Relevant bibliographies by topics / ARRHYTHMIA DATABASE

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'ARRHYTHMIA DATABASE'

Author: Grafiati
Published: 26 October 2023

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Journal articles on the topic "ARRHYTHMIA DATABASE"

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CHIU, CHUANG-CHIEN, TONG-HONG LIN, and BEN-YI LIAU. "USING CORRELATION COEFFICIENT IN ECG WAVEFORM FOR ARRHYTHMIA DETECTION." Biomedical Engineering: Applications, Basis and Communications 17, no. 03 (June 25, 2005): 147–52. http://dx.doi.org/10.4015/s1016237205000238.

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Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphology characteristics of arrhythmias using correlation coefficient in ECG signal. Subjects for experiments included normal subjects, patients with atrial premature contraction (APC), and patients with ventricular premature contraction (PVC). So and Chan's algorithm was used to find the locations of QRS complexes. When the QRS complexes were detected, the correlation coefficient and RR-interval were utilized to calculate the similarity of arrhythmias. The algorithm was tested using MIT-BIH arrhythmia database and every QRS complex was classified in the database. The total number of test data was 538, 9 and 24 for normal beats, APCs and PVCs, respectively. The results are presented in terms of, performance, positive predication and sensitivity. High overall performance (99.3%) for the classification of the different categories of arrhythmic beats was achieved. The positive prediction results of the system reach 99.44%, 100% and 95.35% for normal beats, APCs and PVCs, respectively. The sensitivity results of the system are 99.81%, 81.82% and 95.83% for normal beats, APCs and PVCs, respectively. Results revealed that the system is accurate and efficient to classify arrhythmias resulted from APC or PVC. The proposed arrhythmia detection algorithm is therefore helpful to the clinical diagnosis.
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Zhai, Yuyun, Jinwei Li, and Quan Zhang. "Network pharmacology and molecular docking analyses of the potential target proteins and molecular mechanisms underlying the anti-arrhythmic effects of Sophora Flavescens." Medicine 102, no. 30 (July 28, 2023): e34504. http://dx.doi.org/10.1097/md.0000000000034504.

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The objective was to investigate the potential cardiac arrhythmia-related target proteins and molecular mechanisms underlying the anti-arrhythmic effects of Sophora flavescens using network pharmacology and molecular docking. The bioactive ingredients and related target proteins of S flavescens obtained from the Traditional Chinese medicine systems pharmacology data platform, and gene names for target proteins were obtained from the UniProt database. Arrhythmia-related genes were identified by screening GeneCards and Online Mendelian inheritance in man databases. A Venn diagram was used to identify the key arrhythmia-related genes that are potentially targeted by the bioactive ingredients of S flavescens. Furthermore, CytoScape 3.7.2 software was used to construct an “ingredient-target” network diagram and the “drug-ingredient-target-disease” network diagram. We performed gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis in the Metascape database and performed the docking analysis using CB-Dock software. We identified 45 main bioactive ingredients, from S flavescens and 66 arrhythmia-related target proteins. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that these targets were related to the chemical carcinogenesis-receptor activation signaling pathway, lipid and atherosclerosis signaling pathway, and fluid shear stress and atherosclerosis signaling pathway. Molecular docking showed that the target protein had good binding power with the main active components of the compound of S flavescens. Our study demonstrated the synergistic effects of multiple bioactive components of S flavescens on multiple arrhythmia-related target proteins and identified potential therapeutic mechanisms underlying the anti-arrhythmic effects of S flavescens, providing new clinical ideas for arrhythmia treatment.
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Deal, Barbara J., Constantine Mavroudis, Jeffrey Phillip Jacobs, Melanie Gevitz, and Carl Lewis Backer. "Arrhythmic complications associated with the treatment of patients with congenital cardiac disease: consensus definitions from the Multi-Societal Database Committee for Pediatric and Congenital Heart Disease." Cardiology in the Young 18, S2 (December 2008): 202–5. http://dx.doi.org/10.1017/s104795110800293x.

