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Artículos de revistas sobre el tema "ARRHYTHMIA DATABASE"

Autor: Grafiati

Publicado: 26 de octubre de 2023

Última modificación: 25 de julio de 2025

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1

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 (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. W

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2

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 (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 ide

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3

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 (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 au

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4

Mallikarjunamallu K. "Enhanced Arrhythmia Detection Using Filtered Data, CNN, Graph Convolutional Networks, and SVM on MIT-BIH and PTB Databases." Journal of Electrical Systems 20, no. 1 (2024): 511–24. http://dx.doi.org/10.52783/jes.6078.

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Arrhythmia classification and detection are essential for the early diagnosis of heart diseases, but accurately identifying arrhythmias is challenging due to the inherent noise in electrocardiogram (ECG) data. This study presents a novel method for arrhythmia detection that follows a systematic approach. First, ECG data from the MIT-BIH Arrhythmia Database and the PTB Diagnostic Database are preprocessed using three distinct filters: wavelet transform (WT), finite impulse response (FIR), and an innovative infinite impulse response (IIR) filter to remove noise. The filtered data are then proces

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5

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 (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

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6

Zeng, Yuni, Hang Lv, Mingfeng Jiang, et al. "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

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7

Kapoor, Ankita, Samarthkumar Thakkar, Lucas Battel, et al. "The Prevalence and Impact of Arrhythmias in Hospitalized Patients with Sickle Cell Disorders: A Large Database Analysis." Blood 136, Supplement 1 (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

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8

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 (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

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9

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 (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

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10

Akbar, Muhamad, Siti Nurmaini, and Radiyati Umi Partan. "The deep convolutional networks for the classification of multi-class arrhythmia." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 1325–33. http://dx.doi.org/10.11591/eei.v13i2.6102.

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An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supra

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11

Alinsaif, Sadiq. "Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database." Computation 12, no. 2 (2024): 21. http://dx.doi.org/10.3390/computation12020021.

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Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisions made by cardiologists. This review paper surveys diverse computational intelligence methodologies employed for arrhythmia analysis within the context of the widely utilized MIT-BIH dataset. The paucity of adequately annotated medical datasets significantly impedes advancements in various healthcare domains. Publicly accessible resources such as

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12

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 (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 consi

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13

V S, Mrudhhula, and Mrs R. Thirumahal. "A HYBRID MODEL FOR CLASSIFICATION OF CARDIAC ARRHYTHMIAS USING CNN AND LSTM." International Journal of Engineering Applied Sciences and Technology 09, no. 05 (2024): 115–28. http://dx.doi.org/10.33564/ijeast.2024.v09i05.014.

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Electrocardiograms (ECGs) are vital tools for monitoring heart activity and diagnosing a wide range of heart conditions, including arrhythmias, which are characterized by irregular heartbeats. Arrhythmias, if not properly diagnosed and treated, can result in serious complications such as stroke, heart failure, or even sudden cardiac arrest. Given these risks, the accurate detection and classification of arrhythmias is critical for ensuring timely and effective medical interventions. This project aims to tackle this challenge by using the MIT-BIH Arrhythmia Database, a wellestablished resource

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14

Jesna, K. A*1 Shemeena M. 2. Archa A. B3 Santhosh B. S4 &. Anju V. Gopal5. "ECG FEATURE EXTRACTION AND CARDIAC ARRYTHMIA DETECTION BASED ON TIME DOMAIN ANALYSIS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY NACETEC' 19 (April 6, 2019): 60–67. https://doi.org/10.5281/zenodo.2631583.

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Heart diseases continue to be a primary killer disease in both developed and developing countries. Cardiac problems are considered to be the most fatal in medical world. Abnormalities in the heart lead to different cardiac arrhythmias. Computer-assisted automatic detection of cardiac arrhythmias is important when dealing with heart problems. One of the main techniques for diagnosing heart disease is based on the electrocardiogram (ECG).This paper presents a real time algorithm based on time domain analysis using GNU octave simulation tool for the detection of different arrhythmia by extracting

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Umapathi, Krishna Kishore, Aravind Thavamani, Harshitha Dhanpalreddy, and Hoang H. Nguyen. "Prevalence of cardiac arrhythmias in cannabis use disorder related hospitalizations in teenagers from 2003 to 2016 in the United States." EP Europace 23, no. 8 (2021): 1302–9. http://dx.doi.org/10.1093/europace/euab033.

