Relevant bibliographies by topics / ECG segmentation

Academic literature on the topic 'ECG segmentation'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "ECG segmentation"

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Gacek, A., and W. Pedrycz. "A genetic segmentation of ECG signals." IEEE Transactions on Biomedical Engineering 50, no. 10 (October 2003): 1203–8. http://dx.doi.org/10.1109/tbme.2003.816074.

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Kornev, V. P., and V. A. Tatsenko. "Multiscale wavelet analysis in ECG segmentation problem." Electronics and Communications 18, no. 3 (July 7, 2013): 38–42. http://dx.doi.org/10.20535/2312-1807.2013.18.3.158453.

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Malali, Aman, Srinidhi Hiriyannaiah, Siddesh G.M., Srinivasa K.G., and Sanjay N.T. "Supervised ECG wave segmentation using convolutional LSTM." ICT Express 6, no. 3 (September 2020): 166–69. http://dx.doi.org/10.1016/j.icte.2020.04.004.

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Beraza, Idoia, and Iñaki Romero. "Comparative study of algorithms for ECG segmentation." Biomedical Signal Processing and Control 34 (April 2017): 166–73. http://dx.doi.org/10.1016/j.bspc.2017.01.013.

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Preethi, S., and M. Jayasheela M.Jayasheela. "VLSI Implementation of Segmentation of Single Channel ECG." International Journal of Computer Applications 90, no. 13 (March 26, 2014): 27–30. http://dx.doi.org/10.5120/15781-4515.

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Yamaguchi, H., O. Uenaka, T. Nakamura, and K. Fujikawa. "Evaluation of Pulmonary MRA Using ECG Triggered k-space Segmentation." Japanese Journal of Radiological Technology 51, no. 8 (1995): 957. http://dx.doi.org/10.6009/jjrt.kj00001352532.

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Sayadi, O., and M. B. Shamsollahi. "A model-based Bayesian framework for ECG beat segmentation." Physiological Measurement 30, no. 3 (February 25, 2009): 335–52. http://dx.doi.org/10.1088/0967-3334/30/3/008.

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Teplitzky, Benjamin Adam, Mike McRoberts, and Peter J. Schwartz. "B-PO02-185 DEEP LEARNING FOR ECG WAVEFORM SEGMENTATION." Heart Rhythm 18, no. 8 (August 2021): S173—S174. http://dx.doi.org/10.1016/j.hrthm.2021.06.438.

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Aspuru, Javier, Alberto Ochoa-Brust, Ramón Félix, Walter Mata-López, Luis Mena, Rodolfo Ostos, and Rafael Martínez-Peláez. "Segmentation of the ECG Signal by Means of a Linear Regression Algorithm." Sensors 19, no. 4 (February 14, 2019): 775. http://dx.doi.org/10.3390/s19040775.

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The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.
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Bailey, Ben, and Saeed Babaeizadeh. "Record segmentation to speed up long-term ECG analysis algorithms." Journal of Electrocardiology 69 (November 2021): 86. http://dx.doi.org/10.1016/j.jelectrocard.2021.11.016.

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Dissertations / Theses on the topic "ECG segmentation"

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Varejão, Andreão Rodrigo. "Segmentation de battements ECG par approche markovienne : application à la détection d'ischémies." Evry, Institut national des télécommunications, 2004. http://www.theses.fr/2004TELE0004.