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AbstractA detailed hierarchal nomenclature of arrhythmias is offered with definition of its applications to diagnosis and complications. The conceptual and organizational approach to discussion of arrhythmias employs the following sequence: location – mechanism – aetiology – duration. The classification of arrhythmias is heuristically divided into an anatomical hierarchy: atrial, junctional, ventricular, or atrioventricular. Mechanisms are most simplistically classified as either reentrant, such as macro-reentrant atrial tachycardia, previously described as atrial flutter, or focal, such as automatic or micro-reentrant tachycardia, for example, junctional ectopic tachycardia. The aetiology of arrhythmias can be either iatrogenic, such as postsurgical, or non-iatrogenic, such as genetic or congenital, and in many cases is multi-factorial. Assigning an aetiology to an arrhythmia is distinct from understanding the mechanism of the arrhythmia, yet assignment of a possible aetiology of an arrhythmia may have important therapeutic implications in certain clinical settings. For example, postoperative atrial arrhythmias in patients after cardiac transplantation may be harbingers of rejection or consequent to remediable imbalances of electrolytes. The duration, frequency of, and time to occurrence of arrhythmia are temporal measures that further refine arrhythmia definition, and may offer insight into ascription of aetiology. Finally, arrhythmias do not occur in a void, but interact with other organ systems. Arrhythmias not only can result from perturbations of other organ systems, such as renal failure, but can produce dysfunction in other organ systems due to haemodynamic compromise or embolic phenomena.
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Moreland-Head, Lindsay N., James C. Coons, Amy L. Seybert, Matthew P. Gray, and Sandra L. Kane-Gill. "Use of Disproportionality Analysis to Identify Previously Unknown Drug-Associated Causes of Cardiac Arrhythmias Using the Food and Drug Administration Adverse Event Reporting System (FAERS) Database." Journal of Cardiovascular Pharmacology and Therapeutics 26, no. 4 (January 6, 2021): 341–48. http://dx.doi.org/10.1177/1074248420984082.

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Introduction: Drug-induced QTc-prolongation is a well-known adverse drug reaction (ADR), however there is limited knowledge of other drug-induced arrhythmias. Purpose: The objective of this study is to determine the drugs reported to be associated with arrhythmias other than QTc-prolongation using the FAERS database, possibly identifying potential drug causes that have not been reported previously. Methods: FAERS reports from 2004 quarter 1 through 2019 quarter 1 were combined to create a dataset of approximately 11.6 million reports. Search terms for arrhythmias of interest were selected from the Standardized MedDRA Queries (SMQ) Version 12.0. Frequency of the cardiac arrhythmias were determined for atrial fibrillation, atrioventricular block, bradyarrhythmia, bundle branch block, supraventricular tachycardia, and ventricular fibrillation and linked to the reported causal medications. Reports were further categorized by prior evidence associations using package inserts and established drug databases. A reporting odds ratio (ROR) and confidence interval (CI) were calculated for the ADRs for each drug and each of the 6 cardiac arrhythmias. Results: Of the 11.6 million reports in the FAERS database, 68,989 were specific to cardiac arrhythmias of interest. There were 61 identified medication-reported arrhythmia pairs for the 6 arrhythmia groups with 33 found to have an unknown reported association. Rosiglitazone was the most frequently medication reported across all arrhythmias [ROR 6.02 (CI: 5.82-6.22)]. Other medications with significant findings included: rofecoxib, digoxin, alendronate, lenalidomide, dronedarone, zoledronic acid, adalimumab, dabigatran, and interferon beta-1b. Conclusion: Upon retrospective analysis of the FAERS database, the majority of drug-associated arrhythmias reported were unknown suggesting new potential drug causes. Cardiac arrhythmias other than QTc prolongation are a new area of focus for pharmacovigilance and medication safety. Consideration of future studies should be given to using the FAERS database as a timely pharmacovigilance tool to identify unknown adverse events of medications.
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Zeng, Yuni, Hang Lv, Mingfeng Jiang, Jucheng Zhang, Ling Xia, Yaming Wang, and Zhikang Wang. "Deep arrhythmia classification based on SENet and lightweight context transform." Mathematical Biosciences and Engineering 20, no. 1 (2022): 1–17. http://dx.doi.org/10.3934/mbe.2023001.

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<abstract> <p>Arrhythmia is one of the common cardiovascular diseases. Nowadays, many methods identify arrhythmias from electrocardiograms (ECGs) by computer-aided systems. However, computer-aided systems could not identify arrhythmias effectively due to various the morphological change of abnormal ECG data. This paper proposes a deep method to classify ECG samples. Firstly, ECG features are extracted through continuous wavelet transform. Then, our method realizes the arrhythmia classification based on the new lightweight context transform blocks. The block is proposed by improving the linear content transform block by squeeze-and-excitation network and linear transformation. Finally, the proposed method is validated on the MIT-BIH arrhythmia database. The experimental results show that the proposed method can achieve a high accuracy on arrhythmia classification.</p> </abstract>
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Kapoor, Ankita, Samarthkumar Thakkar, Lucas Battel, Harsh P. Patel, Nikhil Agrawal, Shipra Gandhi, Pritika Manaktala, et al. "The Prevalence and Impact of Arrhythmias in Hospitalized Patients with Sickle Cell Disorders: A Large Database Analysis." Blood 136, Supplement 1 (November 5, 2020): 5–6. http://dx.doi.org/10.1182/blood-2020-142099.

