Relevant bibliographies by topics / Heart beat classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Heart beat classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Heart beat classification"

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Khazaee, Ali. "Heart Beat Classification Using Particle Swarm Optimization." International Journal of Intelligent Systems and Applications 5, no. 6 (2013): 25–33. http://dx.doi.org/10.5815/ijisa.2013.06.03.

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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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Doquire, G., G. de Lannoy, D. François, and M. Verleysen. "Feature Selection for Interpatient Supervised Heart Beat Classification." Computational Intelligence and Neuroscience 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/643816.

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Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task. In practice, over 200 features are often considered, and the features retained in the final model are either chosen using domain knowledge or an exhaustive search in the feature sets without evaluating the relevance of each individual feature included in the classifier. As a consequence, the results obtai
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Bunting, Karina V., Simrat K. Gill, Alice Sitch, et al. "Improving the diagnosis of heart failure in patients with atrial fibrillation." Heart 107, no. 11 (2021): 902–8. http://dx.doi.org/10.1136/heartjnl-2020-318557.

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ObjectiveTo improve the echocardiographic assessment of heart failure in patients with atrial fibrillation (AF) by comparing conventional averaging of consecutive beats with an index-beat approach, whereby measurements are taken after two cycles with similar R-R interval.MethodsTransthoracic echocardiography was performed using a standardised and blinded protocol in patients enrolled in the RATE-AF (RAte control Therapy Evaluation in permanent Atrial Fibrillation) randomised trial. We compared reproducibility of the index-beat and conventional consecutive-beat methods to calculate left ventric
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PAUL, BABY, K. T. SHANAVAZ, and P. MYTHILI. "A NEW OPTIMIZED WAVELET TRANSFORM FOR HEART BEAT CLASSIFICATION." Journal of Mechanics in Medicine and Biology 15, no. 05 (2015): 1550081. http://dx.doi.org/10.1142/s0219519415500815.

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A method for automatic classification of Arrhythmias from Electrocardiogram based on features generated from a new Continuous Wavelet Transform (CWT) is presented in this paper. The classification performance was studied using the most commonly available database, the MIT-BIH arrhythmia database. The new wavelet for classification was evolved using Genetic Algorithm (GA). The optimum wavelet for classification was obtained after several runs of the GA algorithm. The class labeling was followed according to the Association for the Advancement of Medical Instrumentation (AAMI). The wavelet scale
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de Oliveira, L. S. C., R. V. Andreao, and M. Sarcinelli Filho. "Bayesian Network with Decision Threshold for Heart Beat Classification." IEEE Latin America Transactions 14, no. 3 (2016): 1103–8. http://dx.doi.org/10.1109/tla.2016.7459585.

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Iftikhar, Faiza, Ayesha Shams, and Arfa Dilawari. "Rhythm Disorders Heart Beat Classification of an Elec-trocardiogram Signal." International Journal of Computer Applications 39, no. 11 (2012): 38–44. http://dx.doi.org/10.5120/4867-7292.

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Dilmac, Selim, and Mehmet Korurek. "ECG heart beat classification method based on modified ABC algorithm." Applied Soft Computing 36 (November 2015): 641–55. http://dx.doi.org/10.1016/j.asoc.2015.07.010.

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Liu, Zhishuai, Guihua Yao, Qing Zhang, Junpu Zhang, and Xueying Zeng. "Wavelet Scattering Transform for ECG Beat Classification." Computational and Mathematical Methods in Medicine 2020 (October 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/3215681.

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An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG hea
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Yuan, Yongjie, Yongjun Zhang, Junyuan Wang, and Ping Fang. "Classification of Electrocardiogram of Congenital Heart Disease Patients by Neural Network Algorithms." Scientific Programming 2021 (August 31, 2021): 1–8. http://dx.doi.org/10.1155/2021/3801675.

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The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted bet
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Dissertations / Theses on the topic "Heart beat classification"

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Potočňák, Tomáš. "Klasifikace srdečních cyklů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219953.

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The aim of this work was to develop the method for classification of ECG beats into two classes, namely ischemic and non-ischemic beats. Heart beats (P-QRS-T cycles) selected from animals orthogonal ECGs were preprocessed and used as the input signals. Spectral features vectors (values of cross spectral coherency), principal component and HRV parameters were derived from the beats. The beats were classified using feedforward multilayer neural network designed in Matlab. Classification performance reached the value approx. from 87,2 to 100%. Presented results can be suitable in future studies a
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Konz, Maximilian. "Räumlich-zeitliche Dynamik der laserinduzierten Hsp70-Expression in einem humanen Hautexplantatmodell." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-213660.

