a patient-specific way. The computationally efficient implementation facilitates global optimization.
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Couderc, Jean-Philippe. "Measurement and regulation of cardiac ventricular repolarization: from the QT interval to repolarization morphology." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1892 (February 27, 2009): 1283–99. http://dx.doi.org/10.1098/rsta.2008.0284.

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Ventricular repolarization (VR) is a crucial step in cardiac electrical activity because it corresponds to a recovery period setting the stage for the next heart contraction. Small perturbations of the VR process can predispose an individual to lethal arrhythmias. In this review, I aim to provide an overview of the methods developed to analyse static and dynamic aspects of the VR process when recorded from a surface electrocardiogram (ECG). The first section describes the list of physiological and clinical factors that can affect the VR. Technical aspects important to consider when digitally processing ECGs are provided as well. Special attention is given to the analysis of the effect of heart rate on the VR and its regulation by the autonomic nervous system. The final section provides the rationale for extending the analysis of the VR from its duration to its morphology. Several modelling techniques and measurement methods will be presented and their role within the arena of cardiac safety will be discussed.
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Utkarsh, Jai, Raju Kumar Pandey, Shrey Kumar Dubey, Shubham Sinha, and S. S. Sahu. "Classification of Atrial Arrhythmias using Neural Networks." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 2 (April 20, 2018): 90. http://dx.doi.org/10.11591/ijai.v7.i2.pp90-94.

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Electrocardiogram (ECG) is an important tool used by clinicians for successful diagnosis and detection of Arrhythmias, like Atrial Fibrillation (AF) and Atrial Flutter (AFL). In this manuscript, an efficient technique of classifying atrial arrhythmias from Normal Sinus Rhythm (NSR) has been presented. Autoregressive Modelling has been used to capture the features of the ECG signal, which are then fed as inputs to the neural network for classification. The standard database available at Physionet Bank repository has been used for training, validation and testing of the model. Exhaustive experimental study has been carried out by extracting ECG samples of duration of 5 seconds, 10 seconds and 20 seconds. It provides an accuracy of 99% and 94.3% on training and test set respectively for 5 sec recordings. In 10 sec and 20 sec samples it shows 100% accuracy. Thus, the proposed method can be used to detect the arrhythmias in a small duration recordings with a fairly high accuracy.
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Ke Wang, Eric, liu Xi, Ruipei Sun, Fan Wang, Leyun Pan, Caixia Cheng, Antonia D. Dimitrakpoulou-Srauss, Nie Zhe, and Yueping Li. "A new deep learning model for assisted diagnosis on electrocardiogram." Mathematical Biosciences and Engineering 16, no. 4 (2019): 2481–91. http://dx.doi.org/10.3934/mbe.2019124.

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29

Ma, Fengying, Jingyao Zhang, Wei Chen, Wei Liang, and Wenjia Yang. "An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model." Discrete Dynamics in Nature and Society 2020 (August 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/3198783.

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Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.
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Bataineh, Ahmad, Wafa Batayneh, Tasneem Harahsheh, Kholoud Hijazi, Ayman Alrayes, Mahakem Olimat, and Asma Bataineh. "Early Detection of Cardiac Diseases from Electrocardiogram Using Artificial Intelligence Techniques." International Review on Modelling and Simulations (IREMOS) 14, no. 2 (April 30, 2021): 128. http://dx.doi.org/10.15866/iremos.v14i2.19869.

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31

Siddiqui, Shahan Yamin, Atifa Athar, Muhammad Adnan Khan, Sagheer Abbas, Yousaf Saeed, Muhammad Farrukh Khan, and Muhammad Hussain. "Modelling, Simulation and Optimization of Diagnosis Cardiovascular Disease Using Computational Intelligence Approaches." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 1005–22. http://dx.doi.org/10.1166/jmihi.2020.2996.

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Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.
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Rodriguez, Blanca. "The 18th FRAME Annual Lecture, October 2019: Human In Silico Trials in Pharmacology." Alternatives to Laboratory Animals 47, no. 5-6 (November 2019): 221–27. http://dx.doi.org/10.1177/0261192919896356.