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Introduction: Sickle cell disorders (SCD) is associated with progressive dysfunction of vital organs, including the cardiovascular system. While the development of pulmonary hypertension and left ventricular dysfunction have been previously studied, the burden of arrhythmias in SCD patients remains largely unknown. Thus, we aim to describe and analyze the prevalence and impact of arrhythmias in hospitalized adult patients with SCD and their impact in patient-oriented outcomes. Methods: We identified incident arrhythmias in patients with SCD in the National Inpatient Sample (NIS) database in 2 years (2016-2017), using ICD-10 codes. We compared major patient characteristics, outcomes, and economic impact between groups of SCD patients with and without documented arrhythmias. A logistic regression model was used to control for age, sex, race, admission type, hospital characteristics, and relevant comorbidities. To increase statistical robustness, propensity-score matching for age, sex, income, and comorbidities was used to match 1144 SCD patients with arrhythmia and 1144 patients without. Results: Among inpatients with SCD in the database, 5,930 of 174,450 patients with SCD had documented arrhythmias (3.4%). The arrhythmia group consisted mostly of patients' sickle cell disease (5,650; 95%), while 245 had sickle cell trait, and 35 were classified as having other sickle cell disorders. Individuals in the arrhythmia group were significantly older (mean age 41.3, SD 14.1) than those with no arrhythmia (mean age 31.5, SD 10.3). Further, arrhythmia group had higher prevalence of hypertension (44.2% vs 19.1%, p&lt;0.001), congestive heart failure (25.8% vs 4.1%, p&lt;0.001), chronic kidney disease (24.0% vs 5.6%, p&lt;0.001), valvular heart disease (9.3% vs 1.5%, p&lt;0.001), myocardial infarction (4.1% vs 1.25%, p&lt;0.001), type 2 diabetes mellitus (3.5% vs 1.7%, p&lt;0.001), and pulmonary hypertension (3.5% vs 1.2%, p&lt;0.001). When looking at major outcomes, after adjusting for confounders, arrhythmias were positively associated with all cause in-hospital mortality with an adjusted OR of 53.6 (95% CI 44.3, 65.1). After propensity-matching (Table 1), the arrhythmia group had a higher rate of all-cause in-hospital mortality (6.12% vs 0.35%, p&lt;0.001), higher median length of stay (6.8 days vs 5.8 days, p&lt;0.001), and mean total hospital charges ($13,441 vs $10,255, p&lt;0.001). There was no statistically significant difference in the rate of stroke between both groups. Conclusions: The presence of arrhythmias in patients with SCD was associated with markedly increased all cause in-hospital mortality, even after adjusting for confounders via logistic regression and propensity-score matching analyses. Despite a relatively low overall prevalence of 3.4% among this large inpatient cohort, this data suggests that arrhythmias may confer an important excess disease burden in SCD patients, including higher mortality. Intuitively, arrythmias confer higher mortality in patients with chronic disorders and multiple cardiovascular risks, whether this is specific to SCD deserves further study. Also, studies are needed to better understand the occurrence particularly in relation to active vaso-occlusive crisis and to evaluate whether SCD individuals with arrhythmias could potentially benefit from more intensive monitoring and/or better cardiovascular disease control. Disclosures No relevant conflicts of interest to declare.
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OTHMAN, MOHD AFZAN, and NORLAILI MAT SAFRI. "CHARACTERIZATION OF VENTRICULAR ARRHYTHMIAS USING A SEMANTIC MINING ALGORITHM." Journal of Mechanics in Medicine and Biology 12, no. 03 (June 2012): 1250049. http://dx.doi.org/10.1142/s0219519412004946.

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Ventricular arrhythmia, especially ventricular fibrillation, is a type of arrhythmia that can cause sudden death. The aim of this paper is to characterize ventricular arrhythmias using semantic mining by extracting their significant characteristics (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signals that represent the biological behavior of the cardiovascular system. Real data from an arrhythmia database are used after noise filtering and were statistically classified into two groups; normal sinus rhythm (N) and ventricular arrhythmia (V). The proposed method achieved high sensitivity and specificity (98.1% and 97.7%, respectively) and was capable of describing the differences between the N and V types in the ECG signal.
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Xu, Gang, Guangxin Xing, Juanjuan Jiang, Jian Jiang, and Yongsheng Ke. "Arrhythmia Detection Using Gated Recurrent Unit Network with ECG Signals." Journal of Medical Imaging and Health Informatics 10, no. 3 (March 1, 2020): 750–57. http://dx.doi.org/10.1166/jmihi.2020.2928.

