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Gotowa bibliografia na temat „ELECTROCARDIOGRAM FEATURES”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "ELECTROCARDIOGRAM FEATURES"

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 reference system and the position of the heart’s electrical axis, since integral electrocardiological signals allow to obtain more accurate results compared to conventional electrocardiogram, because they take into account the individual characteristics of patients, a wide variety of electrocardiogram waveforms and complexes, which is associated not only with the presence of pathological processes in the myocardium, but also with the position of the electrical axis of the heart, in particular, the electrocardiogram will not register a low-amplitude P wave in the II department in the case of a horizontal electrical axis, but it will be clearly visible on the integral signal. To implement the method proposed in the article, a program was written in the MATLAB language, , the high speed of computation and good optimization of which allow to obtain results much faster and more accurate than using traditional approaches, and using the MATLAB Runtime library, which does not require licensing and is distributed free of charge, it was possible to provide more economical development, as well as to implement interaction with popular operating systems, which makes it more accessible and versatile. Verification of the results was carried out using a database of electrocardiograms, which were recorded using a transtelephone digital 12-channel electrocardiological complex “Telecard”, which is part of the medical diagnostic complex “TREDEX”. The paper shows that the proposed method for the synthesis of an integral signal with locally concentrated features will improve the quality of morphological analysis of electrocardiograms in cardiological decision support systems.

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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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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 extracted from one-dimensional electrocardiogram sequences, ignoring the frequency domain features of electrocardiogram signals. Therefore, developing an automatic arrhythmia detection algorithm based on 12-lead electrocardiogram with high accuracy and strong generalization ability is still challenging. In this paper, a multimodal feature fusion model based on the mechanism is developed. This model utilizes a dual channel deep neural network to extract different dimensional features from one-dimensional and two-dimensional electrocardiogram time–frequency maps, and combines attention mechanism to effectively fuse the important features of 12-lead, thereby obtaining richer arrhythmia information and ultimately achieving accurate classification of nine types of arrhythmia signals. This study used electrocardiogram signals from a mixed dataset to train, validate, and evaluate the model, with an average of F1 score and average accuracy reached 0.85 and 0.97, respectively. Experimental results show that our algorithm has stable and reliable performance, so it is expected to have good practical application potential.

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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 the ECG recordings of normal subjects prepared at IIT(BHU). The result demonstrates that the derived eigenbeat features from proposed ECG characterization perform better and achieve the recognition accuracy of 91.42% and 95.55% on the subjects of MIT-BIH Arrhythmia database and IIT(BHU) database, respectively.

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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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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 patterns of the electrocardiogram elements as features.Design/methodology/approachConsidering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.FindingsFrom the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.Originality/valueThe authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.

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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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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 space exploration, and anomaly detection were conducted. We confirmed that CVAE features reflected the various types of ECG rhythms. Second, we applied CVAE features to new tasks as input data and CVAE weights to weight initialization for different models for transfer learning for the classification of 12 types of arrhythmias. The f1-score for arrhythmia classification with extreme gradient boosting was 0.86 using CVAE features only. The f1-score of the model in which weights were initialized with the CVAE encoder was 5% better than that obtained with random initialization. Unsupervised feature learning with CVAE can extract the characteristics of various types of ECGs and can be an alternative to the feature extraction method for ECGs.

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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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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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