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Abstract Aims Cannabis is an increasingly common recreational substance used by teenagers. However, there is limited data probing association of cardiac arrhythmias with marijuana use in this population. Methods and Results We provide prevalence trends, disease burden and healthcare utilization of cardiac arrhythmias associated with cannabis use disorder (CUD) in hospitalized teenagers (13–20 years) using a large national administrative database in the United States from 2003–2016. We used partial least square regression analysis for assessing trends in prevalence of cardiac arrhythmias and mu

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16

Gupta, T. Raghavendra, and D. Umanandhini. "Enhanced cardiac arrhythmia classification through integration of ensemble empirical mode decomposition and heart rate variability analysis." Future Technology 4, no. 3 (2025): 19–28. https://doi.org/10.55670/fpll.futech.4.3.3.

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Cardiac arrhythmias are critical conditions requiring accurate classification for effective diagnosis as well as treatment. In this investigation, we provide a novel approach for cardiac arrhythmia classification that integrates two advanced techniques for feature extraction from ECG signals: “Ensemble Empirical Mode Decomposition” (EEMD) and “Heart Rate Variability” (HRV) analysis. The proposed approach employs EEMD to decompose ECG signals into intrinsic mode functions, capturing signal features, while HRV analysis provides additional physiological insights into heart rate fluctuations. Comb

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17

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 (1991): A10. http://dx.doi.org/10.1016/0735-1097(91)91008-3.

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Giriprasad Gaddam, P., A. Sanjeeva reddy, and R. V. Sreehari. "Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network." Journal of Physics: Conference Series 2089, no. 1 (2021): 012058. http://dx.doi.org/10.1088/1742-6596/2089/1/012058.

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Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Fou

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19

Veena.K.N and Shobha.S. "An Electrocardiograph based Arrythmia Detection System." International Journal of Engineering and Management Research 8, no. 3 (2018): 131–36. https://doi.org/10.31033/ijemr.8.3.16.

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Cardiac disorders turn out to be a serious disease if not diagnosed and treated at the earliest. Arrhythmia is a cardiac disorder that exists as a result of irregular heart beat conditions. There are several variants in this type of disorder which can be only diagnosed only when patient is under an intensive care conditions and also the patient with such disorder do not experience and physical symptoms. Such diseases turn out to be deadly if not treated early. A detection system is thus required which is capable of detecting these arrhythmias in real time and aid in the diagnosis. An FPGA base

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20

Qi, Tianyu, He Zhang, Huijun Zhao, Chong Shen, and Xiaochen Liu. "Research on ECG Signal Classification Based on Hybrid Residual Network." Applied Sciences 14, no. 23 (2024): 11202. https://doi.org/10.3390/app142311202.

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Arrhythmia detection in electrocardiogram (ECG) signals is essential for monitoring cardiovascular health. Current automated arrhythmia classification methods frequently encounter difficulties in detecting multiple cardiac abnormalities, particularly when dealing with imbalanced datasets. This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual c

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21

Sumanta, Kuila, Maity Sayandeep, Kumar Mal Suman, and Joardar Subhankar. "Performance Analysis of ECG Arrhythmia Classification based on Different SVM Methods." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9, no. 12 (2020): 45–49. https://doi.org/10.5281/zenodo.5839644.

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Heart arrhythmias are the different types of heartbeats which are irregular in nature. In Tachycardia the heartbeat works too fast and in case of Bradycardia it works too slow. In the study of different cardiac conditions automatic detection of heart arrhythmia is done by the classification and feature extraction of Electrocardiogram(ECG) data. Various Support Vector Machine based methods are used to analyze and classify ECG signals for arrhythmia detection. There are several Support Vector Machine (SVM) methods used to classify the ECG data such as one against all, one against one and fuzzy d

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Hammad, Mohamed, Souham Meshoul, Piotr Dziwiński, Paweł Pławiak, and Ibrahim A. Elgendy. "Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification." Sensors 22, no. 23 (2022): 9347. http://dx.doi.org/10.3390/s22239347.