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Background: Arrhythmia is a kind of heart disorder characterized by irregular heartbeats which can be detected with Electrocardiographic (ECG) signals. Accurate and early detection along with differentiation of arrhythmias is of great importance in a clinical setting. However, visual analysis of ECG signal is a challenging and timeconsuming work. We have developed an automatic arrhythmia detection model with deep learning framework to expedite the diagnosis of arrhythmia with a high degree of accuracy. Methods: We proposed a novel automatic arrhythmia detection model utilizing a combination of 1D convolutional neural network (1D-CNN) and Gated Recurrent Unit (GRU) network for the diagnosis of five different arrhythmia on ECG signals taken from the MITBIT arrhythmia physio bank database. Results: The proposed system showed a high classification performance in handling variable length ECG signal data, achieving an accuracy rate of 99.45%, sensitivity of 98.35% and specificity of 99.21% and a F1-Score of 98.95% using a five-fold cross validation strategy. Conclusions: Combining 1D-CNN and GRU networks yielded a higher degree of accuracy compared with other deep learning networks. Our proposed arrhythmia detection method may be a powerful tool to aid clinicians in accurately detecting common arrhythmias on routine ECG screening.
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N. S. V Rama Raju, N., V. Malleswara Rao, and I. Srinivasa Rao. "Automatic detection and classification of cardiac arrhythmia using neural network." International Journal of Engineering & Technology 7, no. 3 (July 11, 2018): 1482. http://dx.doi.org/10.14419/ijet.v7i3.14084.

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This paper proposes a Neural Network classifier model for the automatic identification of the ventricular and supraventricular arrhythmias cardiac arrhythmias. The wavelet transform (DWT) and dual tree complex wavelet transform (DTCWT) is applied for QRS complex detec-tion. After segmentation both feature of DWT and DTCWT is combined for feature extraction, statistical feature has been calculated to re-duce the overhead of classifier. An adaptive filtering with the soft computed wavelet thersholding to the signals before the extraction is done in pre-processing. Different ECG database is considered to evaluate the propose work with MIT-BIH database Normal Sinus Rhythm Da-tabase (NSRD) , and MIT-BIH Supraventricular Arrhythmia Database (svdb) .The evaluated outcomes of ECG classification claims 98 -99 % of accuracy under different training and testing situation.
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Herman, Jeffrey N., Richard I. Fogel, Philip J. Podrid, and Gary R. Garber. "Entropy: A cardiac arrhythmia multimedia database." Journal of the American College of Cardiology 17, no. 2 (February 1991): A10. http://dx.doi.org/10.1016/0735-1097(91)91008-3.

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Dissertations / Theses on the topic "ARRHYTHMIA DATABASE"

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Engström, Magnus, and Nadia Soheily. "EKG-analys och presentation." Thesis, KTH, Data- och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154539.

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Tolkningen av EKG är en viktig metod vid diagnostisering av onormala hjärttillstånd och kan användas i förebyggande syfte att upptäcka tidigare okända hjärtproblem. Att enkelt kunna mäta sitt EKG och få det analyserat och presenterat på ett pedagogiskt sätt utan att behöva rådfråga en läkare är något det finns ett konsumentbehov av. Denna rapport beskriver hur en EKG-signal behandlas med olika algoritmer och metoder i syfte att detektera hjärtslag och dess olika parametrar. Denna information används till att klassificera varje hjärtslag för sig och därmed avgöra om användaren har en normal eller onormal hjärtfunktion. För att nå dit har en mjukvaruprototyp utvecklats där algoritmerna implementerats. En enkätundersökning gjordes i syfte att undersöka hur utdata från mjukvaruprototypen skulle presenteras för en vanlig användare utan medicinsk utbildning. Sju filer med EKG-signaler från MIT-BIH Arrhythmia Database användes för testning av mjukvaruprototypen. Resultatet visade att prototypen kunde detektera en rad olika hjärtfel som låg till grund vid fastställning om hjärtat slog normalt eller onormalt. Resultatet presenterades på en mobilapp baserad på enkätundersökningen.
The interpretation of the ECG is an important method in the diagnosis of abnormal heart conditions and can be used proactively to discover previ-ously unknown heart problems. Being able to easily measure the ECG and get it analyzed and presented in a clear manner without having to consult a doctor is improtant to satisfy consumer needs. This report describes how an ECG signal is treated with different algo-rithms and methods to detect the heartbeat and its various parameters. This information is used to classify each heartbeat separately and thus determine whether the user has a normal or abnormal cardiac function. To achieve this a software prototype was developed in which the algorithms were implemented. A questionnaire survey was done in order to examine how the output of the software prototype should be presented for a user with no medical training. Seven ECG files from MIT-BIH Arrhythmia database were used for validation of the algorithms. The developed algorithms could detect of if any abnormality of heart function occurred and informed the users to consult a physician. The presentation of the heart function was based on the result from the questioner.
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Bsoul, Abed Al-Raoof. "PROCESSING AND CLASSIFICATION OF PHYSIOLOGICAL SIGNALS USING WAVELET TRANSFORM AND MACHINE LEARNING ALGORITHMS." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/258.

