Journal articles: 'ELECTROCARDIOGRAM FEATURES'

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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 (December 28, 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 (July 26, 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 (April 28, 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 (June 1, 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 (October 26, 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 (August 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 (December 1, 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 (April 15, 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 (November 2010): 642–43. http://dx.doi.org/10.1016/j.jelectrocard.2010.10.009.

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Zawadzki, Jacek, Aleksandra Gajek, Jakub Adamowicz, Jacek Gajek, Agnieszka Sławuta, Piotr Strózik, Bartek Skonieczny, and Agnieszka Szczepaniak. "The specific His-bundle pacing features in electrocardiogram." Journal of Electrocardiology 51, no. 6 (November 2018): 1171. http://dx.doi.org/10.1016/j.jelectrocard.2018.10.038.

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Zawadzki, Jacek, Aleksandra Gajek, Jakub Adamowicz, Jacek Gajek, Agnieszka Sławuta, Piotr Strózik, Bartek Skonieczny, and Agnieszka Szczepaniak. "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 (July 31, 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 characteristics, which makes classification more difficult. Feature selection can be used to eliminate non-essential features from a dataset. This research aimed to determine the feature selection’s impact on the Naïve Bayes classification. It was proven by increased accuracy by 4%, precision by 0.13, recall by 0.13, and f-measure by 0.14. The computation time was 0.03 seconds faster. The highest performance was obtained by classification with 80 features. The accuracy was 93%, precision and recall were 0.45, f-measure was 0.42, and computation time was 0.10 seconds.

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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 (February 28, 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 patients. Results. It is stated, that in patients with arterial hypertension, processes of cardiac electrophysiological changes are gender-and-age dependent. In our study women showed a longer QT interval as compared to men, but QT interval prolongation did not depend on the stage of the disease. No essential sexual distinctions were found in QT interval dispersion. LPA were registered more often in male patients, while there was no significant difference in LPV registration. It is worth noting that discussed changes are observed in patients with arterial hypertension, while no significant difference was found in patients without cardiac pathology. Therefore, men show a higher sympathetic activity influencing heart rate in comparison with women. Increased QT interval duration and dispersion was found in older patients with arterial hypertension, while no distinctions in LPA and LPV registration were observed.

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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 (April 26, 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) depression or negative TWs. Negative giant TWs are often found in apical HCM. However, in many patients, the ECG may only show non-specific ST–T changes with diphasic or flat TWs. An isolated inverted TW in lateral leads (usually aVL) may be the only marker for HCM in some patients. Electrocardiogram helps to diagnose sarcomeric HCM and distinguish it from different phenocopies, such as cardiac amyloidosis, glycogen storage, or Fabry disease. Electrocardiogram may also have a prognostic role, identifying high-risk features that could impact the clinical outcome.

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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 (August 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 (February 21, 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 (November 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 and the [Formula: see text]-value was used for feature ranking. We found that nonlinear features can effectively represent the physiological realities of the human heart.

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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 (October 2, 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 is assessed using k-nearest neighbor. The best combination of features with 99.7% testing accuracy is the power ratio composite that combines both low-frequency and mid-frequency information. An intelligent profiling model is successfully developed using the composite features and an optimized artificial neural network. The model was able to identify between different electrocardiogram groups with overall accuracy of 98.4% and mean squared error of less than 0.1. Conclusively, the proposed signal processing approach has provided an improved alternative to the established methods from literature.

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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 (October 2, 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 is assessed using k-nearest neighbor. The best combination of features with 99.7% testing accuracy is the power ratio composite that combines both low-frequency and mid-frequency information. An intelligent profiling model is successfully developed using the composite features and an optimized artificial neural network. The model was able to identify between different electrocardiogram groups with overall accuracy of 98.4% and mean squared error of less than 0.1. Conclusively, the proposed signal processing approach has provided an improved alternative to the established methods from literature.

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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 (July 2, 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 feature extractor methods. The Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and Improved complete ensemble empirical mode decomposition with adaptive noise were the feature extraction techniques evaluated (ICEEMDAN). The PCA method is then used to reduce the number of features. Finally, for classification, the Support Vector Machine (SVM) was used, which was trained using the features extracted in the first stage. The proposed models are tested using datasets from MIT-BIH arrhythmia and PTB Diagnostics. The experimental results show that using 3-PCs with the DWT method produces better results than the other methods, which achieve 98.7% in terms of accuracy.

