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Artykuły w czasopismach na temat „ELECTROCARDIOGRAM FEATURES”

Autor: Grafiati

Data publikacji: 11 września 2023

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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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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 extr

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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 th

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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

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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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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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Zawadzki, Jacek, Aleksandra Gajek, Jakub Adamowicz, et al. "The specific His-bundle pacing features in electrocardiogram." Journal of Electrocardiology 51, no. 6 (2018): 1171. http://dx.doi.org/10.1016/j.jelectrocard.2018.10.038.

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Zawadzki, Jacek, Aleksandra Gajek, Jakub Adamowicz, et al. "The specific His-bundle pacing features in electrocardiogram." Journal of Electrocardiology 53 (March 2019): e11. http://dx.doi.org/10.1016/j.jelectrocard.2019.01.040.

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Syarief, Mohammad, Mulaab Mulaab, and Husni Husni. "THE IMPACT OF FEATURE SELECTION ON THE PROBABILISTIC MODEL ON ARRHYTHMIA DIAGNOSIS." International Journal of Science, Engineering and Information Technology 6, no. 2 (2022): 296–302. http://dx.doi.org/10.21107/ijseit.v6i2.15265.

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Arrhythmia is a type of cardiac illness identified by an irregular heart rhythm that can be either too rapid or too slow. An electrocardiograph method is required to diagnose arrhythmia. Electrocardiogram, ECG, is the result of this Electrocardiograph process. The ECG is then utilized as a diagnostic tool for arrhythmia. Because the ECG data is so extensive, an adequate processing procedure is required. Understanding the ECG data can be done in various ways, one of which is classification. Naïve Bayes is a classification technique that can handle enormous amounts of data. ECG data has a lot of

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Gimayev, R. Kh, V. I. Ruzov, V. A. Razin, and E. E. Yudina. "Gender-age features of cardiac electrophysiological changes IN patients with arterial hypertension." "Arterial’naya Gipertenziya" ("Arterial Hypertension") 15, no. 1 (2009): 57–64. http://dx.doi.org/10.18705/1607-419x-2009-15-1-57-64.

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Aim. The research addresses gender-age features of cardiac electrophysiological changes in patients with arterial hypertension based on a standard electrocardiogram, high-frequency electrocardiogram and cardiointervalography data. Materials and methods. 171 patients with arterial hypertension (97 men and 74 women) aged between 30 -73 years were included. Standard 12- lead electrocardiogram, high-frequency electrocardiogram with the analysis of late potentials of atria (LPA) and ventricles (LPV), and cardiointervalography with an estimation of heart rate variability were performed in all patien

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Bernardini, Andrea, Lia Crotti, Iacopo Olivotto, and Franco Cecchi. "Diagnostic and prognostic electrocardiographic features in patients with hypertrophic cardiomyopathy." European Heart Journal Supplements 25, Supplement_C (2023): C173—C178. http://dx.doi.org/10.1093/eurheartjsupp/suad074.

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Abstract The standard 12-lead electrocardiogram (ECG) represents a cornerstone for the diagnosis and evaluation of hypertrophic cardiomyopathy (HCM), the most common genetically determined heart muscle disease, due to its cost-effectiveness and wide availability. The ECG may surprisingly look normal in 4–6% of adult patients, and in less than 3% of paediatric patients, but it is abnormal in the vast majority of the remaining patients. ‘Specific’ features comprise pathological Q-waves, deep S-waves in V1–V3, or high R-waves in V4–V6 due to left ventricular hypertrophy with T-wave (TW) depressio

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CLAYTON, R. H., A. MURRAY, and R. W. F. CAMPBELL. "Objective features of the surface electrocardiogram during ventricular tachyarrhythmias." European Heart Journal 16, no. 8 (1995): 1115–19. http://dx.doi.org/10.1093/oxfordjournals.eurheartj.a061055.

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Sree Janani, KK, and RS Sabeenian. "Transfer learning-based electrocardiogram classification using wavelet scattered features." Biomedical and Biotechnology Research Journal (BBRJ) 7, no. 1 (2023): 52. http://dx.doi.org/10.4103/bbrj.bbrj_341_22.

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Nagarakanti, Rangadham, and Kavin Raj. "Wide Complex Tachycardia in Arrhythmogenic Right Ventricular Cardiomyopathy: Electrocardiogramand Intracardiac Electrogram Features." Indian Journal of Clinical Cardiology 3, no. 1 (2022): 47–50. http://dx.doi.org/10.1177/26324636221080595.