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Safety and efficacy testing is a crucial part of the drug development process, and several different methods are used to obtain the necessary data (e.g. in vitro testing, animal trials and clinical trials). Our group has been investigating the potential of modelling and simulation as an alternative approach to some of the methods used for testing drugs for cardiac effects. To achieve our goal of developing and promoting novel approaches in drug development, we formed multidisciplinary collaborations that included clinicians, computer scientists and biologists. Our in silico models are based on human data (e.g. magnetic resonance images, electrocardiogram) and on current knowledge of human electrophysiology, thus generating predictions that are directly applicable to humans. Such models are a particularly powerful tool because they encompass different sources of population heterogeneity, which is crucial for drug testing and for assessing how interindividual variability might affect clinical endpoints. Our group has shown that computer modelling can be used to predict the effects of a test drug in a virtual population or in combination with machine learning to predict different phenotypes when a drug is given to a diseased population. Furthermore, our user-friendly drug testing software is freely available and is being adopted by industry in their drug development process. We have been engaging with industry and regulators to show that our models can contribute to the replacement of animals in drug development. Our ambition is to generate models for simulation of different diseases and therapies for investigations from subcellular to whole organ.
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Cai, Pingmei, Guinan Wang, Shiwei Yu, Hongjuan Zhang, Shuxue Ding, and Zikai Wu. "Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system." IET Systems Biology 10, no. 1 (February 1, 2016): 34–40. http://dx.doi.org/10.1049/iet-syb.2015.0002.

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Kasturiwale, Hemant P., and Sujata N. Kale. "BioSignal modelling for prediction of cardiac diseases using intra group selection method." Intelligent Decision Technologies 15, no. 1 (March 24, 2021): 151–60. http://dx.doi.org/10.3233/idt-200058.

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The Autonomous Nervous System (ANS) controls the nervous system and Heart Rate Variability (HRV) can be used as a diagnostic tool to diagnose heart defects. HRV can be classified into linear and nonlinear HRV indices which are used mostly to measure the efficiency of the model. For prediction of cardiac diseases, the selection and extraction features of machine learning model are effective. The available model used till date is based on HRV indices to predict the cardiac diseases accurately. The model could hardly throw light on specifics of indices, selection process and stability of the model. The proposed model is developed considering all facet electrocardiogram amplitude (ECG), frequency components, sampling frequency, extraction methods and acquisition techniques. The machine learning based model and its performance shall be tested using the standard BioSignal method, both on the data available and on the data obtained by the author. This is unique model developed by considering the vast number of mixtures sets and more than four complex cardiac classes. The statistical analysis is performed on a variety of databases such as MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) and Peripheral Pule Analyser using feature compatibility techniques. The classifiers are trained for prediction with approximately 40000 sets of parameters. The proposed model reaches an average accuracy of 97.87 percent and is sensitive and précised. The best features are chosen from the different HRV features that will be used for classification. The present model was checked under all possible subject scenarios, such as the raw database and the non-ECG signal. In this sense, robustness is defined not only by the specificity parameter, but also by other measuring output parameters. Support Vector Machine (SVM), K-nearest Neighbour (KNN), Ensemble Adaboost (EAB) with Random Forest (RF) are tested in a 5% higher precision band and a lower band configuration. The Random Forest has produced better results, and its robustness has been established.
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Yeu, Ratha, Namhui Ra, Seong-A. Lee, and Yunyoung Nam. "Evaluation of Pencil Lead Based Electrodes for Electrocardiogram Monitoring in Hot Spring." Computers, Materials & Continua 66, no. 2 (2021): 1411–25. http://dx.doi.org/10.32604/cmc.2020.013761.

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Brown, Diane, and Connie Chronister. "The Effect of Simulation Learning on Critical Thinking and Self-confidence When Incorporated Into an Electrocardiogram Nursing Course." Clinical Simulation in Nursing 5, no. 1 (January 2009): e45-e52. http://dx.doi.org/10.1016/j.ecns.2008.11.001.

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Mena, Luis J., Vanessa G. Félix, Alberto Ochoa, Rodolfo Ostos, Eduardo González, Javier Aspuru, Pablo Velarde, and Gladys E. Maestre. "Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly." Computational and Mathematical Methods in Medicine 2018 (May 29, 2018): 1–9. http://dx.doi.org/10.1155/2018/9128054.

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Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.
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Wang, Xiashuang, Guanghong Gong, Ni Li, Li Ding, and Yaofei Ma. "Decoding pilot behavior consciousness of EEG, ECG, eye movements via an SVM machine learning model." International Journal of Modeling, Simulation, and Scientific Computing 11, no. 04 (July 2, 2020): 2050028. http://dx.doi.org/10.1142/s1793962320500282.