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Over the last century, physiological signals have been broadly analyzed and processed not only to assess the function of the human physiology, but also to better diagnose illnesses or injuries and provide treatment options for patients. In particular, Electrocardiogram (ECG), blood pressure (BP) and impedance are among the most important biomedical signals processed and analyzed. The majority of studies that utilize these signals attempt to diagnose important irregularities such as arrhythmia or blood loss by processing one of these signals. However, the relationship between them is not yet fully studied using computational methods. Therefore, a system that extract and combine features from all physiological signals representative of states such as arrhythmia and loss of blood volume to predict the presence and the severity of such complications is of paramount importance for care givers. This will not only enhance diagnostic methods, but also enable physicians to make more accurate decisions; thereby the overall quality of care provided to patients will improve significantly. In the first part of the dissertation, analysis and processing of ECG signal to detect the most important waves i.e. P, QRS, and T, are described. A wavelet-based method is implemented to facilitate and enhance the detection process. The method not only provides high detection accuracy, but also efficient in regards to memory and execution time. In addition, the method is robust against noise and baseline drift, as supported by the results. The second part outlines a method that extract features from ECG signal in order to classify and predict the severity of arrhythmia. Arrhythmia can be life-threatening or benign. Several methods exist to detect abnormal heartbeats. However, a clear criterion to identify whether the detected arrhythmia is malignant or benign still an open problem. The method discussed in this dissertation will address a novel solution to this important issue. In the third part, a classification model that predicts the severity of loss of blood volume by incorporating multiple physiological signals is elaborated. The features are extracted in time and frequency domains after transforming the signals with Wavelet Transformation (WT). The results support the desirable reliability and accuracy of the system.
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Zhorný, Lukáš. "Detekce komplexů QRS v signálech EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413175.

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This thesis deals with the detection of QRS complexes from electrocardiograms using time-frequency analysis. Detection procedures are based on wavelet and Stockwell transform. The theoretical part describes the basics of electrocardiography, then introduces common approaches to time-frequency analysis, such as short-time Fourier transform (STFT), wavelet transform and Stockwell transform. These algorithms were tested on a set of electrograms from the MIT-BIH and CSE-MO1 arrhythmia database. For the CSE database worked best the method based on the wavelet transform with the filter bank Symlet4, with the resulting value of sensitivity 100 % and positive predictivity 99.86%. For the MIT database had the best performance the detector using the Stockwell transform with values of sensitivity 99.54% and positive predictivity 99.68%. The results were compared with the values of other authors mentioned in the text.
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MUNJAL, NAVEEN KUMAR. "ECG DENOISING USING THE WAVELETS AND ROBUST ANALYSIS OF ECG SIGNALS." Thesis, 2013. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15780.

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The project aims at the successful development of an algorithm to rapidly and efficiently denoising the ECG waveforms . In general, ECG signals affected by noises such as baseline wandering,power line interference, electromagnetic interference, and high frequency noises during data acquisition. In order to retain the ECG signal morphology, several researches have adopted using different preprocessing methods. I have considered the Discrete Wavelet Transform (DWT) based wavelet denoising have incorporated using different thresholding techniques to remove three major sources of noises from the acquired ECG signals namely, power line interference, baseline wandering, and high frequency noises. seven wavelet functions ("db1","coif1","rbio1.1","dmey","bior1.1","haar" and "sym1") and four different thresholding levels are used to de-noise the noise in ECG signals.The proposed algorithm in this thsis can be used for accurate and fast feature extraction from any ECG signal and for further classification into normal and abnormal signal. Our work basically includes three phases namely de-noising of the input signal, detection of peaks and finally detecting the abnormality if any present.
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Kuo, Yi-Shi, and 郭怡希. "Use of Cardiac Arrhythmia Interpretation Timing Characteristics Related Diseases Characterized by Solid Research and Database for Hadoop." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/uhc4gm.