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Kossaify, Antoine, Nadir Saoudi, and Sami Succar. "Electrocardiographic Characteristics of Ventricular Arrhythmia Originating from the Left Coronary Cusp." Case Reports in Medicine 2011 (2011): 1–2. http://dx.doi.org/10.1155/2011/935951.

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Aortic cusps originating arrhythmias are rare; they have special electrocardiogram features that help to locate the site of origin. We report on a 20-year-old male patient without structural heart disease presenting with accelerated idioventricular rhythm; electrocardiogram analysis was typical of left coronary cusp origin.

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HAGIWARA, YUKI, and OLIVER FAUST. "NONLINEAR ANALYSIS OF CORONARY ARTERY DISEASE, MYOCARDIAL INFARCTION, AND NORMAL ECG SIGNALS." Journal of Mechanics in Medicine and Biology 17, no. 07 (2017): 1740006. http://dx.doi.org/10.1142/s0219519417400061.

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In this study, we analyze nonlinear feature extraction methods in terms of their ability to support the diagnosis of coronary artery disease (CAD) and myocardial infarction (MI). The nonlinear features were extracted from electrocardiogram (ECG) signals that were measured from CAD patients, MI patients as well as normal controls. We tested 34 recurrence quantification analysis (RQA) features, 14 bispectrum, and 136 cumulant features. The features were extracted from 10,546 normal, 41,545 CAD, and 40,182 MI heart beats. The feature quality was assessed with Student’s [Formula: see text]-test an

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21

Hanis Hussin, Amerah, Ahmad Syukri Abdul Aziz, and Megat Syahirul Amin Megat Ali. "Profiling of Myocardial Infarction History from Electrocardiogram using Artificial Neural Network." International Journal of Engineering & Technology 7, no. 4.11 (2018): 236. http://dx.doi.org/10.14419/ijet.v7i4.11.20814.

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Myocardial infarction is an irreversible damage of heart muscle caused by prolonged oxygen deficiency. As a result, the presence of damaged tissue will alter the normal sinus rhythm. Hence, the paper proposes to profile history of myocardial infarction from electrocardiogram using artificial neural network. Data for anterior and inferior myocardial infarction, as well as healthy control is acquired from PTB Diagnostic ECG Database. Subsequently, QRS power ratio features for different frequency zones are extracted from the pre-processed electrocardiogram. Discriminative ability of the features

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Hanis Hussin, Amerah, Ahmad Syukri Abdul Aziz, and Megat Syahirul Amin Megat Ali. "Profiling of Myocardial Infarction History from Electrocardiogram using Artificial Neural Network." International Journal of Engineering & Technology 7, no. 4.11 (2018): 276. http://dx.doi.org/10.14419/ijet.v7i4.11.21392.

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Myocardial infarction is an irreversible damage of heart muscle caused by prolonged oxygen deficiency. As a result, the presence of damaged tissue will alter the normal sinus rhythm. Hence, the paper proposes to profile history of myocardial infarction from electrocardiogram using artificial neural network. Data for anterior and inferior myocardial infarction, as well as healthy control is acquired from PTB Diagnostic ECG Database. Subsequently, QRS power ratio features for different frequency zones are extracted from the pre-processed electrocardiogram. Discriminative ability of the features

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Ahmad Khorsheed, Eman. "Detection of Abnormal electrocardiograms Based on Various Feature Extraction methods." Academic Journal of Nawroz University 12, no. 3 (2023): 111–19. http://dx.doi.org/10.25007/ajnu.v12n3a1818.

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Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart obtained by placing various electrodes on specific areas of the subject's body surface. Abnormalities in a patient's ECG signal may indicate cardiac diseases that require immediate medical attention. As a result, detecting an abnormal ECG is critical for the patient's benefit. This work develops a method for classifying ECG signals as normal or abnormal. In this paper, we propose a method for detecting cardiac arrhythmias in electrocardiograms (ECG). In the first stage, the proposal focuses on various

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Kozhevnikova, Olga V., Eka A. Abashidze, Andrey P. Fisenko, et al. "Features of electrocardiogram in school-age children with COVID-19." Russian Pediatric Journal 24, no. 6 (2022): 372–80. http://dx.doi.org/10.46563/1560-9561-2021-24-6-372-380.

