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Relevant bibliographies by topics / ELECTROCARDIOGRAM FEATURES

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Academic literature on the topic 'ELECTROCARDIOGRAM FEATURES'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "ELECTROCARDIOGRAM FEATURES"

1

Filatova, Anna Yevhenivna, Anatoliy Ivanovych Povoroznyuk, Bohdan Petrovych Nosachenko, and Mohamad Fahs. "Synthesis of an integral signal for solving the problem of morphological analysis of electrocardiograms." Herald of Advanced Information Technology 5, no. 4 (2022): 263–74. http://dx.doi.org/10.15276/hait.05.2022.19.

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This work is devoted to solving the scientific and practical problem of morphological analysis of electrocardiograms based on an integral biomedical signal with locally concentrated features. In modern conditions of introduction of telemedicine in the health care system of Ukraine the creation of cardiological decision support systems based on automatic morphological analysis of electrocardiogram is of particular importance. The authors proposed a method for synthesizing an integral electrocardiogram in the frontal plane from all limb leads, taking into account the lead angle in the hexaxial r

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2

Madias, John E. "Electrocardiogram features predictive of takotsubo syndrome." Clinical Research in Cardiology 108, no. 2 (2018): 221. http://dx.doi.org/10.1007/s00392-018-1338-8.

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3

Yang, Xiao, and Zhong Ji. "Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram." Sensors 23, no. 9 (2023): 4372. http://dx.doi.org/10.3390/s23094372.

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Cardiovascular disease is one of the main causes of death worldwide. Arrhythmias are an important group of cardiovascular diseases. The standard 12-lead electrocardiogram signals are an important tool for diagnosing arrhythmias. Although 12-lead electrocardiogram signals provide more comprehensive arrhythmia information than single-lead electrocardiogram signals, it is difficult to effectively fuse information between different leads. In addition, most of the current researches working on automatic diagnosis of cardiac arrhythmias are based on modeling and analysis of single-mode features extr

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4

Singh, Yogendra Narain, and Sanjay Kumar Singh. "Identifying Individuals Using Eigenbeat Features of Electrocardiogram." Journal of Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/539284.

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The authors of this paper present a new method to characterize the electrocardiogram (ECG) for individual identification. We propose an ECG biometric system which is insensitive to noise signals and muscle flexure. The method utilizes the principal of linearly projecting the heartbeat features into a subspace of lower dimension using an orthogonal basis that represents the most significant features to distinguish the individuals. The performance of the proposed biometric system is evaluated on the subjects of both health statuses such as the ECG recordings of MIT-BIH Arrhythmia database and th

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5

Wu, Shun-Chi, Peng-Tzu Chen, and Jui-Hsuan Hsieh. "Spatiotemporal features of electrocardiogram for biometric recognition." Multidimensional Systems and Signal Processing 30, no. 2 (2018): 989–1007. http://dx.doi.org/10.1007/s11045-018-0593-1.

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6

Al-Yarimi, Fuad Ali Mohammed, Nabil Mohammed Ali Munassar, and Fahd N. Al-Wesabi. "Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction." Data Technologies and Applications 54, no. 5 (2020): 685–701. http://dx.doi.org/10.1108/dta-03-2020-0076.

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PurposeDigital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential

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7

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

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8

Jang, Jong-Hwan, Tae Young Kim, Hong-Seok Lim, and Dukyong Yoon. "Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder." PLOS ONE 16, no. 12 (2021): e0260612. http://dx.doi.org/10.1371/journal.pone.0260612.

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Most existing electrocardiogram (ECG) feature extraction methods rely on rule-based approaches. It is difficult to manually define all ECG features. We propose an unsupervised feature learning method using a convolutional variational autoencoder (CVAE) that can extract ECG features with unlabeled data. We used 596,000 ECG samples from 1,278 patients archived in biosignal databases from intensive care units to train the CVAE. Three external datasets were used for feature validation using two approaches. First, we explored the features without an additional training process. Clustering, latent s

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9

A. Elsayed, Hend, Ahmed F. Abed, and Shawkat K. Guirguis. "Comparative Features Extraction Techniques for Electrocardiogram Images Regression." Research Journal of Applied Sciences, Engineering and Technology 14, no. 4 (2017): 132–36. http://dx.doi.org/10.19026/rjaset.14.4156.

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10

Illig, David, Aaron Lewicke, and Stephanie Schuckers. "Electrocardiogram features for detection of abnormal cardiac events." Journal of Electrocardiology 43, no. 6 (2010): 642–43. http://dx.doi.org/10.1016/j.jelectrocard.2010.10.009.

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