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L'enregistrement ambulatoire continu de l'électrocardiogramme par la méthode de Holter (ECG ambulatoire) pourvoit des informations pour le dépistage de l'ischémie myocardique pour des patients atteints d'une maladie coronarienne. Dans ce contexte, nous proposons un système automatique d'analyse d'ECG ambulatoire basé sur la fusion d'une approche markovienne et d'une approche heuristique capable de détecter des épisodes ischémiques. L'approche markovienne permet d'extraire du signal ECG les informations nécessaires à l'analyse du décalage du segment ST. Notre approche est capable de prendre en compte des morphologies complexes grâce à une modélisation individuelle des ondes P, QRS et T par des modèles de Markov spécifiques. De plus, une stratégie originale d'apprentissage non supervisée permet d'adapter les paramètres du modèle markovien au signal de la personne. Pour obtenir une classification en terme de pathologie, nous avons ajouté des règles qui s'appliquent aux informations extraites du signal par l'approche markovienne. Nous avons aussi exploré la fusion des informations obtenues sur plusieurs dérivations produisant ainsi des résultats plus fiables. Finalement, notre système a été validé sur deux bases d'ECG ambulatoires. La performance a été évaluée pour différents problèmes : détection des complexes QRS, segmentation précise des ondes P et T, et du complexe QRS, détection des battements ventriculaires et détection des épisodes ischémiques. Les résultats permettent de mettre en valeur l'intérêt de la modélisation proposée et se situent favorablement par rapport à l'état de l'art
Ambulatory electrocardiography (AECG) provides precise and rich information from the clinical point of view for the diagnostic of cardiac diseases and particularly myocardial ischemia in patients with coronary disease. Early detection of myocardial ischemia allows fast diagnostic and makes treatment more effective. Ischemic episodes are detected through the ST-segment deviation function, wich is built after analysis of each heartbeat. In this context, we propose a system combining a Markovian approach and a heuristic approach to perform automatic ischemic episode detection. Our markovian approach extracts from the ECG signal the information needed to perform ST-sergment deviation analysis. It is able to take into account complex morphologies thanks to the use of individual HMM to model each beat waveform (P, QRS and T). In addition, our original non supervised training strategy provides HMM parameter adaptation to the ECG signal of each patient. To classify the ECG signal in terms of a specific abnormality, we added a set of rules to manage the information extracted from the signal. We also explored the information fusion obtained from different leads yielding to more reliable detection results. Finally, we assessed our system performance over two AECG databases. Different problems were concerned QRS complex detection, waveform sergmentation precision, premature ventricular contraction beat detection and ischemic episode detection. All results attest the interest in the approach proposed and compare favourably to the state of the art
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Velychko, O., and K. Kampos. "Application of wavelet transformation for QRS-complex definition." Thesis, НТУ "ХПІ", 2019. http://openarchive.nure.ua/handle/document/9278.

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Method of QRS-definition based on the wavelet transformation had been developed and realized in Matlab. The ECG-signal had been transformed by the complex Morlet-wavelet. Principle of QRS borders is based on the features of regular and irregular components of the certain wavelet spectrum.
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Lin, Chao. "P and T wave analysis in ECG signals using Bayesian methods." Phd thesis, Toulouse, INPT, 2012. http://oatao.univ-toulouse.fr/8990/1/lin.pdf.

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This thesis studies Bayesian estimation/detection algorithms for P and T wave analysis in ECG signals. In this work, different statistical models and associated Bayesian methods are proposed to solve simultaneously the P and T wave delineation task (determination of the positions of the peaks and boundaries of the individual waves) and the waveform-estimation problem. These models take into account appropriate prior distributions for the unknown parameters (wave locations and amplitudes, and waveform coefficients). These prior distributions are combined with the likelihood of the observed data to provide the posterior distribution of the unknown parameters. Due to the complexity of the resulting posterior distributions, Markov chain Monte Carlo algorithms are proposed for (sample-based) detection/estimation. On the other hand, to take full advantage of the sequential nature of the ECG, a dynamic model is proposed under a similar Bayesian framework. Sequential Monte Carlo methods (SMC) are also considered for delineation and waveform estimation. In the last part of the thesis, two Bayesian models introduced in this thesis are adapted to address a specific clinical research problem referred to as T wave alternans (TWA) detection. One of the proposed approaches has served as an efficient analysis tool in the Endocardial T wave Alternans Study (ETWAS) project in collaboration with St. Jude Medical, Inc and Toulouse Rangueil Hospital. This project was devoted to prospectively assess the feasibility of TWA detection in repolarisation on EGM stored in ICD memories.
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Opravilová, Kamila. "Segmentace signálu EKG na základě kvality odhadnuté z akcelerometrických dat a komprese kvalitních segmentů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400966.