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To decode the pilot’s behavioral awareness, an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data. Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves. The experiment starts directly from the multimodal physiological characteristics to explore pilots’ behavior. Electroencephalography, electrocardiogram, and eye movement were recorded simultaneously. Extracted multimodal features of ground missions, air missions, and cruise mission were trained to generate support vector machine behavior model based on supervised learning. The results showed that different behaviors affects different multiple rhythm features, which are power spectra of the [Formula: see text] waves of EEG, standard deviation of normal to normal, root mean square of standard deviation and average gaze duration. The different physiological characteristics of the pilots could also be distinguished using an SVM model. Therefore, the multimodal physiological data can contribute to future research on the behavior activities of pilots. The result can be used to design and improve pilot training programs and automation interfaces.
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Lyon, Aurore, Ana Mincholé, Juan Pablo Martínez, Pablo Laguna, and Blanca Rodriguez. "Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances." Journal of The Royal Society Interface 15, no. 138 (January 2018): 20170821. http://dx.doi.org/10.1098/rsif.2017.0821.

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Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
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Montnach, Jérôme, Isabelle Baró, Flavien Charpentier, Michel De Waard, and Gildas Loussouarn. "Modelling sudden cardiac death risks factors in patients with coronavirus disease of 2019: the hydroxychloroquine and azithromycin case." EP Europace 23, no. 7 (May 2, 2021): 1124–36. http://dx.doi.org/10.1093/europace/euab043.

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Abstract Aims Coronavirus disease of 2019 (COVID-19) has rapidly become a worldwide pandemic. Many clinical trials have been initiated to fight the disease. Among those, hydroxychloroquine and azithromycin had initially been suggested to improve clinical outcomes. Despite any demonstrated beneficial effects, they are still in use in some countries but have been reported to prolong the QT interval and induce life-threatening arrhythmia. Since a significant proportion of the world population may be treated with such COVID-19 therapies, evaluation of the arrhythmogenic risk of any candidate drug is needed. Methods and results Using the O'Hara-Rudy computer model of human ventricular wedge, we evaluate the arrhythmogenic potential of clinical factors that can further alter repolarization in COVID-19 patients in addition to hydroxychloroquine (HCQ) and azithromycin (AZM) such as tachycardia, hypokalaemia, and subclinical to mild long QT syndrome. Hydroxychloroquine and AZM drugs have little impact on QT duration and do not induce any substrate prone to arrhythmia in COVID-19 patients with normal cardiac repolarization reserve. Nevertheless, in every tested condition in which this reserve is reduced, the model predicts larger electrocardiogram impairments, as with dofetilide. In subclinical conditions, the model suggests that mexiletine limits the deleterious effects of AZM and HCQ. Conclusion By studying the HCQ and AZM co-administration case, we show that the easy-to-use O'Hara-Rudy model can be applied to assess the QT-prolongation potential of off-label drugs, beyond HCQ and AZM, in different conditions representative of COVID-19 patients and to evaluate the potential impact of additional drug used to limit the arrhythmogenic risk.
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Якимова, Natalya Yakimova, Вокина, and Vera Vokina. "nature behavIoral responses, lIpId profIle and state of cardIovascular system In lead IntoxIcatIon modellIng on the background of hyperlIpIdemIa In albIno rats." Бюллетень Восточно-Сибирского научного центра Сибирского отделения Российской академии медицинских наук 1, no. 5 (December 6, 2016): 138–41. http://dx.doi.org/10.12737/23410.

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The article describes the comparative study of the effect of lead intoxication in healthy animals and in terms of mod-elled hyperlipidemic condition on lipid metabolism, ECG performance, behavior in the test of the extrapolation of deliverance. The study was performed on 40 albino outbred adult male rats. Modeling hyperlipidemic condition was carried out daily by feeding natural unsalted fat at the rate of 8 grams per animal for 16 days. Lead intoxication was created after the atherogenic diet by adding lead acetate into drinking water in a dose of 50mg/kg of body weight for 4weeks. To investigate the lipid metabolism was measured in serum total cholesterol, triglycerides, HDL choles-terol, LDL cholesterol. In the test of extrapolation deliverance 70% of animals with lead intoxication on the lipid load background did not cope with the task versus 40% of albino rats in the group with lead acetate exposure alone. All control animals successfully solved the problem of the deliverance test. The deterioration of the functioning of the car-diovascular system of rats with lead poisoning on the background of atherogenic diet was manifested by elongation of intraventricular conduction intervals on an electrocardiogram as compared with animals with lead intoxication alone. Disorders of lipid metabolism were manifested by increased levels of LDL cholesterol in rats with lead intoxication on the background of hyperlipidemia to 0.86 (0.69–1.14)mmol/l compared with the value of 0.67(0.58–0.79)mmol/l in animals with lead intoxication.
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Li, Yibing, Wei Nie, Fang Ye, and Ao Li. "A Fetal Electrocardiogram Signal Extraction Algorithm Based on the Temporal Structure and the Non-Gaussianity." Computational and Mathematical Methods in Medicine 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9658410.