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An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals

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Kozieł, Paweł, Maria Grodkiewicz, Klaudia Artykiewicz, et al. "Does the watch can detect cardiac arrhythmias?" Journal of Education, Health and Sport 13, no. 2 (2023): 293–98. http://dx.doi.org/10.12775/jehs.2023.13.02.042.

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Introduction and purpose:The prevalence of cardiac arrhythmias in the population is not exactly known. Since cardiac arrhythmias are often episodic, they cannot be detected by conventional methods such as electrocardiography (ECG), which takes only a few seconds to record. The purpose of this review is to analyze the latest information regarding the use of smart watches to detect cardiac arrhythmias.Material and methods:This review is based on available data collected in the PubMed database published between 2015 and 2022. The search was performed by browsing keywords such as: "smartwatch", "c

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Soniwala, Mujtaba, Saadia Sherazi, Susan Schleede, et al. "Arrhythmia Burden in Patients with Indolent Lymphoma." Blood 136, Supplement 1 (2020): 6–7. http://dx.doi.org/10.1182/blood-2020-140053.

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Introduction Indolent Non-Hodgkin lymphomas (NHL) comprise a heterogeneous group of diseases including marginal zone lymphoma (MZL), lymphoplasmacytic lymphoma (LPL), small lymphocytic lymphoma/chronic lymphocytic leukemia (SLL/CLL), and follicular lymphoma (FL). These compose a heterogenous group of disorders that frequently measures survival in years due to the long natural history of these diseases. Frequency and morbidity of cardiac arrhythmias in patients with indolent lymphoma is unknown, but recent observations note that arrhythmias are an increasing problem. Due to advances in treatmen

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Ziti Fariha Mohd Apandi, Ryojun Ikeura, Soichiro Hayakawa, and Shigeyoshi Tsutsumi. "QRS Detection Based on Discrete Wavelet Transform for ECG Signal with Motion Artifacts." Journal of Advanced Research in Applied Sciences and Engineering Technology 40, no. 1 (2024): 118–28. http://dx.doi.org/10.37934/araset.40.1.118128.

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Motion artifacts in ECG signals recorded during physical exercises activities can affect the diagnosis of arrhythmia. To minimize the faults in arrhythmia detection, it was important to choose accurate algorithm for detecting QRS in ECG signal with noises produced during physical movements of the patients. Therefore, choosing the QRS detection algorithm with good competency for the signal affected by noises and motion artifacts is needed for arrhythmia detection analysis. The QRS detection based on Discrete Wavelet Transform was implemented and presented in this paper. The performance of the a

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Kovalchuk, O. V., and O. V. Barmak. "Method of arrhythmia classification on ECG signal." Optoelectronic Information-Power Technologies 48, no. 2 (2024): 34–44. http://dx.doi.org/10.31649/1681-7893-2024-48-2-34-44.

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This paper proposes an improved arrhythmia classification method based on a convolutional neural network (CNN) applied to ECG signals. To improve the quality of classification, ECG signals were split into fragments containing three cardiac cycles with the current cardiac cycle in the center. The improved CNN architecture includes the addition of batch normalization layers, an additional convolutional layer, and a dropout layer, which improvs the model's accuracy. In addition, hyperparameters were optimized for new CNN architecture. The model was trained data of the MIT-BIH Arrhythmia Database

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DeCamilla, J., X. Xia, M. Wang, et al. "The multiple arrhythmia dataset evaluation database (M.A.D.A.E.)." Journal of Electrocardiology 51, no. 6 (2018): S106—S112. http://dx.doi.org/10.1016/j.jelectrocard.2018.08.005.

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Linghu, Rongqian, and Ke Zhang. "Real-time Automatic Arrhythmia Detection System based on Extreme Gradient Boosting and Neural Network Algorithm." Journal of Physics: Conference Series 2449, no. 1 (2023): 012033. http://dx.doi.org/10.1088/1742-6596/2449/1/012033.

