Relevant bibliographies by topics / Arrhythmia classifier

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Academic literature on the topic 'Arrhythmia classifier'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "Arrhythmia classifier"

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Priti, Suryawanshi, and Shailesh Hambarde Dr. "Prediction of Cardiac Arrhythmia using Random Forest Machine Learning Algorithm." Journal of Emerging Technologies and Innovative Research 11, no. 3 (2024): d199—d204. https://doi.org/10.5281/zenodo.10828678.

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   Arrhythmia disease is a common disorder that affects the heart's rhythm and can lead to serious complications. The accurate prediction of arrhythmia is crucial for early diagnosis and effective treatment of patients. In recent years, machine learning algorithms have emerged as a promising approach for predicting arrhythmia disease. Early and precise detection of cardiac arrhythmias is crucial for improved patient outcomes. This investigation delves into the efficacy of a Random Forest (RF) classifier for automated arrhythmia detection using electrocardiogram (ECG) data. The research evaluates the RF model's performance on a publicly available ECG dataset, benchmarking it against existing methodologies. The findings substantiate the effectiveness of the RF classifier in arrhythmia detection, achieving superior accuracy, robustness, and interpretability.
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Vimal, C., and B. Sathish. "Random Forest Classifier Based ECG Arrhythmia Classification." International Journal of Healthcare Information Systems and Informatics 5, no. 2 (2010): 1–10. http://dx.doi.org/10.4018/jhisi.2010040101.

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Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.
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SATHYAMANGALAM NATARAJAN, SHIVAPPRIYA, ARUN KUMAR SHANMUGAM, JUDE HEMANTH DURAISAMY, and HARIKUMAR RAJAGURU. "PREDICTION OF CARDIAC ARRHYTHMIA USING MULTI CLASS CLASSIFIERS BY INCORPORATING WAVELET TRANSFORM BASED FEATURES." DYNA 97, no. 4 (2022): 418–24. http://dx.doi.org/10.6036/10458.

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Timely diagnosis and earlier detection of the dangerous heart conditions will reduce the mortality rate and save life of the patient. For that, it is necessary to automate the classi?cation and prediction of Cardiac Arrhythmia. Raw ECG signal is extracted from the MIT-BIH Arrhythmia database, followed by preprocessing and feature extraction using wavelet transform method. Further the extracted features are used for the classification of four different cardiac arrhythmias such as Bradycardia, Tachycardia, Left and Right Bundle Branch Block. Comparative study on the five different classifiers namely Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers (KNN), Ensemble Classifiers, and its variants are experimented in the proposed work. Among these, the weighted KNN classifier gives higher accuracy (90.3%) and prediction speed (10,000 observations per second) with reduced training time (4.329 seconds), compared with the existing state of the art methods. The prediction speed is 10,000 numbers of observations per second which identifies the heart problem earlier, and so appropriate treatment can be given to the patient. To further improve the classification accuracy, three optimizable classifiers namely Optimizable KNN, optimizable SVM, optimizable ensemble are used for the hyper parameter tunning and weight optimization. The optimizable SVM provides better perform (accuracy 93.4 %) among the three optimizable classifiers as well as the existing state of the art works. Therefore, the proposed work used for earlier Cardiac arrhythmia disease diagnosis and prognosis. Keywords: ECG, Cardiac Arrhythmia, Wavelet Transform, Multi class Classifiers, Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers, Ensemble Classifiers, Optimizable classifier.
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Kumar M., Arun, and Arvind Chakrapani. "Classification of ECG signal using FFT based improved Alexnet classifier." PLOS ONE 17, no. 9 (2022): e0274225. http://dx.doi.org/10.1371/journal.pone.0274225.

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Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.
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Jan, Atif, Numan Khurshid, and Muhammad Irfan Khattak. "Developing Resource Efficient Heart Arrhythmia Classifier." International Journal of Computer Applications 109, no. 16 (2015): 35–39. http://dx.doi.org/10.5120/19274-1014.