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碩士
國立中正大學
通訊資訊數位學習碩士在職專班
103
Abstract This article aims to propose an interpretation method that utilizes time sequence characteristics in order to classify the symptoms of heart diseases, as well as to store the voluminous data before and after classification into the database via the parallel algorithm approach, in order to facilitate the utilization of future medical therapy. Those issues to be faced are as follows: the initial one is to obtain a PR interval and to use the change of this time sequence as input data for the identification between normal rhythm and abnormal rhythm of cardiac arrhythmia. The waveforms identified by a classifier include the normal rhythm, cardiac arrhythmia and others. The data of ECG signals are from the database of MIT-BIH Arrhythmia with selected 5-file data of heartbeat periods integrated with the LIBSVM Function and algorithm of Professor Lin Chih-Jen. The time sequence characteristics can still have an almost 100% accuracy rate under the influence of sound. Also, the characteristic points are computed as to the hyper-plane distance and the relationships between those accuracy rates are investigated.
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Zhao, Hui. "Magnetocardiographic evaluation of fetal arrhythmia /." 2005. http://www.library.wisc.edu/databases/connect/dissertations.html.

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Silva, Aurélio Filipe de Sousa e. "Deteção de extra-sístoles ventriculares." Master's thesis, 2012. http://hdl.handle.net/10216/68387.

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Silva, Aurélio Filipe de Sousa e. "Deteção de extra-sístoles ventriculares." Dissertação, 2012. http://hdl.handle.net/10216/68387.

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Vega, Amanda L. "Arrhythmia mutations in the cardiac inward rectifying potassium channel Kir2.1 (KCNJ2) : mechanisms for molecular and cellular phenotypes /." 2008. http://www.library.wisc.edu/databases/connect/dissertations.html.

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Book chapters on the topic "ARRHYTHMIA DATABASE"

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Kuila, Sumanta, Namrata Dhanda, and Subhankar Joardar. "Feature Extraction and Classification of MIT-BIH Arrhythmia Database." In Lecture Notes in Electrical Engineering, 417–27. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0829-5_41.

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Martono, Niken Prasasti, Toru Nishiguchi, and Hayato Ohwada. "ECG Signal Classification Using Recurrence Plot-Based Approach and Deep Learning for Arrhythmia Prediction." In Intelligent Information and Database Systems, 327–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21743-2_26.

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Zhang, Jingyao, Fengying Ma, and Wei Chen. "An Improved CNNLSTM Algorithm for Automatic Detection of Arrhythmia Based on Electrocardiogram Signal." In Database Systems for Advanced Applications. DASFAA 2021 International Workshops, 185–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73216-5_13.

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Montenegro, Larissa, Hugo Peixoto, and José M. Machado. "Evaluation of Transfer Learning to Improve Arrhythmia Classification for a Small ECG Database." In Advances in Artificial Intelligence – IBERAMIA 2022, 231–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22419-5_20.

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Torres-Alegre, Santiago, Juan Fombellida, Juan Antonio Piñuela-Izquierdo, and Diego Andina. "Artificial Metaplasticity: Application to MIT-BIH Arrhythmias Database." In Artificial Computation in Biology and Medicine, 133–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18914-7_14.

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Travieso, Carlos M., Jesús B. Alonso, Miguel A. Ferrer, and Jorge Corsino. "Automatic Arrhythmia Detection." In Soft Computing Methods for Practical Environment Solutions, 204–18. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch013.

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In the present chapter, the authors have developed a tool for the automatic arrhythmias detection, based on time-frequency features and using a Support Vector Machines (SVM) as classifier. Arrhythmia Database Massachusetts Institute of Technology (MIT) has been used in the work in order to detect eight different states, seven are pathologies and one is normal. The unions of different blocks and its optimization have found success rates of 99.82% for RR’ interval detection from electrocardiogram (PQRST waves), and 99.23% for pathologic detection. In particular, the authors have used wavelet transform in order to characterize the wave of electrocardiogram (ECG), based on Biorthogonal family, achieving the most discriminative coefficients. A discussion on arrhythmia ECG classification methods is also presented in this paper.
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Jha, Chandan Kumar. "ECG Signal Analysis for Automated Cardiac Arrhythmia Detection." In Advances in Bioinformatics and Biomedical Engineering, 140–57. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch008.