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Die Narbenbildung des Hautorgans stellt für die gegenwärtige Medizin weiterhin eine schwierige Aufgabe dar. Die frühzeitige Beeinflussung des Wundheilungspro- zesses hin zu einer verminderten oder narbenlosen Heilung scheint von entschei- dender Bedeutung. Ein vielversprechender Ansatz ist die präoperative Laserthe- rapie und dadurch erzeugte Hitzeschockantwort. Auf molekulare Ebene kommt es u.a. zur Expression von Hitzeschockproteine. Die vorliegende in-vitro Studie beschäftigte sich mit der laserinduzierten Hochregulation des Hitzeschockproteins 70 in den epidermalen Schichten. Hierfür wurde
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Hoffmann, Sandra. "Charakterisierung kardialer β-Adrenozeptoren in B.U.T. Big 6 Puten in Abhängigkeit von Alter und Geschlecht: Bedeutung für die Entstehung kardiovaskulärer Erkrankungen". Doctoral thesis, 2016. https://ul.qucosa.de/id/qucosa%3A15611.

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Beyer, Rico. "Winkelaufgelöste Messungen der spezifischen Wärme des organischen Supraleiters beta''-(ET)2SF5CH2CF2SO3." Doctoral thesis, 2012. https://tud.qucosa.de/id/qucosa%3A26632.

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Im Jahr 1964 wurde eine Theorie der Supraleitung vorgestellt, welche Cooper-Paarbindungen mit nichtverschwindendem Gesamtimpuls berücksichtigt. Sie wird nach den maßgeblich beteiligten Physikern P. Fulde, R. A. Ferrell, A. I. Larkin und Y. N. Ovchinnikov als FFLO-Supraleitung bezeichnet [1, 2]. Aufgrund recht anspruchsvoller Voraussetzungen kommen nur wenige Festkörper-Systeme in Frage, die eine FFLO-Phase ausbilden könnten. Im Jahr 2007 konnte R. Lortz durch Messungen der spezifischen Wärme an dem organischen Supraleiter kappa-(ET)2Cu(NCS)2 einen soliden Nachweis für eine weitere thermodynami
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Book chapters on the topic "Heart beat classification"

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de Oliveira, Lorena Sophia Campos, Rodrigo Varejão Andreão, and Mário Sarcinelli-Filho. "The Use of Bayesian Networks for Heart Beat Classification." In Advances in Experimental Medicine and Biology. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-79100-5_12.

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Rahman, Md Mahfujur, Shamim Al Mamun, M. Shamim Kaiser, Md Shahidul Islam, and Md Arifur Rahman. "Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4673-4_47.

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de Lannoy, Gael, Damien François, Jean Delbeke, and Michel Verleysen. "Weighted SVMs and Feature Relevance Assessment in Supervised Heart Beat Classification." In Biomedical Engineering Systems and Technologies. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18472-7_17.

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Arif, Muhammad, Fayyaz A. Afsar, Muhammad Usman Akram, and Adnan Fida. "Arif Index for Predicting the Classification Accuracy of Features and Its Application in Heart Beat Classification Problem." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_45.

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Malik, Shahid Ismail, and Imran Siddiqi. "Classification of Normal Heart Beats Using Spectral and Nonspectral Features for Phonocardiography Signals." In Applications of Intelligent Technologies in Healthcare. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96139-2_2.

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Kanani, Pratik, and Mamta Chandraprakash Padole. "ECG Image Classification Using Deep Learning Approach." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch016.

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Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.
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Jha, Chandan Kumar, and Maheshkumar H. Kolekar. "Classification and Compression of ECG Signal for Holter Device." In Biomedical Signal and Image Processing in Patient Care. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2829-6.ch004.

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ECG signal processing for holter monitoring of heart patients is still exploratory. Many signal processing techniques have been evolved for classification and compression of ECG signal. Despite an increase in research in this area, many challenges remain in designing an efficient classification and compression algorithm for ECG signal. These challenges include classification accuracy, good compression ratio with acceptable diagnostic quality etc. This chapter addresses a classification and a compression algorithm based on discrete wavelet transform. Classification algorithm uses discrete wavelet transform based feature to classify abnormal heart beat from ECG signal. Support vector machine is used as a classifier to detect abnormal heartbeat. The compression algorithm utilizes discrete wavelet transform and run-length encoding as a compression tool. Proposed classification and compression algorithms can be employed in monitoring of cardiac patients using holter device.
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Jha, Chandan Kumar, and Maheshkumar H. Kolekar. "Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7796-6.ch004.