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This diploma thesis is devoted to segmentation of ECG signal based on its quality and compression of quality segments suitable for diagnostics (in telemedicine). A completely new approach is to use accelerometer data to estimate ECG signal quality. This is possible thanks to the Bittium Faros mobile recorder. It records both the ECG signal motion – accelerometric data. A total of 34 features were extracted from accelerometric data. Using these features the predictive model was taught to classify the ECG signal into 3 quality groups according to the level of noise. Quality segments were compressed. The wavelet transform in combination with high-frequency bands zeroing and length encoding was used as a compression method.
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Zobačová, Barbora. "Segmentace signálů EKG na základě jejich kvality." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377765.

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This semestral thesis deals with methods for continuous estimation of the quality of the ECG signal. The theoretical part includes the functional anatomy of the heart, the basics of electrocardiography, the types of noise that can be found in the ECG records, and a description of several methods for the continuous estimation of the ECG signal quality. Next here are some approaches to segmenting ECG signals based on their quality. The practical part deals with the implementation of two methods. The first method is the SNR estimation method based on the Wiener filter. The second method is the method of segmentation of ECG signals based on their quality. Both methods were tested on artificial and real signals.
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Kubát, Milan. "Borcení časové osy v oblasti biosignálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220845.

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This work is dedicated to dynamic time warping in biosignal processing, especially it´s application for ECG signals. On the beginning the theoretical notes about cardiography are summarized. Then, the DTW analysis follows along with conditions and demands assessments for it’s successful application. Next, several variants and application possibilities are described. The practical part covers the design of this method, the outputs comprehension, settings optimization and realization of methods related with DTW
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Löfhede, Johan. "The EEG of the neonatal brain : classification of background activity." Doctoral thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3533.

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The brain requires a continuous supply of oxygen and nutrients, and even a short period of reduced oxygen supply can cause severe and lifelong consequences for the affected individual. The unborn baby is fairly robust, but there are of course limits also for these individuals. The mostsensitive and most important organ is the brain. When the brain is deprivedof oxygen, a process can start that ultimately may lead to the death of braincells and irreparable brain damage. This process has two phases; one more orless immediate and one delayed. There is a window of time of up to 24 hourswhere action can be taken to prevent the delayed secondary damage. One recently clinically available technique is to reduce the metabolism and thereby stop the secondary damage in the brain by cooling the baby.It is important to be able to quickly diagnose hypoxic injuries and to followthe development of the processes in the brain. For this, the electroencephalogram (EEG) is an important tool. The EEG is a voltage signal that originates within the brain and that can be recorded easily andnon-invasively at bedside. The signals are, however, highly complex and require special competence to interpret, a competence that typically is not available at the intensive care unit, and particularly not continuously day and night. This thesis addresses the problem of automatic classification ofneonatal EEG and proposes methods that would be possible to use in bedside monitoring equipment for neonatal intensive care units.The thesis is a compilation of six papers. The first four deal with the segmentation of pathological signals (burst suppression) from post-asphyctic full term newborn babies. These studies investigate the use of various classification techniques, using both supervised and unsupervised learning.In paper V the scope is widened to include both classification of pathologicalactivity versus activity found in healthy babies as well as application of thesegmentation methods on the parts of the EEG signal that are found to be of the pathological type. The use of genetic algorithms for feature selection isalso investigated. In paper VI the segmentation methods are applied onsignals from pre-term babies to investigate the impact of a certain medication on the brain.The results of this thesis demonstrate ways to improve the monitoring of the brain during intensive care of newborn babies. Hopefully it will someday be implemented in monitoring equipment and help to prevent permanent brain damage in post asphyctic babies.
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Hodulíková, Tereza. "Analýza EEG během anestezie." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220369.