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The graphical recordings of electrical stimuli generated by heart muscle cells are known as an electrocardiogram (ECG). In cardiology, ECG is widely used to detect different cardiovascular diseases among which arrhythmias are the most common. Irregular heart cycles are collectively known as arrhythmias and may produce sudden cardiac arrest. Many times, arrhythmia evolves over an extended period. Hence, it requires an artificial-intelligence-enabled continuous ECG monitoring system that can detect irregular heart cycles automatically. In this regard, this chapter presents a methodological analysis of machine-learning and deep-learning-based arrhythmia detection techniques. Focusing on the state of the art, a deep-learning-based technique is implemented which recognizes normal heartbeat and seven different classes of arrhythmias. This technique uses a convolutional neural network as a classification tool. The performance of this technique is evaluated using ECG records of the MIT-BIH arrhythmia database. This technique performs well in terms of different classification metrics.
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El Omary, Sara, Souad Lahrache, and Rajae El Ouazzani. "A Lightweight CNN to Identify Cardiac Arrhythmia Using 2D ECG Images." In AI Applications for Disease Diagnosis and Treatment, 122–60. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2304-2.ch005.

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Worldwide, cardiac arrhythmia disease has become one of the most frequent heart problems, leading to death in most cases. In fact, cardiologists use the electrocardiogram (ECG) to diagnose arrhythmia by analyzing the heartbeat signals and utilizing electrodes to detect variations in the heart rhythm if they show certain abnormalities. Indeed, heart attacks depend on the treatment speed received, and since its risk is increased by arrhythmias, in this chapter the authors create an automatic system that can detect cardiac arrhythmia by using deep learning algorithms. They propose a deep convolutional neural network (CNN) to automatically classify five types of arrhythmias then evaluate and test it on the MIT-BIH database. The authors obtained interesting results by creating five CNN models, testing, and comparing them to choose the best performing one, and then comparing it to some state-of-the-art models. The authors use significant performance metrics to evaluate the models, including precision, recall, sensitivity, and F1 score.
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Jha, Chandan Kumar, and Maheshkumar H. Kolekar. "Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier." In Advances in Medical Technologies and Clinical Practice, 74–88. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7796-6.ch004.

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Abnormal behavior of heart muscles generates irregular heartbeats which are collectively known as arrhythmia. Classification of arrhythmia beats plays a prominent role in electrocardiogram (ECG) analysis. It is widely used in online and long-term patient monitoring systems. This chapter reports a classification technique to recognize normal (N) and five arrhythmia beats (i.e., left bundle branch block [LBBB], right bundle branch block [RBBB], premature ventricular contraction [V], paced [P], and atrial premature contraction [A]). The technique utilizes features of heartbeats extracted by the wavelet multi-resolution analysis. The feature vectors are used to train and test the classifier based on the support vector machine which has been emerged as a benchmark in machine learning classifier. It accomplishes the beat classification very efficiently. ECG records of the MIT-BIH arrhythmia database are utilized to acquire the different types of heartbeats. Performance of the proposed classifier outperforms the contemporary arrhythmia beats classification techniques.
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N., Raghu. "Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–20. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1192-3.ch001.

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An electrocardiogram (ECG) is used as one of the important diagnostic tools for the detection of the health of a heart. An automatic heart abnormality identification methods sense numerous abnormalities or arrhythmia and decrease the physician's pressure as well as share their workload. In ECG analysis, the main focus is to enhance degree of accuracy and include a number of heart diseases that can be classified. In this chapter, arrhythmia classification is proposed using hybrid features of T-wave in ECG. The classification system consists of majorly three phases, windowing technique, feature extraction, and classification. This classifier categorizes the normal and abnormal signals efficiently. The experimental analysis showed that the hybrid features arrhythmia classification performance of accuracy approximately 98.3%, specificity 98.0%, and sensitivity 98.6% using MIT-BIH database.
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Conference papers on the topic "ARRHYTHMIA DATABASE"

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Wu, Meng-Hsi, and Edward Y. Chang. "DeepQ Arrhythmia Database." In MM '17: ACM Multimedia Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132635.3132647.

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Baia, Alexandre Farias, and Adriana Rosa Garcez Castro. "A Competitive Structure of Convolutional Autoencoder Networks for Electrocardiogram Signals Classification." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4446.

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This paper presents the proposal of an electrocardiogram (ECG) signals classification system through a competitive structure of Convolutional Autoencoders (CAE). Two Convolutional Autoencoders were trained to reconstruct ECG signals for the cases of patients with arrhythmia and patients with signals considered normals. After the training, the two networks were arranged in a competitive parallel structure to classify these signals. For the development and testing of the system, the MIT-BIH Arrhythmia Database of ECG signals was used. An accuracy of 88,9% was achieved considering the database used for system testing.
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Merdjanovska, E., and A. Rashkovska. "Cross-Database Generalization of Deep Learning Models for Arrhythmia Classification." In 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, 2021. http://dx.doi.org/10.23919/mipro52101.2021.9596930.