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Introduction. Currently, there is an increase in the incidence and an increase in the severity of the course of COVID-19 in children. The tropism of the SARS-CoV-2 virus to the cardiovascular system has been established, while post-COVID syndrome with various manifestations is recorded in 25% of recovered adolescents. The purpose of the work was to identify the features of the electrocardiogram (ECG) pattern in children hospitalized with a diagnosis of COVID-19. Results. Significant changes in the conductivity and activity of the left heart myocardium were found in COVID-19 patients with pneum

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Shuvalova, N. V., G. L. Drandrov, S. V. Lezhenina, et al. "SOME PHYSIOLOGICAL FEATURES OF ELECTROCARDIOGRAM INDICATORS IN ATHLETES IN ADOLESCENCE." Современные проблемы науки и образования (Modern Problems of Science and Education), no. 3 2020 (2020): 80. http://dx.doi.org/10.17513/spno.29902.

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Augustyniak, Piotr. "Adaptive Sampling of the Electrocardiogram Based on Generalized Perceptual Features." Sensors 20, no. 2 (2020): 373. http://dx.doi.org/10.3390/s20020373.

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A non-uniform distribution of diagnostic information in the electrocardiogram (ECG) has been commonly accepted and is the background to several compression, denoising and watermarking methods. Gaze tracking is a widely recognized method for identification of an observer’s preferences and interest areas. The statistics of experts’ scanpaths were found to be a convenient quantitative estimate of medical information density for each particular component (i.e., wave) of the ECG record. In this paper we propose the application of generalized perceptual features to control the adaptive sampling of a

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Thach, Terence Huy, Sarah Blissett, and Matthew Sibbald. "Worked examples for teaching electrocardiogram interpretation: Salient or discriminatory features?" Medical Education 54, no. 8 (2020): 720–26. http://dx.doi.org/10.1111/medu.14066.

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Ibiyemi, T. S. "A Novel Data Compression Technique for Electrocardiogram Classification." Engineering in Medicine 15, no. 1 (1986): 35–38. http://dx.doi.org/10.1243/emed_jour_1986_015_010_02.

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A novel high data compression of ECG data in the measurement space by clipping the signal and using the zero-crossing intervals as features. This yields low-dimensional features sufficient and efficient for screening and some diagnostics. It is validated by experiment using ECG of an in vitro heart of a rat. This new idea is built around a Z-80 microprocessor.

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Belle, Ashwin, Rosalyn Hobson Hargraves, and Kayvan Najarian. "An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram." Computational and Mathematical Methods in Medicine 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/528781.

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This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been imp

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Jain, Paras, CH N. V. S. Praneeth, Iragavarapu Kannan, Potluri Harsha Sai, and Jaba Deva Krupa Abel. "Electrocardiogram Beat Classification Using Data Filtration Technique and Support Vector Machine." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3613–20. http://dx.doi.org/10.1166/jctn.2020.9240.

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This work addresses the automatic classification of arrhythmia beats into four generalized classes as described by the Association for the Advancement of Medical Instrumentation (AAMI) standard. We propose a method that includes time-series, statistical and frequency features of RR-interval, DWT, and EMD analysis of QRS morphology. Also, a data filtration technique using support vector selection and under-sampling is applied to find those features as well as data points having significant prediction capabilities. While testing the above combination on MIT-BIH arrhythmia database, adopting the

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31

Graupe, D., M. H. Graupe, Y. Zhong, and R. K. Jackson. "Blind adaptive filtering for non-invasive extraction of the fetal electrocardiogram and its non-stationarities." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 222, no. 8 (2008): 1221–34. http://dx.doi.org/10.1243/09544119jeim417.

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The objective is to extract automatically a beat-to-beat fetal electrocardiogram (fECG) from a maternal electrocardiogram (mECG) using surface electrodes placed on the maternal abdomen and to derive fetal PR, QT, QTc, and QS durations to allow early diagnosis and monitoring treatment of certain fetal cardiac disorders. mECG and abdominal noise in abdominal maternal recordings can be orders of magnitude stronger than the fECG signal and the P and T waves that are embedded in them. A two-stage blind adaptive filtering algorithm was used for fECG extraction, the first stage using frequency-domain

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Sayed Ismail, Sharifah Noor Masidayu, Nor Azlina Ab. Aziz, Siti Zainab Ibrahim, et al. "Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system." F1000Research 10 (May 30, 2022): 1114. http://dx.doi.org/10.12688/f1000research.73255.2.

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Background: The electrocardiogram (ECG) is a physiological signal used to diagnose and monitor cardiovascular disease, usually using 2- D ECG. Numerous studies have proven that ECG can be used to detect human emotions using 1-D ECG; however, ECG is typically captured as 2-D images rather than as 1-D data. There is still no consensus on the effect of the ECG input format on the accuracy of the emotion recognition system (ERS). The ERS using 2-D ECG is still inadequately studied. Therefore, this study compared ERS performance using 1-D and 2-D ECG data to investigate the effect of the ECG input

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Sayed Ismail, Sharifah Noor Masidayu, Nor Azlina Ab. Aziz, Siti Zainab Ibrahim, et al. "Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system." F1000Research 10 (November 4, 2021): 1114. http://dx.doi.org/10.12688/f1000research.73255.1.

