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Automatic ECG Artefact Removal from EEG Signals

R eferences [1] Dirlich, G., Vogl, L., Plaschke, M., Strian, F. (1997). Cardiac field effects on the EEG. Electroencephalography and Clinical Neurophysiology , 102 (4), 307-315. [2] Park, H.J., Jeong, D.U., Park, K.S. (2002). Automated detection and elimination of periodic ECG artefacts in EEG using the energy interval histogram method. IEEE Transactions on Biomedical Engineering , 49 (12), 1526-1533. [3] Suresh, H.N., Puttamadappa, C. (2008). Removal of EMG and ECG artifacts from EEG based on real time recurrent learning algorithm

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Physiological ECG Value for Polish Half-Bred Anglo-Arab Horses

.A.: Exercise training-induced hypervolemia in the horse. Med Sci Sports Exer 1987, 19 , 21-27. 13. Menzies-Go N.: ECG interpretation in the horse. Equine Pract 2001, 9 , 454-459. 14. Mokhber Dezfouli M.R., Fakor S., Bahari A.A., Alidadi N., Rezakhani A.: Electrocardiographic parameters of the Kurd horse using base apex lead. J Appl Anim Res 2006, 30 , 89-92. 15. Nielsen K., Vibe-Petersen G.: Relationship between QRS-duration (heart score) and racing performance in trotters. Equine Vet J 1980, 2 , 81-84. 16

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Simplified 2D Bidomain Model of Whole Heart Electrical Activity and ECG Generation

] Trudel, M.-C. et al. (2004). Simulation of QRST integral maps with a membrane-based computer heart model employing parallel processing. IEEE Transactions of Biomedical Engineering, 51 (8), 1319-1329. [5] Jacquemet, V., van Oosterom, A., Vesin, J., Kappenberger, L. (2006). Analysis of electrocardiograms during atrial fibrillation. IEEE Engineering in Medicine and Biology Magazine, 25 (6), 79-88. [6] Pfeifer, B. et al. (2007). A training whole-heart model for simulating propagation and ECG patters. Biomedical Signal Processing and Control

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An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

of sudden cardiac death are a consequence of arrhythmogenic cardiac disorders. Using surface electrodes, a simple recording of the heart electrical activity is defined as an electrocardiogram (ECG) [ 3 ]. Electrocardiogram is an efficient non-invasive tool that has different employment in biomedical sciences such as diagnosing rhythm disturbances, evaluating the heartbeat rate, checking the cardiac rhythm, biometric identification, emotion identification, etc. To avoid CVD deaths that are caused by long-term effect of the cardiac arrhythmias occurring inside

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Development of Underwear with Integrated 12 Channel ECG for Men and Women

://tu-dresden.de/die_tu_dresden/fakultaeten/fakultaet_maschinenwesen/itb/forschung/forschungsthemen/smart_textiles/index_html. [10] Ohmatex. Brochure, http://www.ohmatex.dk/pdfer/Ohmatex_brochure.pdf. [11] Novonic, R. Eine Marke der W. Zimmermann GmbH & Co. KG, http://www.novonic.de/web/novonic.nsf/id/pa_home_d.html. [12] Aumann S, Trummer S, Brücken A, Ehrmann A, Büsgen A. Conceptual design of a sensory shirt for fire-fighters. Text. Res. J., 2014; 84: 1661-1665. [13] Canart V, Flacke E, Aumann S, Trummer S, Brücken A, Ehrmann A, Büsgen A. ECG sensor technology for fire fighters. Technical Textiles, 2014; 57: E61-E62 [14

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Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

death in sports. Journal of Athletic Training, 47 (1), 96-118. [16] Jan, M., Trobec, R. (2017). Long-term follow-up case study of atrial fibrillation after treatment. In 40th International Convention on Information and Communication Technologies, Electronics and Microelectronics (MIPRO) , Opatija, Croatia. IEEE, 297-302. [17] Širaiy, B., Stanič, J.U., Poplas, A.S., Katkič, Z. (2018). Impact assessment of the morning gymnastics “1000 movements” via ECG and sport test. In 41st International Convention on Information and Communication Technologies

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Synchronization Protocols in Spanish Merino Sheep: Reduction in Time to Estrus by the Addition of eCG to a Progesterone-Based Estrus Synchronization Protocol

.K. (2009). Estrus synchronization in sheep with synthetic progestagens. Trop. Anim. Health Prod., 41: 1521-1524. Azawi O.I., Al-Mola M.K. (2010). A study on superovulation using FSH and eCG in Awassi ewes. Trop. Anim. Health Prod., 42: 799-801. Barrett D.M.,Bartlewski P.M.,Batista-Arteaga M.,Symington A.,Ra wling s N.C. (2004). Ultrasound and endocrine evaluation of the ovarian response to a single dose of 500 IU of eCG following a 12-day treatment with progestogen-releasing intravaginal sponges in the breeding and nonbreeding seasons in

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The Early Repolarization ECG Pattern – An Update

early repolarization pattern: a consensus paper. J Am Coll Cardiol. 2015; 66: 470-477. 5. Maury P, Rollin A. Prevalence of early repolarization/J wave patterns in the normal population. J Electrocardiol. 2013; 46: 411–416. 6. Noseworthy PA, Tikkanen JT, Porthan K, et al. The early repolarization pattern in the general population. J Am Coll Cardiol. 2011; 57: 2284-2289. 7. Surawicz B, Parikh SR. Prevalence of male and female patterns of early ventricular repolarization in the normal ECG of males and females from childhood to old age. J Am Coll Cardiol

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A Fast and Simple Adaptive Bionic Wavelet Transform: ECG Baseline Shift Correction

References 1. Clifford, G. D., F. Azuaje, P. McSharry. Advanced Methods and Tools for ECG Data Analysis. Linear Filtering Methods. Chapter 5. Boston, Artech House Publishers, 2006. 2. Thakor, N. V., Y. S. Zhu. Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection. – IEEE Transactions on Biomedical Engineering, Vol. 38 , 1991, No 8, pp. 785-794. 3. Clifford, G. D., F. Azuaje, P. Mc Sharry. Advanced Methods and Tools for ECG Data Analysis. Artech House Publishers, 2006, pp. 164-167. 4. Daqrouq, K

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Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

References Can Ye, Vijaya Kumar, B.V.K. and Coimbra, M.T. (2012). Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification, Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), Arlington, VA, USA, pp. 2428-2431. de Chazal, P., O’Dwyer, M. and Reilly, R.B. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Transactions on Biomedical Engineering 51 (7): 1196-1206. Chi

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