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Fetal electrocardiogram (FECG) extraction is an important issue in biomedical signal processing. In this paper, we develop an objective function for extraction of FECG. The objective function is based on the non-Gaussianity and the temporal structure of source signals. Maximizing the objective function, we can extract the desired FECG. Combining with the solution vector obtained by maximizing the objective function, we further improve the accuracy of the extracted FECG. In addition, the feasibility of the innovative methods is analyzed by mathematical derivation theoretically and the efficiency of the proposed approaches is illustrated with the computer simulations experimentally.
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Jing, Enbiao, Haiyang Zhang, ZhiGang Li, Yazhi Liu, Zhanlin Ji, and Ivan Ganchev. "ECG Heartbeat Classification Based on an Improved ResNet-18 Model." Computational and Mathematical Methods in Medicine 2021 (April 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/6649970.

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Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.
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Wan, Xiang-kui, Haibo Wu, Fei Qiao, Feng-cong Li, Yan Li, Yue-wen Yan, and Jia-xin Wei. "Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering." Computational and Mathematical Methods in Medicine 2019 (March 3, 2019): 1–7. http://dx.doi.org/10.1155/2019/7196156.

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One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF) algorithms. However, the T waveform distortions introduced by the WT and the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WT to overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinical BW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. The results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG.
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45

Tadros, Rafik, Hanno L. Tan, Sulayman el Mathari, Jan A. Kors, Pieter G. Postema, Najim Lahrouchi, Leander Beekman, et al. "Predicting cardiac electrical response to sodium-channel blockade and Brugada syndrome using polygenic risk scores." European Heart Journal 40, no. 37 (September 3, 2019): 3097–107. http://dx.doi.org/10.1093/eurheartj/ehz435.

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Abstract Aims Sodium-channel blockers (SCBs) are associated with arrhythmia, but variability of cardiac electrical response remains unexplained. We sought to identify predictors of ajmaline-induced PR and QRS changes and Type I Brugada syndrome (BrS) electrocardiogram (ECG). Methods and results In 1368 patients that underwent ajmaline infusion for suspected BrS, we performed measurements of 26 721 ECGs, dose–response mixed modelling and genotyping. We calculated polygenic risk scores (PRS) for PR interval (PRSPR), QRS duration (PRSQRS), and Brugada syndrome (PRSBrS) derived from published genome-wide association studies and used regression analysis to identify predictors of ajmaline dose related PR change (slope) and QRS slope. We derived and validated using bootstrapping a predictive model for ajmaline-induced Type I BrS ECG. Higher PRSPR, baseline PR, and female sex are associated with more pronounced PR slope, while PRSQRS and age are positively associated with QRS slope (P < 0.01 for all). PRSBrS, baseline QRS duration, presence of Type II or III BrS ECG at baseline, and family history of BrS are independently associated with the occurrence of a Type I BrS ECG, with good predictive accuracy (optimism-corrected C-statistic 0.74). Conclusion We show for the first time that genetic factors underlie the variability of cardiac electrical response to SCB. PRSBrS, family history, and a baseline ECG can predict the development of a diagnostic drug-induced Type I BrS ECG with clinically relevant accuracy. These findings could lead to the use of PRS in the diagnosis of BrS and, if confirmed in population studies, to identify patients at risk for toxicity when given SCB.
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Rooijakkers, M. J., S. Song, C. Rabotti, S. G. Oei, J. W. M. Bergmans, E. Cantatore, and M. Mischi. "Influence of Electrode Placement on Signal Quality for Ambulatory Pregnancy Monitoring." Computational and Mathematical Methods in Medicine 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/960980.

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Noninvasive fetal health monitoring during pregnancy has become increasingly important in order to prevent complications, such as fetal hypoxia and preterm labor. With recent advances in signal processing technology using abdominal electrocardiogram (ECG) recordings, ambulatory fetal monitoring throughout pregnancy is now an important step closer to becoming feasible. The large number of electrodes required in current noise-robust solutions, however, leads to high power consumption and reduced patient comfort. In this paper, requirements for reliable fetal monitoring using a minimal number of electrodes are determined based on simulations and measurement results. To this end, a dipole-based model is proposed to simulate different electrode positions based on standard recordings. Results show a significant influence of bipolar lead orientation on maternal and fetal ECG measurement quality, as well as a significant influence of interelectrode distance for all signals of interest.
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47

Zhu, Bohui, Yongsheng Ding, and Kuangrong Hao. "A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/453402.