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This master's thesis deals with the method of functional examination of brain electric activity. In the first part is description of central nervous system, method of electroencephalography and possible connections. Furthermor the project involves characteristic of EEG signal and its artifacts. It also includes signal processing and list of symptoms, which will be used for an analysis of the EEG during anesthesia. The second part of thesis involves development of application, which allow viewing and proccesing of EEG signal. In conclusion of thesis is carried out unequal segmentation and statistical processing.
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Unakafov, Anton M. [Verfasser]. "Ordinal-patterns-based segmentation and discrimination of time series with applications to EEG data / Anton M. Unakafov." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2015. http://d-nb.info/1071509675/34.

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Al, Madi Naser S. "A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1416868268.

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Books on the topic "ECG segmentation"

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Parker, Philip M. The 2007 Report on Dried Egg Whites: World Market Segmentation by City. ICON Group International, Inc., 2006.

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The Breeding Habits and the Segmentation of the egg of the Pipefish. Franklin Classics, 2018.

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Rothbard, Nancy P., and Ariane Ollier-Malaterre. Boundary Management. Edited by Tammy D. Allen and Lillian T. Eby. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199337538.013.5.

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Boundary management is an important area within the field of work–life research. It is a set of cognitions and strategies by which people manage the critical boundaries between their multiple life domains. In this chapter, we embed this construct in its historical context from the industrial revolution to the present day. We review research that has accumulated on the different types of boundaries (e.g., spatial, temporal, relational, cognitive), the different dimensions of boundary management (segmentation versus integration preferences and behaviors, permeability, and control), as well as its directionality and time horizon. This chapter also presents a chronological approach that invites us to revisit the value of segmentation against of backdrop of an increasing blurring of the boundaries in the new world of work.
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Sonnentag, Sabine, Dana Unger, and Elisabeth Rothe. Recovery and the Work–Family Interface. Edited by Tammy D. Allen and Lillian T. Eby. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199337538.013.37.

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Recovery after work is essential in order to stay energetic when facing work demands. This chapter discusses how unwinding and restoration processes after work relate to experiences at the work–family interface. Empirical studies have shown that specific activities (e.g., sport and exercise) and experiences (e.g., psychological detachment from work during nonwork time) are important to achieve recovery. Boundary management strategies at the work–family interface (e.g., a preference for segmentation) predict recovery experiences. Moreover, recovery experiences moderate the relationship between work–family conflict (particularly family-to-work conflict) and strain outcomes. This chapter presents directions for future research and highlights practical implications by describing what individuals, families, and organizations can do in order to foster recovery processes.
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Davis, George C., and Elena L. Serrano. Production and Profit Beyond the Farm Gate. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199379118.003.0013.

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Chapter 13 first looks at how changes at one level in the food supply chain may affect prices and quantities at another level via profit maximization. The chapter then considers firms that are closer to the consumer (e.g., restaurants) who will often be able to set their own prices and consider the analytics of profit maximization under this scenario. Utilizing this framework, the chapter considers the question: Are healthier foods more or less profitable than unhealthy foods? This leads naturally to a discussion of market segmentation, the limit of the market, and the distribution of healthy and unhealthy foods in the food system. As there are many calls for food firms to be more socially responsible and offer healthier foods, the chapter utilizes the framework to explore the implications of corporate social responsibility and how compatible that idea is with profit maximization.
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Book chapters on the topic "ECG segmentation"

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Vullings, H. J. L. M., M. H. G. Verhaegen, and H. B. Verbruggen. "ECG segmentation using time-warping." In Advances in Intelligent Data Analysis Reasoning about Data, 275–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052847.

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Moskalenko, Viktor, Nikolai Zolotykh, and Grigory Osipov. "Deep Learning for ECG Segmentation." In Studies in Computational Intelligence, 246–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30425-6_29.

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Bognár, Gergő, and Sándor Fridli. "ECG Segmentation by Adaptive Rational Transform." In Computer Aided Systems Theory – EUROCAST 2019, 347–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45096-0_43.

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Sevak, Mayur M., Dhruv Patel, Parikshit Mishra, and Vatsal Shah. "Abnormality Detection Based on ECG Segmentation." In Communications in Computer and Information Science, 89–99. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3660-8_9.