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Leong, P. H. W., and M. A. Jabri. "A low-power VLSI arrhythmia classifier." IEEE Transactions on Neural Networks 6, no. 6 (1995): 1435–45. http://dx.doi.org/10.1109/72.471380.

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DAS, MANAB KUMAR, and SAMIT ARI. "ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET." Journal of Mechanics in Medicine and Biology 14, no. 05 (2014): 1450066. http://dx.doi.org/10.1142/s0219519414500663.

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In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.
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Arvanaghi, Roghayyeh, Sabalan Daneshvar, Hadi Seyedarabi, and Atefeh Goshvarpour. "CLASSIFICATION OF CARDIAC ARRHYTHMIAS USING ARTERIAL BLOOD PRESSURE BASED ON DISCRETE WAVELET TRANSFORM." Biomedical Engineering: Applications, Basis and Communications 29, no. 05 (2017): 1750034. http://dx.doi.org/10.4015/s101623721750034x.

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Early and correct diagnosis of cardiac arrhythmias is an important step in the treatment of patients. In the recent decades, a wide area of bio-signal processing is allocated to cardiac arrhythmia classification. Unlike other studies, which have employed Electrocardiogram (ECG) signal as a main signal to classify the arrhythmia and sometimes they have used other vital signals as an auxiliary signal to fill missing data and robust detections. In this study, the Arterial Blood Pressure (ABP) is used to classify six types of heart arrhythmias. In other words, in this study for first time, the arrhythmias are classified according ABP signal information. Discrete Wavelet Transform (DWT) is used to de-noise and decompose ABP signal. On feature extraction stage, three types of features including frequency, power, and entropy are extracted. In classification stage, Least Square Support Vector Machine (LS-SVM) is employed as a classifier. The accuracy, sensitivity, and specificity rates of 95.75%, 96.77%, and 96.32% are achieved, respectively. Currently, the classification of cardiac arrhythmias is based on the ABP signal which has some advantages. The recording of ABP signal is done by means of one electrode and therefore it has resulted in lower costs compared with the ECG signal. Finally, it has been shown that ABP has very important and valuable information about the heart performance and can be used in arrhythmia classification.
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Rizqyawan, Muhammad Ilham, Artha Ivonita Simbolon, and Dwi Esti Kusumandari. "Weighted SVM with RR Interval based Features for Android-based Arrhythmia Classifier." Internetworking Indonesia Journal 10, no. 2 (2018): 57–62. https://doi.org/10.5281/zenodo.3257962.

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ECG is one of the most popular fields in biosignals research. One of the popular area in ECG research is automatic Arrhythmia classification. In this paper, we presented an effort to make an Arrhythmia classifier for Android. We use RRI based features and SVM as the classification method. Then we conduct an experiment with three different SVM configuration to see how much improvement can be made by using these configurations. By looking at kappa score as the metrics, the configuration 2 is greatly improve the classifier (169% increase). And by using hyper-parameter tuning we further optimize the classifier as can be seen on result of configuration 3 (10.5% increase).
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Thirrunavukkarasu, R. R., and T. Meera Devi. "Empirical Mode Decomposition with Fuzzy Weight Beetle Swarm Optimization (EMD-FWBSO) Denoising and Enhanced Kernel Support Vector Machine (EKSVM) Classifier for Arrhythmia in Electrocardiogram Recordings." Journal of Medical Imaging and Health Informatics 11, no. 11 (2021): 2778–89. http://dx.doi.org/10.1166/jmihi.2021.3870.