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This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared withK-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.
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Oertli, Annemarie, Susanne Rinné, Robin Moss, Stefan Kääb, Gunnar Seemann, Britt-Maria Beckmann, and Niels Decher. "Molecular Mechanism of Autosomal Recessive Long QT-Syndrome 1 without Deafness." International Journal of Molecular Sciences 22, no. 3 (January 23, 2021): 1112. http://dx.doi.org/10.3390/ijms22031112.

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KCNQ1 encodes the voltage-gated potassium (Kv) channel KCNQ1, also known as KvLQT1 or Kv7.1. Together with its ß-subunit KCNE1, also denoted as minK, this channel generates the slowly activating cardiac delayed rectifier current IKs, which is a key regulator of the heart rate dependent adaptation of the cardiac action potential duration (APD). Loss-of-function mutations in KCNQ1 cause congenital long QT1 (LQT1) syndrome, characterized by a delayed cardiac repolarization and a prolonged QT interval in the surface electrocardiogram. Autosomal dominant loss-of-function mutations in KCNQ1 result in long QT syndrome, called Romano–Ward Syndrome (RWS), while autosomal recessive mutations lead to Jervell and Lange-Nielsen syndrome (JLNS), associated with deafness. Here, we identified a homozygous KCNQ1 mutation, c.1892_1893insC (p.P631fs*20), in a patient with an isolated LQT syndrome (LQTS) without hearing loss. Nevertheless, the inheritance trait is autosomal recessive, with heterozygous family members being asymptomatic. The results of the electrophysiological characterization of the mutant, using voltage-clamp recordings in Xenopus laevis oocytes, are in agreement with an autosomal recessive disorder, since the IKs reduction was only observed in homomeric mutants, but not in heteromeric IKs channel complexes containing wild-type channel subunits. We found that KCNE1 rescues the KCNQ1 loss-of-function in mutant IKs channel complexes when they contain wild-type KCNQ1 subunits, as found in the heterozygous state. Action potential modellings confirmed that the recessive c.1892_1893insC LQT1 mutation only affects the APD of homozygous mutation carriers. Thus, our study provides the molecular mechanism for an atypical autosomal recessive LQT trait that lacks hearing impairment.
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Shi, Jingjing, Chao Chen, Hui Liu, Yinglong Wang, Minglei Shu, and Qing Zhu. "Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis." Computational and Mathematical Methods in Medicine 2021 (April 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/6691177.

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Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.
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Proietti, Marco, Alessio Farcomeni, Peter Goethals, Christophe Scavee, Johan Vijgen, Ivan Blankoff, Yves Vandekerckhove, Gregory YH Lip, and Georges H. Mairesse. "Cost-effectiveness and screening performance of ECG handheld machine in a population screening programme: The Belgian Heart Rhythm Week screening programme." European Journal of Preventive Cardiology 26, no. 9 (April 1, 2019): 964–72. http://dx.doi.org/10.1177/2047487319839184.

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Aims Overall, 40% of patients with atrial fibrillation are asymptomatic. The usefulness and cost-effectiveness of atrial fibrillation screening programmes are debated. We evaluated whether an atrial fibrillation screening programme with a handheld electrocardiogram (ECG) machine in a population-wide cohort has a high screening yield and is cost-effective. Methods We used a Markov-model based modelling analysis on 1000 hypothetical individuals who matched the Belgian Heart Rhythm Week screening programme. Subgroup analyses of subjects ≥65 and ≥75 years old were performed. Screening was performed with one-lead ECG handheld machine Omron® HeartScan HCG-801. Results In both overall population and subgroups, the use of the screening procedure diagnosed a consistently higher number of diagnosed atrial fibrillation than not screening. In the base-case scenario, the screening procedure resulted in 106.6 more atrial fibrillation patient-years, resulting in three fewer strokes, 10 more life years and five more quality-adjusted life years (QALYs). The number needed-to-screen (NNS) to avoid one stroke was 361. In subjects ≥65 years old, we found 80.8 more atrial fibrillation patient-years, resulting in three fewer strokes, four more life-years and five more QALYs. The NNS to avoid one stroke was 354. Similar results were obtained in subjects ≥75 years old, with a NNS to avoid one stroke of 371. In the overall population, the incremental cost-effectiveness ratio for any gained QALY showed that the screening procedure was cost-effective in all groups. Conclusions In a population-wide screening cohort, the use of a handheld ECG machine to identify subjects with newly diagnosed atrial fibrillation was cost-effective in the general population, as well as in subjects ≥65 and subjects ≥75 years old.
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