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Ředina, "Richard, Jakub Hejc, David Pospisil, Marina Ronzhina, Petra Novotna, and Zdenek Starek." "Arrhythmia Database with Annotated Intracardial Atrial Signals from Pediatric Patients Undergoing Catheter Ablation." In 2022 Computing in Cardiology Conference. Computing in Cardiology, 2022. http://dx.doi.org/10.22489/cinc.2022.282.

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Oliveira, Gustavo Henrique de, and Franklin César Flores. "Classification of heart arrhythmia by digital image processing and machine learning." In Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/semish.2023.230225.

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The electrocardiogram (ECG) exam can be used reliably as a measure to monitor the functionality of the cardiovascular system. Although there are many similarities between different ECG conditions, the focus of most studies has been to classify a set of database signals known as PhysionNet MIT-BIH and PTB Diagnostics data sets, rather than classifying problems in real images. In this article, we propose methods to extract features from the exam image and then algorithms such as CNN, decision tree, extra trees and random forest are used for the classification of exams, which is able to accurately classify according to the AAMI EC57 standard . According to the results, the suggested method is capable of making predictions with an average accuracy of 97.4 %.
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Tsoutsouras, Vasileios, Dimitra Azariadi, Sotirios Xydis, and Dimitrios Soudris. "Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database." In 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies". ICST, 2015. http://dx.doi.org/10.4108/eai.14-10-2015.2261640.

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Chakroborty, Sandipan, and Meru A. Patil. "Real-time arrhythmia classification for large databases." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6943873.

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Poigai Arunachalam, Shivaram, Elizabeth M. Annoni, Suraj Kapa, Siva K. Mulpuru, Paul A. Friedman, and Elena G. Tolkacheva. "Robust Discrimination of Normal Sinus Rhythm and Atrial Fibrillation on ECG Using a Multiscale Frequency Technique." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3302.

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Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death [1]. 12-lead electrocardiogram (ECG) is used to monitor normal sinus rhythm (NSR) and also detect AF. Although the persistent form of AF can be detected relatively easy, detecting paroxysmal AF is often a challenge since requiring continuous monitoring, which becomes expensive and cumbersome to collect lot of ECG data [1]. Several researchers have attempted to develop new methods to discriminate NSR and AF which are based on R-R interval analysis, linear methods, filtering, spectral analysis, statistical approaches such as entropy etc. which faces limitation of successfully detecting AF of all types with high sensitivity and specificity using short time ECG data [1–3]. The major issues with these approaches is that they often distort the ECG by several pre-processing steps with filters, do not provide reliable discrimination using short ECG time series data and many of them lack real-time capability that makes it difficult to trust the data for diagnosis and treatment. Both clinical and scientific communities recognize these difficulties and the necessity to develop novel methods that can enable accurate monitoring and detection of AF [2]. In addition, robust detection and classification algorithms are essential for delivering appropriate therapy for implantable cardioverter defibrillators (ICD) to provide lifesaving timely action. In this work, the authors propose and demonstrate the application of a multiscale frequency (MSF) approach [4] for accurate detection and discrimination between AF and NSR ECG traces taken from publically available Physionet database. The MSF approach takes into account the contribution from various frequencies in ECG and thus yield valuable information regarding the chaotic nature of AF. Therefore, we demonstrate that MSF can capture the complexity of AF which is associated with higher MSF value compared with NSR thus enabling robust discrimination e AF manifests itself with numerous chaotic frequencies within the body surface ECG,. We validate the feasibility of this technique to discriminate NSR from AF.
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Manilo, Liudmila A., Anatoly P. Nemirko, Ekaterina G. Evdakova, and Anna A. Tatarinova. "ECG Database for Evaluating the Efficiency of Recognizing Dangerous Arrhythmias." In 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB). IEEE, 2021. http://dx.doi.org/10.1109/csgb53040.2021.9496029.

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Wei Heng, Wei, Eileen Su Lee Ming, Ahmad Nizar Jamaluddin, Fauzan Khairi Che Harun, Nurul Ashikin Abdul-Kadir, and Che Fai Yeong. "Prediction Algorithm of Malignant Ventricular Arrhythmia Validated across Multiple Online Public Databases." In 2019 Computing in Cardiology Conference. Computing in Cardiology, 2019. http://dx.doi.org/10.22489/cinc.2019.295.

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Reports on the topic "ARRHYTHMIA DATABASE"

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Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.
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