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Elderly persons are generally prone to CHDs (Chronic Heart Diseases). Arrhythmia is a persistent CHD with high mortalities resulting from cardiac failures, heart strokes, and CADs (Coronary Artery Diseases). Arrhythmia can be detected using ECG (Electrocardiogram) signals. ECG signals need to be pre-processed for removing noises present in signals. Since denoising is a significant step in ECG signals. Recently Support Vector Machine -Radial Bias Function (SVM-RBF) classifier is introduced for arrhythmia classification, it doesn’t remove noises presented from the ECG signals. The major aim of the work is to design a new classifier with removed noises and enhanced ECG signal. In this work, EMDs (Empirical Mode Decompositions) is introduced for noise removing which works recursively and dependent on signals called sifting. In EMD, IMFs (Intrinsic Mode Functions) decompose noisy signals into intrinsic oscillatory components adaptively using sifting. Further, FWBSOs (Fuzzy Weight Beetle Swarm Optimizations) are used in this work for optimizing EMDs and IMFs. This work in the initial phase reconstructs ECG signals which are filtered by IMFs. These filters are followed by extraction of morphological features from waves of P-QRS-T while ECG segments are selected using PCAs and DTWs. In the final phase, EKSVMs (Enhanced Kernel Support Vector Machines) classifies extracted features automatically by categorizing ECG signals into Normal and Ventricular Ectopic Beats. This work’s resulted are evaluated with performance metrics of Sensitivity, F-measure, Positive Productivity and Accuracy. This work uses database of MIT-BIH arrhythmia in a 5 fold cross validation for its predictions. The proposed EKSVMs classifier is compared to existing classifiers such as K-Nearest Neighbors (KNN), Enhanced Particle Swarm Optimisation-Multiple Layer Perception (EPSO-MLP) and SVM-RBF. The experiments of the proposed classifier and existing methods are carried out on MATLAB R2018a.
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Dissertations / Theses on the topic "Arrhythmia classifier"

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(10724028), Jason David Ummel. "NONINVASIVE MEASUREMENT OF HEARTRATE, RESPIRATORY RATE, AND BLOOD OXYGENATION THROUGH WEARABLE DEVICES." Thesis, 2021.

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<p>The last two decades have shown a boom in the field of wearable sensing technology. Particularly in the consumer industry, growing trends towards personalized health have pushed new devices to report many vital signs, with a demand for high accuracy and reliability. The most common technique used to gather these vitals is photoplethysmography or PPG. PPG devices are ideal for wearable applications as they are simple, power-efficient, and can be implemented on almost any area of the body. Traditionally PPGs were utilized for capturing just heart rate, however, recent advancements in hardware and digital processing have led to other metrics including respiratory rate (RR) and peripheral oxygen saturation (SpO2), to be reported as well. Our research investigates the potential for wearable devices to be used for outpatient apnea monitoring, and particularly the ability to detect opioid misuse resulting in respiratory depression. Ultimately, the long-term goal of this work is to develop a wearable device that can be used in the rehabilitation process to ensure both accountability and safety of the wearer. This document details contributions towards this goal through the design, development, and evaluation of a device called “Kick Ring”. Primarily, we investigate the ability of Kick Ring to record heartrate (HR), RR, and SpO2. Moreover, we show that the device can calculate RR in real time and can provide an immediate indication of abnormal events such as respiratory depression. Finally, we explore a novel method for reporting apnea events through the use of several PPG characteristics. Kick Ring reliably gathers respiratory metrics and offers a combination of features that does not exist in the current wearables space. These advancements will help to move the field forward, and eventually aid in early detection of life-threatening events.</p>
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Books on the topic "Arrhythmia classifier"

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Katritsis, Demosthenes G., Bernard J. Gersh, and A. John Camm. Ventricular arrhythmias. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199685288.003.1275_update_004.

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Ventricular arrhythmias are classified, and their pathophysiology is presented. The differential diagnosis of wide QRS tachycardias is summarized. Risk stratification tests for patients with ventricular arrhythmias and acute and chronic management of these conditions are discussed. Ventricular arrhythmias are also discussed in the context of relevant clinical settings, and specific recommendations about management are provided. ACC/AHA and ESC guidelines that refer to ventricular arrhythmias and indications for ICD therapy have been tabulated.
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Book chapters on the topic "Arrhythmia classifier"

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Wu, Jing, Shuo Zhang, Xingyao Wang, and Chengyu Liu. "GCN-ResNet: A Multi-label Classifier for ECG Arrhythmia." In 12th Asian-Pacific Conference on Medical and Biological Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51455-5_52.