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Abnormal behavior of heart muscles generates irregular heartbeats which are collectively known as arrhythmia. Classification of arrhythmia beats plays a prominent role in electrocardiogram (ECG) analysis. It is widely used in online and long-term patient monitoring systems. This chapter reports a classification technique to recognize normal (N) and five arrhythmia beats (i.e., left bundle branch block [LBBB], right bundle branch block [RBBB], premature ventricular contraction [V], paced [P], and atrial premature contraction [A]). The technique utilizes features of heartbeats extracted by the wavelet multi-resolution analysis. The feature vectors are used to train and test the classifier based on the support vector machine which has been emerged as a benchmark in machine learning classifier. It accomplishes the beat classification very efficiently. ECG records of the MIT-BIH arrhythmia database are utilized to acquire the different types of heartbeats. Performance of the proposed classifier outperforms the contemporary arrhythmia beats classification techniques.
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Hernandez-Matamoros, Andres, Hamido Fujita, and Hector Perez-Meana. "Recognition of Heartbeat Categories Applying a Novel Preprocessing Scheme and Neural Networks." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200562.

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Heart disease is the principal cause of mortality and the major contributor to reduced quality of life. The electrocardiogram is used to monitor the cardiovascular system. The correct classification of the beats in electrocardiograms gives an opportunity to have treatment more focused. The manual analysis of the ECG signals faces different problems. For this reason, automated diagnosis systems are fed by ECG signals to detect anomalies. In this paper, we propose a method based on a novel preprocessing approach and neural networks for the classification of heartbeats which is able to classify five categories of arrhythmias in accordance with the AAMI standard. The preprocessing stage allows each beat to have “P wave-R peak-R peak” information. We evaluated the proposed method on the MIT-BIH database, which is one of the most used databases. According to the results, the proposed approach is able to make predictions with the average accuracies of 97%. The average accuracies are compared to different approaches that use different preprocessing and classifier stages. Our approach is superior to that of most of them.
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Roos-Hesselink, Jolien W., and Lucia Baris. "Contraception and pregnancy." In ESC CardioMed. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0180.

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An increasing number of women with congenital heart disease reach childbearing age. Contraception and pregnancy should be discussed routinely. The risk of cardiovascular complications during pregnancy and peripartum depends on the underlying congenital heart disease malformation and risk stratification can be based on the modified World Health Organization classification. Multidisciplinary risk assessment and counselling is essential throughout pregnancy, and this should include an individualized delivery plan. Vaginal delivery is best for the majority of mothers with congenital heart disease and caesarean section is reserved for obstetric complications or a few high-risk cardiac conditions.
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Conference papers on the topic "Heart beat classification"

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Popov, Anton, Oleg Panichev, Yevgeniy Karplyuk, Yaroslav Smirnov, Sebastian Zaunseder, and Volodymyr Kharytonov. "Heart beat-to-beat intervals classification for epileptic seizure prediction." In 2017 Signal Processing Symposium (SPSympo). IEEE, 2017. http://dx.doi.org/10.1109/sps.2017.8053647.

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Sadiq, Ismail, and Shoab Ahmad Khan. "Heart Beat Classification of ECGs Using Morphology and Beat Intervals." In 2011 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2011. http://dx.doi.org/10.1109/icbbe.2011.5780248.

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Ahmad, Zeeshan, Anika Tabassum, Ling Guan, and Naimul Khan. "ECG Heart-Beat Classification Using Multimodal Image Fusion." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414709.

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"FEATURE SELECTION FOR INTER-PATIENT SUPERVISED HEART BEAT CLASSIFICATION." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003163200670073.

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"FEATURE RELEVANCE ASSESSMENT IN AUTOMATIC INTER-PATIENT HEART BEAT CLASSIFICATION." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002690900130020.

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Surrel, Gregoire, Francisco Rincon, Srinivasan Murali, and David Atienza. "Real-time probabilistic heart beat classification and correction for embedded systems." In 2015 Computing in Cardiology Conference (CinC). IEEE, 2015. http://dx.doi.org/10.1109/cic.2015.7408611.

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Liu, Si, Enqi Zhan, Yang Wang, and Jianbin Zheng. "Heart beat classification and matching recognition based on hierarchical dynamic time warping." In Fourth International Workshop on Pattern Recognition, edited by Zhenxiang Chen, Xudong Jiang, and Guojian Chen. SPIE, 2019. http://dx.doi.org/10.1117/12.2540503.

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Deriche, Mohamed, Saeed Aljabri, Mohammed Al-Akhras, Mohammed Siddiqui, and Naziha Deriche. "An Optimal Set of Features for Multi-Class Heart Beat Abnormality Classification." In 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2019. http://dx.doi.org/10.1109/ssd.2019.8893151.

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Senapati, Manoj Kumar, Mrutyunjaya Senapati, and Srinivasu Maka. "Cardiac Arrhythmia Classification of ECG Signal Using Morphology and Heart Beat Rate." In 2014 Fourth International Conference on Advances in Computing and Communications (ICACC). IEEE, 2014. http://dx.doi.org/10.1109/icacc.2014.20.

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"ASSESSMENT AND COMPARISON OF TIME REALIGNMENT METHODS FOR SUPERVISED HEART BEAT CLASSIFICATION." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001434602390244.

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