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Abstract Arrhythmia and other diseases are puzzling more and more people. Accurate detection is the key to realizing intelligent diagnosis of electrocardiogram(ECG) monitoring systems. It can prevent heart disease and effectively reduce mortality. An efficient and accurate arrhythmia detection method is urgent. In this work, a real-time automatic arrhythmia detection technology based on extreme gradient boosting (XGboost) and convolutional neural network (CNN) algorithm were developed. First, ECG signals in the MIT-BIH Arrhythmia database are preprocessed: 1) EMG interference filtering; 2) Pow

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Cintra, Fatima Dumas, Marcia Regina Pinho Makdisse, Wercules Antônio Alves de Oliveira, et al. "Exercise-induced ventricular arrhythmias: analysis of predictive factors in a population with sleep disorders." Einstein (São Paulo) 8, no. 1 (2010): 62–67. http://dx.doi.org/10.1590/s1679-45082010ao1469.

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ABSTRACT Objective: To assess the prevalence of ventricular arrhythmias induced by exercise in a population with sleep disorders and to analyze the triggering factors. Methods: Patients were consecutively selected from the database of the Sleep Clinic of Universidade Federal de São Paulo. All subjects were submitted to basal polysomnography, blood sample collection, physical examination, 12-lead ECG, spirometry, cardiorespiratory exercise study on a treadmill, and echocardiogram. The Control Group was matched for age and gender. Results: A total of 312 patients were analyzed. Exercise-induced

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Kovalchuk, Oleksii, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, and Iurii Krak. "Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning." Technologies 13, no. 1 (2025): 34. https://doi.org/10.3390/technologies13010034.

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Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we developed an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks. Second, we proposed an arrhythmia classification method utilizing a modified conv

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Rameshbabu, Swetha, and Sabitha Ramakrishnan. "Machine Learning Approach for Diagnosis and Prognosis of Cardiac Arrhythmia Condition Using a Minimum Feature Set and Auto-Segmentation-Based Window Optimisation." Elektronika ir Elektrotechnika 29, no. 5 (2023): 51–61. http://dx.doi.org/10.5755/j02.eie.34357.

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Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identifie

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Akram, Jaddoa Khalaf, and Jasim Mohammed Samir. "Verification and comparison of MIT-BIH arrhythmia database based on number of beats." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 4950–61. https://doi.org/10.11591/ijece.v11i6.pp4950-4961.

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, The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MITBIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the di fference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number t

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Abdulhafiz, Sabo, Abdulsalam Ya’u Gital, Sani Sabo Mohammed, and D. M. Nazif. "Modified Cardiac Arrhythmia Classification from Electrocardiography Signals Using a Convolutional Neural Network Model." Asian Journal of Science, Technology, Engineering, and Art 3, no. 4 (2025): 1007–28. https://doi.org/10.58578/ajstea.v3i4.5905.

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Manual classification of cardiac arrhythmias from electrocardiogram (ECG) signals is a labor-intensive and error-prone process due to the complex and variable nature of cardiac waveforms. Convolutional Neural Networks (ConvNets), widely recognized for their success in image classification, offer a promising solution for automating this task. This study proposes an enhanced ConvNet-based approach for the classification of cardiac arrhythmias, leveraging AlexNet as a feature extractor. The features obtained from the convolutional layers are input into a backpropagation neural network for final c

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Lin, Shih-Yi, Wu-Huei Hsu, Cheng-Chieh Lin, et al. "Association of Arrhythmia in Patients with Cervical Spondylosis: A Nationwide Population-Based Cohort Study." Journal of Clinical Medicine 7, no. 9 (2018): 236. http://dx.doi.org/10.3390/jcm7090236.

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Background: Sympathetic activity, including cervical ganglia, is involved in the development of cardiac arrhythmias. Objective: The present study investigated the association between cervical spondylosis and arrhythmia, which has never been reported before. Methods: Patients newly diagnosed with cervical spondylosis (CS) with an index date between 2000 and 2011 were identified from the National Health Insurance Research Database. We performed a 1:1 case-control matched analysis. Cases were matched to controls according to their estimated propensity scores, based on demographics and existing ri

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Liu, Feifei, Chengyu Liu, Xinge Jiang, et al. "Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases." Journal of Healthcare Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/9050812.