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Mohebbanaaz, L. V. Rajani Kumari, and Y. Padma Sai. "Classification of Arrhythmia Beats Using Optimized K-Nearest Neighbor Classifier." In Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6081-5_31.

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Qaisar, Saeed Mian, Moez Krichen, and Fatma Jallouli. "Multirate ECG Processing and k-Nearest Neighbor Classifier Based Efficient Arrhythmia Diagnosis." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51517-1_29.

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Huang, Hao, Jintao Lv, Yu Pu, Yuxuan Wang, and Junjiang Zhu. "Multi-label Diagnosis Algorithm for Arrhythmia Diseases Based on Improved Classifier Chains." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7207-1_10.

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Mohanty, Monalisa, Asit Kumar Subudhi, Pradyut Kumar Biswal, and Sukanta Sabut. "An Efficient Classifier-Based Approach for Early Arrhythmia Detection with Feature Reduction Using Ranker Search Algorithm." In Lecture Notes in Networks and Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2774-6_38.

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Shet, Milan S., Minal Patel, Aakarsh Rao, Chethana Kantharaj, and K. V. Suma. "ECG Arrhythmia Classification Using R-Peak Based Segmentation, Binary Particle Swarm Optimization and Absolute Euclidean Classifier." In Advances in Intelligent Systems and Computing. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0740-5_37.

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Yaghouby, F., and A. Ayatollahi. "An Arrhythmia Classification Method Based on Selected Features of Heart Rate Variability Signal and Support Vector Machine-Based Classifier." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_512.

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Singstad, Bjørn Jostein, Bendik Steinsvåg Dalen, Sandhya Sihra, Nickolas Forsch, and Samuel Wall. "Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers." In Computational Physiology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05164-7_8.

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AbstractImmature cardiomyocytes, such as those obtained by stem cell differentiation, have been shown to be useful alternatives to mature cardiomyocytes, which are limited in availability and difficult to obtain, for evaluating the behaviour of drugs for treating arrhythmia. In silico models of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) can be used to simulate the behaviour of the transmembrane potential and cytosolic calcium under drug-treated conditions. Simulating the change in action potentials due to various ionic current blocks enables the approximation of drug behaviour. We used eight machine learning classification models to predict partial block of seven possible ion currents $$ (\textit{I}_{\textit{CaL}},\textit{I}_{\textit{Kr}},\textit{I}_{\textit{to}},\textit{I}_{\textit{K1}},\textit{I}_{\textit{Na}},\textit{I}_{\textit{NaL}} and \textit{I}_{\textit{Ks}}) $$ in a simulated dataset containing nearly 4600 action potentials represented as a paired measure of transmembrane potential and cytosolic calcium. Each action potential was generated under 1 $$ \textit{H}_{\textit{z}} $$ pacing. The Convolutional Neural Network outperformed the other models with an average accuracy of predicting partial ionic current block of 93% in noise-free data and 72% accuracy with 3% added random noise. Our results show that $$ \textit{I}_{\textit{CaL}} $$ and $$ \textit{I}_{\textit{Kr}} $$ current block were classified with high accuracy with and without noise. The classification of $$ \textit{I}_{\textit{to}} $$ , $$ \textit{I}_{\textit{K1}} $$ and $$ \textit{I}_{\textit{Na}} $$ current block showed high accuracy at 0% noise, but showed a significant decrease in accuracy when noise was added. Finally, the accuracy of $$ \textit{I}_{\textit{NaL}} $$ and $$ \textit{I}_{\textit{Ks}} $$ classification were relatively lower than the other current blocks at 0% noise and also showed a significant drop in accuracy when noise was added. In conclusion, these machine learning methods may present a pathway for estimating drug response in adult phenotype cardiac systems, but the data must be sufficiently filtered to remove noise before being used with classifier algorithms.
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Arias-García, Santiago, José Hernández-Torruco, Betania Hernández-Ocaña, and Oscar Chávez-Bosquez. "Cardiac Arrhythmia Identification Using Feature Selection and Rule-Based Classifiers." In IFMBE Proceedings. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62520-6_14.