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Kozhevnikova, Olga V., Eka A. Abashidze, Andrey P. Fisenko, Elina E. Akhmedova, Olga S. Logacheva, Anton S. Balabanov, Aleksandra E. Paltseva, et al. "Features of electrocardiogram in school-age children with COVID-19." Russian Pediatric Journal 24, no. 6 (January 19, 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 pneumonia and respiratory failure. Pronounced changes in ECG indices were found in children of senior school age who were admitted for treatment 2.4 times more often than other children. Proarrhythmogenic ECG indices in children were detected in severe COVID-19 - with community-acquired pneumonia (ΔQTc, QTcmin) and respiratory failure (TpTe/QTmax). These ECG changes, combined with the trend in inflammation markers (an increase in the C-reactive protein level and a decrease in the number of lymphocytes) in children with a moderate course of COVID-19 may be a sign of the involvement of the myocardium in an infectious inflammatory process. This suggests that the effect on the myocardium is exerted by systemic inflammation and not by the hemodynamic overload of the right heart, which is expected in pulmonary pathology. Conclusion. The obtained data indicate the need for dynamic ECG monitoring during the acute stage of the disease and rehabilitation of children who suffered from COVID-19.

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Shuvalova, N. V., G. L. Drandrov, S. V. Lezhenina, A. V. Karpunina, A. V. Moskovskiy, K. A. Malova, and A. V. Rybin. "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 (January 9, 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 digital ECG. Firstly, based on temporal distribution of the information density, local ECG bandwidth is estimated and projected to the actual positions of components in heartbeat representation. Next, the local sampling frequency is calculated pointwise and the ECG is adaptively low-pass filtered in all simultaneous channels. Finally, sample values are interpolated at new time positions forming a non-uniform time series. In evaluation of perceptual sampling, an inverse transform was used for the reconstruction of regularly sampled ECG with a percent root-mean-square difference (PRD) error of 3–5% (for compression ratios 3.0–4.7, respectively). Nevertheless, tests performed with the use of the CSE Database show good reproducibility of ECG diagnostic features, within the IEC 60601-2-25:2015 requirements, thanks to the occurrence of distortions in less relevant parts of the cardiac cycle.

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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 (March 23, 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 (January 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 implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

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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 (August 1, 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 inter-patient paradigm, we achieved 70%, 99.79%, 64.5%, and 80.55% Se and 61.76%, 94.64%, 83.22%, and 77.48% PPV for F, N, SVEB, and VEB classes respectively. Further, the proposed method reduced the classifier’s complexity through feature selection and computation time by data reduction while maintaining the generalization capability of the model. Another finding includes the significant contribution that RR-interval, 180–360 Hz and 0–45 Hz band power, and non-linear statistical characteristics have in distinguishing the arrhythmia classes. The feature and data selection criterion used is F -score and one-class classification by RBF-SVM respectively. The classifier used for building the final model is SVM with the cubic kernel.

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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 (November 1, 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 electrocardiogram features and the second considering time-domain features. Three channels of abdominal recordings were obtained from 12 patients at 20–40 weeks of gestation. In each case beat-to-beat unaveraged fECGs were isolated. The combined filter allowed identification of diagnostically important PR, QT, and RR durations. Comparison with synthetic data is also included.

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Sayed Ismail, Sharifah Noor Masidayu, Nor Azlina Ab. Aziz, Siti Zainab Ibrahim, Sophan Wahyudi Nawawi, Salem Alelyani, Mohamed Mohana, and Lee Chia Chun. "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 format on the ERS. Methods: This study employed the DREAMER dataset, which contains 23 ECG recordings obtained during audio-visual emotional elicitation. Numerical data was converted to ECG images for the comparison. Numerous approaches were used to obtain ECG features. The Augsburg BioSignal Toolbox (AUBT) and the Toolbox for Emotional feature extraction from Physiological signals (TEAP) extracted features from numerical data. Meanwhile, features were extracted from image data using Oriented FAST and rotated BRIEF (ORB), Scale Invariant Feature Transform (SIFT), KAZE, Accelerated-KAZE (AKAZE), Binary Robust Invariant Scalable Keypoints (BRISK), and Histogram of Oriented Gradients (HOG). Dimension reduction was accomplished using linear discriminant analysis (LDA), and valence and arousal were classified using the Support Vector Machine (SVM). Results: The experimental results show 1-D ECG-based ERS achieved 65.06% of accuracy and 75.63% of F1 score for valence, and 57.83% of accuracy and 44.44% of F1-score for arousal. For 2-D ECG-based ERS, the highest accuracy and F1-score for valence were 62.35% and 49.57%; whereas, the arousal was 59.64% and 59.71%. Conclusions: The results indicate that both inputs work comparably well in classifying emotions, which demonstrates the potential of 1-D and 2-D as input modalities for the ERS.