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Background: The electrocardiogram (ECG) is a physiological signal used to diagnose and monitor cardiovascular disease, usually using ECG wave images. Numerous studies have proven that ECG can be used to detect human emotions using numerical data; however, ECG is typically captured as a wave image rather than as a numerical data. There is still no consensus on the effect of the ECG input format (either as an image or a numerical value) on the accuracy of the emotion recognition system (ERS). The ERS using ECG images is still inadequately studied. Therefore, this study compared ERS performance u

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Lee, Kar Fye Alvin, Elliot Chan, Josip Car, Woon-Seng Gan, and Georgios Christopoulos. "Lowering the Sampling Rate: Heart Rate Response during Cognitive Fatigue." Biosensors 12, no. 5 (2022): 315. http://dx.doi.org/10.3390/bios12050315.

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Cognitive fatigue is a mental state characterised by feelings of tiredness and impaired cognitive functioning due to sustained cognitive demands. Frequency-domain heart rate variability (HRV) features have been found to vary as a function of cognitive fatigue. However, it has yet to be determined whether HRV features derived from electrocardiogram data with a low sampling rate would remain sensitive to cognitive fatigue. Bridging this research gap is important as it has substantial implications for designing more energy-efficient and less memory-hungry wearables to monitor cognitive fatigue. T

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Volobuev, A., P. Romanchuk, I. Davydkin, and M. Dmitrieva. "Some features of the reflection of myocardial ischemia on the electrocardiogram." Vrach 31, no. 10 (2020): 19–21. http://dx.doi.org/10.29296/25877305-2020-10-03.

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Li, Hongzu, and Pierre Boulanger. "Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features." Sensors 22, no. 7 (2022): 2467. http://dx.doi.org/10.3390/s22072467.

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Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients’ heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial produc

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Rohan, Remalli, D. Santhosh Kumar, and Srinivasa Rao Patri. "Various Methods for Identification of Obstructive Sleep Apnea Using Electrocardiogram Features." Journal of scientific research 64, no. 01 (2020): 169–277. http://dx.doi.org/10.37398/jsr.2020.640151.

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Nanjundegowda, Raghu, and Vaibhav Meshram. "Arrhythmia Detection Based on Hybrid Features of T-wave in Electrocardiogram." International Journal of Intelligent Engineering and Systems 11, no. 1 (2018): 153–62. http://dx.doi.org/10.22266/ijies2018.0228.16.

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NIEUWENHUIZEN, C. L. C., H. A. Ph HARTOG, and E. MATTHIJSSEN. "New diagnostic features in the four lead electrocardiogram of angina pectoris." Acta Medica Scandinavica 98, no. 6 (2009): 468–99. http://dx.doi.org/10.1111/j.0954-6820.1939.tb11023.x.

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Juanni, Hou, Dachun Yang, De Li, and Haifeng Pei. "GW27-e0593 New features of electrocardiogram in arrhythmogenic right ventricular cardiomyopathy." Journal of the American College of Cardiology 68, no. 16 (2016): C118. http://dx.doi.org/10.1016/j.jacc.2016.07.465.

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Xue, Joel. "New morphology features of pediatric long-QT electrocardiogram by signal decomposition." Journal of Electrocardiology 38, no. 4 (2005): 38–39. http://dx.doi.org/10.1016/j.jelectrocard.2005.06.053.

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Zadeh, Ataollah Ebrahim, Ali Khazaee, and Vahid Ranaee. "Classification of the electrocardiogram signals using supervised classifiers and efficient features." Computer Methods and Programs in Biomedicine 99, no. 2 (2010): 179–94. http://dx.doi.org/10.1016/j.cmpb.2010.04.013.

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Aarathi, S., and Dr S. Vasundra. "Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection." International Journal of Engineering and Advanced Technology 9, no. 1s5 (2019): 147–58. http://dx.doi.org/10.35940/ijeat.a1036.1291s519.