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Böck, Carl, Michael Lunglmayr, Christoph Mahringer, Christoph Mörtl, Jens Meier, and Mario Huemer. "Global Decision Making for Wavelet Based ECG Segmentation." In Computer Aided Systems Theory – EUROCAST 2017, 179–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74727-9_21.

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Frénay, B., G. de Lannoy, and M. Verleysen. "Emission Modelling for Supervised ECG Segmentation using Finite Differences." In IFMBE Proceedings, 1212–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_290.

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Karim, Rashed, Henry Chubb, Wieland Staab, Shadman Aziz, R. James Housden, Mark O’Neill, Reza Razavi, and Kawal Rhode. "Left Atrial Segmentation from 3D Respiratory- and ECG-gated Magnetic Resonance Angiography." In Functional Imaging and Modeling of the Heart, 155–63. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20309-6_18.

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Duque-Mejía, C., M. A. Becerra, C. Zapata-Hernández, C. Mejia-Arboleda, A. E. Castro-Ospina, E. Delgado-Trejos, Diego H. Peluffo-Ordóñez, P. Rosero-Montalvo, and Javier Revelo-Fuelagán. "Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study." In Intelligent Information and Database Systems, 269–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14799-0_23.

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Qiu, Xi, Shen Liang, and Yanchun Zhang. "Simultaneous ECG Heartbeat Segmentation and Classification with Feature Fusion and Long Term Context Dependencies." In Advances in Knowledge Discovery and Data Mining, 371–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_28.

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Moreta-Martínez, Rafael, Gonzalo Vegas Sánchez-Ferrero, Lasse Andresen, Jakob Qvortrup Holsting, and Raúl San José Estépar. "Multi-cavity Heart Segmentation in Non-contrast Non-ECG Gated CT Scans with F-CNN." In Thoracic Image Analysis, 14–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62469-9_2.

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Conference papers on the topic "ECG segmentation"

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Espiritu-Santo-Rincon, Antonio, and Cuauhtemoc Carbajal-Fernandez. "ECG feature extraction via waveform segmentation." In 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) (Formerly known as ICEEE). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5608655.

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Kovacs, Peter, Carl Bock, Jens Meier, and Mario Huemer. "ECG segmentation using adaptive hermite functions." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335601.

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Thomas, Julien, Cedric Rose, and Francois Charpillet. "A Multi-HMM Approach to ECG Segmentation." In 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06). IEEE, 2006. http://dx.doi.org/10.1109/ictai.2006.17.

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Tate, Jess, Nejib Zemzemi, Wilson Good, Peter van Dam, Dana Brooks, and Rob MacLeod. "Effect of Segmentation Variation on ECG Imaging." In 2018 Computing in Cardiology Conference. Computing in Cardiology, 2018. http://dx.doi.org/10.22489/cinc.2018.374.

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Wu Shi and Igor Kheidorov. "Hybrid hidden Markov models for ECG segmentation." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5583618.

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Wang, Jianjian, and Zheying Li. "An ECG Segmentation Model Used for Signal Generator." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.132.

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Sereda, Iana, Sergey Alekseev, Aleksandra Koneva, Roman Kataev, and Grigory Osipov. "ECG Segmentation by Neural Networks: Errors and Correction." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852106.

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Chompusri, Yotaka, Kobchai Dejhan, Surapun Yimman, and Noppadol Charbkaew. "Modified beat segmentation for DTW based ECG compression." In TENCON 2014 - 2014 IEEE Region 10 Conference. IEEE, 2014. http://dx.doi.org/10.1109/tencon.2014.7022337.

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El-Saadawy, Hadeer, Manal Tantawi, Howida A. Shedeed, and Mohamed F. Tolba. "Electrocardiogram (ECG) Classification Based On Dynamic Beats Segmentation." In the 10th International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2908446.2908452.

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Li, Huaming, and Jindong Tan. "Body Sensor Network Based ECG Segmentation and Analysis." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353505.

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