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Sahoo, Prakash Chandra, and Binod Kumar Pattanayak. "Classification of Arrhythmia ECG Signal Using EMD and Rule-Based Classifiers." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_36.

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Conference papers on the topic "Arrhythmia classifier"

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Wei, Guangshun, Guanglin Deng, Xuecong Lu та Bing Li. "A 1.83 μ J High-Robust Cardiac Health Monitoring with Adaptive-Threshold QRS Detector and Hybrid Neural Network Arrhythmia Classifier". У 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024. https://doi.org/10.1109/biocas61083.2024.10798265.

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Lin, Jia Lun, Jin Yang Xia, and Xiao Ling Li. "Research on a CNN Based Clinical Electrocardiogram Classification Model." In 12th Annual International Conference on Material Science and Engineering. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-fj7a4x.

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Electrocardiogram (ECG) is the most commonly used diagnostic method for heart diseases such as arrhythmia. However, its inherent complexity, to some extent, reduces the accuracy of diagnosis. To quickly and automatically identify the type of arrhythmia, this paper constructs a clinical ECG classification model based on Convolutional Neural Network (CNN) to assist clinicians in analyzing ECG signals. The MIT-BIH ECG database is used as the research data source, and the heart beats are classified into 5 categories based on AAMI EC57 standard. 95% of the ECG data is randomly divided into training and testing sets, and the remaining 5% is used as the internal testing set. Based on the experimental outcomes, the model's accuracy exceeds 96%, indicating a commendable overall performance.
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Ozdemir, Ahmet Turan, Kenan Danisman, and Musa Hakan Asyali. "FPGA based arrhythmia classifier." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130253.

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Abayaratne, Himeshika, Shalindri Perera, Erandi De Silva, Pramadhi Atapattu, and Malitha Wijesundara. "A Real-Time Cardiac Arrhythmia Classifier." In 2019 National Information Technology Conference (NITC). IEEE, 2019. http://dx.doi.org/10.1109/nitc48475.2019.9114464.

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Sultana, Nasreen, and Yedukondalu Kamatham. "MSVM-based classifier for cardiac arrhythmia detection." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732229.

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Hammed, Norhan S., and Mohamed I. Owis. "Patient adaptable ventricular arrhythmia classifier using template matching." In 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2015. http://dx.doi.org/10.1109/biocas.2015.7348370.

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Ali, M. S. A. M., N. F. Shaari, N. Julai, et al. "Robust arrhythmia classifier using hybrid multilayered perceptron network." In 2013 IEEE 9th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2013. http://dx.doi.org/10.1109/cspa.2013.6530061.

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Park, Juyoung, Kuyeon Lee, and Kyungtae Kang. "Arrhythmia detection from heartbeat using k-nearest neighbor classifier." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732594.

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Catuneanu, Mircea-Traian, Mohammad Taghi Fathi, Ryan Hamerly, and Kambiz Jamshidi. "Serial Convolution-Based Optical Accelerator for ECG Arrhythmia Classifier." In Signal Processing in Photonic Communications. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/sppcom.2022.sptu4j.1.

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This study focuses on the design of photonic hardware for an ECG arrythmia classifier based on convolutional neural network using optical delay lines for the implementation of “multiply and accumulate” (MAC) operations.
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Xu, Yifan, and Hao Liu. "Lightweight Arrhythmia Classifier Using Hybrid Compressed Convolutional Neural Network." In 2023 11th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE, 2023. http://dx.doi.org/10.1109/icbcb57893.2023.10246560.

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Reports on the topic "Arrhythmia classifier"

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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), 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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