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Sayed Ismail, Sharifah Noor Masidayu, Nor Azlina Ab. Aziz, Siti Zainab Ibrahim, Sophan Wahyudi Nawawi, Salem Alelyani, Mohamed Mohana, and Lee Chia Chun. "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 using ECG image and ECG numerical data to determine the effect of the ECG input format on the ERS. Methods: This study employed the DREAMER dataset, which contains 23 ECG recordings obtained during audio-visual emotional elicitation. Numerical data was converted to ECG images for the comparison. Numerous approaches were used to obtain ECG features. The Augsburg BioSignal Toolbox (AUBT) and the Toolbox for Emotional feature extraction from Physiological signals (TEAP) extracted features from numerical data. Meanwhile, features were extracted from image data using Oriented FAST and rotated BRIEF (ORB), Scale Invariant Feature Transform (SIFT), KAZE, Accelerated-KAZE (AKAZE), Binary Robust Invariant Scalable Keypoints (BRISK), and Histogram of Oriented Gradients (HOG). Dimension reduction was accomplished using linear discriminant analysis (LDA), and valence and arousal were classified using the Support Vector Machine (SVM). Results: The experimental results indicated that numerical data achieved arousal and valence accuracy of 69% and 79%, respectively, which was greater than those of image data. For ECG images, the highest accuracy for arousal was 58% percent; meanwhile, the valence was 63%. Conclusions: The finding showed that numerical data provided better accuracy for ERS. However, ECG image data which shows positive potential and can be considered as an input modality for the ERS.

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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 (May 10, 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. This study aimed to examine (1) the level of agreement between frequency-domain HRV features derived from lower and higher sampling rates, and (2) whether frequency-domain HRV features derived from lower sampling rates could predict cognitive fatigue. Participants (N = 53) were put through a cognitively fatiguing 2-back task for 20 min whilst their electrocardiograms were recorded. Results revealed that frequency-domain HRV features derived from sampling rate as low as 125 Hz remained almost perfectly in agreement with features derived from the original sampling rate at 2000 Hz. Furthermore, frequency domain features, such as normalised low-frequency power, normalised high-frequency power, and the ratio of low- to high-frequency power varied as a function of increasing cognitive fatigue during the task across all sampling rates. In conclusion, it appears that sampling at 125 Hz is more than adequate for frequency-domain feature extraction to index cognitive fatigue. These findings have significant implications for the design of low-cost wearables for detecting cognitive fatigue.

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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 (March 23, 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 product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.

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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 (February 28, 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 (April 24, 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 (October 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 (October 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 (August 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 (December 30, 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 disease-prone is very much crucial in clinical practices. Particularly, the accuracy and sensitivity are more vital in computer-aided heart disease prediction methods. Hence, the recent research contributions are quantifying the possibilities of optimizing machine-learning approaches to achieve significance in computer-aided methods to perform predictive analysis on arrhythmia detection. Regarding this context, this manuscript is defining a Regression Heuristics by Tridimensional Features of the electrocardiogram reports, which has intended to perform arrhythmia prediction. The experimental study evincing the significance of the proposed model that scaled against the contemporary methods.

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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 (December 31, 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 horizon for the shape of the ECG signal. A complete statistical analysis is furnished by normalized histogram and statistical classifiers, P wave features extraction based on the detected fiducial points is deliberated. In this concept by utilizing QRS detection, morphological top-bottom hat transformation and notch filters is ameliorated PSNR and latency constraints, furnishes high accuracy and reduced elapsed time. Keywords: Electrocardiogram (ECG) denoising, unbiased finite impulse response (UFIR) filtering, P wave feature extraction, normalized histogram, QRS complex detection.

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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 to more serious changes up to sudden cardiac death. Fatal rhythm disorders in athletes are described, possible in Wolff-Parkinson-White syndrome, syndrome of ion channel pathology, arrhythmogenic dysplasia of the right ventricle, etc. Particular attention is paid to arrhythmia due to connective tissue dysplasia syndrome. Knowledge of these issues is necessary to choose the right tactics for an athlete with changes to the electrocardiogram and in the protocol of daily Holter monitoring of the electrocardiogram, and a doctor related to sports medicine should be aware of the features of “electrophysiological remodeling” of the athlete’s heart, normal and pathological “sports electrocardiogram”, about conditions accompanied with the development of serious rhythm disorders and algorithms for examining the cardiovascular system of the athlete.

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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 (October 21, 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 and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user’s baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively

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Crescenzi, Cinzia, Elisa Silvetti, Fabiana Romeo, Annamaria Martino, Edoardo Bressi, Germana Panattoni, Matteo Stefanini, et al. "The electrocardiogram in non-ischaemic-dilated cardiomyopathy." European Heart Journal Supplements 25, Supplement_C (April 26, 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 (October 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 of ECG beats using an optimized support vector machine (SVM). The proposed strategy begins with gathering ECG datasets, removal of noise from ECG signals, and extracting beats from denoised ECG signals. Feature extraction is done using ResNet18 via concept of transfer learning. These extracted features are classified using optimized SVM. These methods are evaluated and tested on the MIT-BIH arrhythmia database. Our proposed model is effective compared to all State of Art Techniques with an accuracy of 98.70%.</span></span>

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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 (September 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. Singular value decomposition (SVD) is used to extract features from the IMF and classification is performed using support vector machine. This method is more efficient for classification of ECG signals and at the same time provides good generalization properties.

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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 feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

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Journal articles on the topic 'ELECTROCARDIOGRAM FEATURES'

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