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Computer aided predictive analytics are vital in noncommunicable diseases. In particular, early diagnosis of arrhythmia (heart related disease) is crucial to prevent sudden deaths due to heart failure. The critical context to prevent deaths caused by arrhythmia is early prediction of the arrhythmia scope. The clinical experts widely consider the Electro Cardio Gram (ECG) report as primary parameter to scale the scope of arrhythmia. However, the diagnosis accuracy of clinical experts is highly correlate on their expertise. Unlike the other domains, the sensitivity that is the accuracy in diseas

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Rajani, A. "Denoising of ECG Signal using UFIR Smoothing with Notch Filter." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 2115–22. http://dx.doi.org/10.22214/ijraset.2021.39687.

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Abstract: The electrical activity of the heart is test with an electrocardiogram (ECG). The fundamental information for the taking decision about various types of heart diseases identified by electrocardiogram. There have been numerous attempts over decades to extract the characteristics of the heartbeat through ECG records with high accuracy and efficiency using a variety of strategies and techniques. In this paper a novel scheme is acquainted, the problem is solved by isolated time space using q-lag unbiased finite impulse response (UFIR), then the received time changing of optimal average h

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Vakhnenko, Yu V., I. E. Dorovskikh, E. N. Gordienko, and M. A. Chernykh. "Some topical aspects of the problem of "athlete’s heart" (review). Part II." Bulletin Physiology and Pathology of Respiration, no. 79 (April 2, 2021): 127–40. http://dx.doi.org/10.36604/1998-5029-2021-79-127-140.

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Electrocardiography occupies a special place among a significant list of other methods for diagnosing the pathology of the cardiovascular system of athletes. Often its results differ significantly from those in the general population, being a consequence of the adaptation of the heart to economical functioning at rest and super-intensive work in training and competitions. This review focuses on the features of the “athlete’s electrocardiogram (ECG)”. in particular, those changes that are not a reason for removing athletes from physical activity, but in combination with known factors can lead t

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Sharma, Pragya, Zijing Zhang, Thomas B. Conroy, Xiaonan Hui, and Edwin C. Kan. "Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor." Sensors 22, no. 20 (2022): 8047. http://dx.doi.org/10.3390/s22208047.

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This work presents a study on users’ attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram an

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Crescenzi, Cinzia, Elisa Silvetti, Fabiana Romeo, et al. "The electrocardiogram in non-ischaemic-dilated cardiomyopathy." European Heart Journal Supplements 25, Supplement_C (2023): C179—C184. http://dx.doi.org/10.1093/eurheartjsupp/suad043.

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Abstract This article summarizes the main electrocardiogram (ECG) findings in dilated cardiomyopathy (DCM) patients. Recent reports are described in the great ‘pot’ of DCM peculiar ECG patterns that are typical of specific forms of DCM. Patients with late gadolinium enhancement on CMR, who are at greatest arrhythmic risk, have also distinctive ECG features. Future studies in large DCM populations should evaluate the diagnostic and prognostic value of the ECG.

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Mohebbanaaz, Mohebbanaaz, Y. Padma Sai, and L. V. Rajani Kumari. "Detection of cardiac arrhythmia using deep CNN and optimized SVM." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 217. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp217-225.

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<span>Deep learning (DL) <span>has become a topic of study in various applications, including healthcare. Detection of abnormalities in an electrocardiogram (ECG) plays a significant role in patient monitoring. It is noted that a deep neural network when trained on huge data, can easily detect cardiac arrhythmia. This may help cardiologists to start treatment as early as possible. This paper proposes a new deep learning model adapting the concept of transfer learning to extract deep-CNN features and facilitates automated classification of electrocardiogram (ECG) into sixteen types

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SUCHETHA, M., and N. KUMARAVEL. "CLASSIFICATION OF ARRHYTHMIA IN ELECTROCARDIOGRAM USING EMD BASED FEATURES AND SUPPORT VECTOR MACHINE WITH MARGIN SAMPLING." International Journal of Computational Intelligence and Applications 12, no. 03 (2013): 1350015. http://dx.doi.org/10.1142/s1469026813500156.

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Electrocardiogram (ECG) signals represent a useful information source about the rhythm and the functioning of the heart. Any disturbance in the heart's normal rhythmic contraction is called an arrhythmia. Analysis of Electrocardiogram signals is the most effective available method for diagnosing cardiac arrhythmias. Computer based classification of ECG provides higher accuracy and offer a potential of an affordable cardiac abnormality mass screening. The empirical mode decomposition is performed on various arrhythmia signals and different levels of intrinsic mode functions (IMF) are obtained.

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Das, Manab Kumar, and Samit Ari. "ECG Beats Classification Using Mixture of Features." International Scholarly Research Notices 2014 (September 17, 2014): 1–12. http://dx.doi.org/10.1155/2014/178436.

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Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted featur

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