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A systematical evaluation work was performed on ten widely used and high-efficient QRS detection algorithms in this study, aiming at verifying their performances and usefulness in different application situations. Four experiments were carried on six internationally recognized databases. Firstly, in the test of high-quality ECG database versus low-quality ECG database, for high signal quality database, all ten QRS detection algorithms had very high detection accuracy (F1 >99%), whereas the F1 results decrease significantly for the poor signal-quality ECG signals (all <80%). Secondly, in

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Abdou, Abdoul-Dalibou, Ndeye Fatou Ngom, and Oumar Niang. "Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression." International Journal of Computational Intelligence and Applications 19, no. 03 (2020): 2050024. http://dx.doi.org/10.1142/s1469026820500248.

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In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the a

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Khalaf, Akram Jaddoa, and Samir Jasim Mohammed. "Verification and comparison of MIT-BIH arrhythmia database based on number of beats." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 4950. http://dx.doi.org/10.11591/ijece.v11i6.pp4950-4961.

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<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find th

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Feng, Jianchao, Yujuan Si, Yu Zhang, Meiqi Sun, and Wenke Yang. "A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition." Sensors 24, no. 14 (2024): 4558. http://dx.doi.org/10.3390/s24144558.

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In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using

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Bae, Tae Wuk, Sang Hag Lee, and Kee Koo Kwon. "An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis." Sensors 20, no. 21 (2020): 6144. http://dx.doi.org/10.3390/s20216144.

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With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major a

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kamil, Sarah, and Lamia Muhammed. "Arrhythmia Classification Using One Dimensional Conventional Neural Network." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (2021): 43–58. http://dx.doi.org/10.15849/ijasca.211128.04.

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Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its s

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Ma, Shuai, Jianfeng Cui, Weidong Xiao, and Lijuan Liu. "Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–17. http://dx.doi.org/10.1155/2022/1577778.

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Automated ECG-based arrhythmia detection is critical for early cardiac disease prevention and diagnosis. Recently, deep learning algorithms have been widely applied for arrhythmia detection with great success. However, the lack of labeled ECG data and low classification accuracy can have a significant impact on the overall effectiveness of a classification algorithm. In order to better apply deep learning methods to arrhythmia classification, in this study, feature extraction and classification strategy based on generative adversarial network data augmentation and model fusion are proposed to

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Moody, G. B., and R. G. Mark. "The impact of the MIT-BIH Arrhythmia Database." IEEE Engineering in Medicine and Biology Magazine 20, no. 3 (2001): 45–50. http://dx.doi.org/10.1109/51.932724.

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Shen, Qin, Hongxiang Gao, Yuwen Li, et al. "An Open-Access Arrhythmia Database of Wearable Electrocardiogram." Journal of Medical and Biological Engineering 40, no. 4 (2020): 564–74. http://dx.doi.org/10.1007/s40846-020-00554-3.

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Qin, Qin, Jianqing Li, Yinggao Yue, and Chengyu Liu. "An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm." Journal of Healthcare Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5980541.

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R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, we

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Sherazi, Saadia, Susan Schleede, Scott McNitt, et al. "Arrhythmogenic Cardiotoxicity Associated With Contemporary Treatments of Lymphoproliferative Disorders." Journal of the American Heart Association, March 9, 2023. http://dx.doi.org/10.1161/jaha.122.025786.

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Background There are limited data on risk of arrhythmias among patients with lymphoproliferative disorders. We designed this study to determine the risk of atrial and ventricular arrhythmia during treatment of lymphoma in a real‐world setting. Methods and Results The study population comprised 2064 patients included in the University of Rochester Medical Center Lymphoma Database from January 2013 to August 2019. Cardiac arrhythmias—atrial fibrillation/flutter, supraventricular tachycardia, ventricular arrhythmia, and bradyarrhythmia—were identified using International Classification of Disease

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Park, Yoonjee, Geum Joon Cho, Seung‐Young Roh, Jin Oh Na, and Min‐Jeong Oh. "Increased Cardiac Arrhythmia After Pregnancy‐Induced Hypertension: A South Korean Nationwide Database Study." Journal of the American Heart Association 11, no. 2 (2022). http://dx.doi.org/10.1161/jaha.121.023013.

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Background Although pregnancy‐induced hypertension (PIH) is associated with an elevated cardiovascular risk, long‐term studies or prepregnancy baseline data are scarce. Therefore, using a large nationwide cohort with prepregnancy periodic health screening data, we investigated whether clinically significant arrhythmia incidence increases after PIH. Methods and Results Data were extracted from the Korea National Health Insurance database and combined with the National Health Screening Examination database; women who gave birth between 2007 and 2015 and underwent the national health screening te

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Kobayashi, Takashi, and Kengo Kusano. "Cardiac arrhythmias in cancer patients using the nationwide claim‐based database in Japan." Journal of Arrhythmia 41, no. 4 (2025). https://doi.org/10.1002/joa3.70079.

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AbstractBackgroundCardio‐oncology has recently developed as a new discipline. No study using a Nationwide Claim‐Based Database has examined the association between cancer and arrhythmia in Japan.MethodsJROAD‐DPC (Japanese Registry Of All cardiac and vascular Diseases ‐ Diagnosis Procedure Combination) is a nationwide claims database using data from the Japanese Diagnosis Procedure Combination/Per Diem Payment System. Among 11 297 525 records found between April 2012 and March 2021 from 1119 hospitals, 2 976 362 patients with arrhythmias were studied and divided into categories using cancer.Res

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Saha, S., J. Zhou, S. C. Rosemas, et al. "A large, real-world cohort analysis of arrhythmia detections with insertable cardiac monitors." European Heart Journal 44, Supplement_2 (2023). http://dx.doi.org/10.1093/eurheartj/ehad655.309.

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Abstract Background Insertable cardiac monitors (ICMs) provide long-term continuous monitoring for arrhythmia diagnosis and management for various clinical indications. However, little data exists on the real-world diagnostic yield of ICMs across indicated patient populations, including both expected and incidental arrhythmic findings. Validated artificial intelligence (AI) algorithms have been developed to identify true arrhythmia episodes while significantly reducing false positives, enabling the adjudication of ICM episodes for larger cohorts of patients. Purpose To characterize comprehensi

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Vincze, V., A. Kardos, L. Kornyei, and H. Balint. "Supraventricular arrhythmia in tetralogy of Fallot repair." EP Europace 23, Supplement_3 (2021). http://dx.doi.org/10.1093/europace/euab116.308.

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Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Gottsegen National Cardiovascular Center BACKGROUND With aging morbidity related to arrhythmias in adult patients with Tetralogy of Fallot repair (TOFr) is increasing. OBJECTIVE We aimed to analyze the prevalence of supraventricular tachycardia in these patients using our prospective database. METHODS TOFr data were collected from our prospective database conducted since 2010. Supraventricular arrhythmias (intraatrial reentrant tachycardia (IART), atrial fibrillation, AFib) related complicati

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Nash, Dustin, Sonali Patel, Aarti Dalal, et al. "Abstract 16392: Arrhythmias in Acute Care Cardiology: Prevalence, Therapies, and Outcomes- An Analysis of the Pediatric Acute Care Cardiology Collaborative (PAC ³ ) Database." Circulation 148, Suppl_1 (2023). http://dx.doi.org/10.1161/circ.148.suppl_1.16392.

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Introduction: Arrhythmias are common in pediatric cardiology patients, yet prevalence during hospitalizations and treatments are poorly defined. This descriptive study reports arrhythmia prevalence and medical management from Pediatric Acute Care Cardiology Collaborative (PAC 3 ) centers. Aims Our primary aim is to describe prevalence and outcomes of treated arrhythmias among acute care cardiology units (ACCU). Secondary aims include comparison of medical vs. post-operative admissions and selection of antiarrhythmics. Methods: We performed a multi-center retrospective review of the PAC 3 regis

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