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Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros

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Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros

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dc.contributor.author Mjahad, Azeddine es_ES
dc.contributor.author Rosado Muñoz, Alfredo es_ES
dc.contributor.author Bataller Mompeán, Manuel es_ES
dc.contributor.author Francés Víllora, Jose V. es_ES
dc.contributor.author Guerrero Martínez, Juan F. es_ES
dc.date.accessioned 2020-05-14T10:55:49Z
dc.date.available 2020-05-14T10:55:49Z
dc.date.issued 2017-12-05
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/143185
dc.description.abstract [ES] Este trabajo propone la detección de FV y su discriminación de TV y otros ritmos cardiacos basándose en la representación tiempo-frecuencia del ECG y su conversión en imágen como entrada a un clasificador de vecinos más cercanos (KNN) sin necesidad de extracción de parámetros adicionales. Tres variantes de datos de entrada al clasificador son evaluados. Los resultados clasifican la señal en cuatro clases diferentes: ’Normal’ para latidos con ritmo sinusal, ’FV’ para fibrilación ventricular, ’TV’ para taquicardia ventricular y ’Otros’ para el resto de ritmos. Los resultados para detección de FV mostraron 88,27% de sensibilidad y 98,22% de especificidad para la entrada de imágen equivalente reducida que es la más rápida computacionalmente a pesar de obtener resultados de clasificación ligeramente inferiores a las representaciones no reducidas. En el caso de TV, se alcanzó un 88,31% de sensibilidad y 98,80% de especificidad, un 98,14% de sensibilidad y 96,82% de especificidad para ritmo sinusal normal y 96,91% de sensibilidad con 99,06% de especificidad para la clase ’Otros’. Finalmente, se realiza una comparación con otros algoritmos. es_ES
dc.description.abstract [EN] This work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours - KNN) without any further signal parameter extraction or additional characteristics. For comparison purposes, three time-frequency variants are analysed: pseudo Wigner-Ville representation (RTF), grey-scale image obtained from RTF (IRTF), and reduced image from IRTF (reduced IRTF). Four types of rhythms (classes) are defined: ’Normal’ for sinus rhythm, ’VT’ for ventricular tachycardia, ’VF’ for ventricular fibrillation and ’Others’ for the rest of rhythms. Classification results for VF detection in case of reduced IRTF are 88.27% sensitivity and 98.22% specificity. In case of VT, 88.31% sensitivity and 98.80% specificity is obtained, 98.14% sensitivity and 96.82% specificity for normal rhythms, and 96.91% sensitivity and 99.06% specificity for other rhythms. Finally, results are compared with other authors. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Biomedical systems es_ES
dc.subject Electrocardiographic signals es_ES
dc.subject Time-frequency representation es_ES
dc.subject Non-stationary signals es_ES
dc.subject Image analysis es_ES
dc.subject Classification es_ES
dc.subject Sistemas biomédicos es_ES
dc.subject Señales Electrocardiográficas es_ES
dc.subject Representación tiempo-frecuencia es_ES
dc.subject Señales no estacionarias es_ES
dc.subject Análisis de imágenes es_ES
dc.subject Clasificación es_ES
dc.title Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros es_ES
dc.title.alternative Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2017.8833
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Mjahad, A.; Rosado Muñoz, A.; Bataller Mompeán, M.; Francés Víllora, JV.; Guerrero Martínez, JF. (2017). Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros. Revista Iberoamericana de Automática e Informática industrial. 15(1):124-132. https://doi.org/10.4995/riai.2017.8833 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2017.8833 es_ES
dc.description.upvformatpinicio 124 es_ES
dc.description.upvformatpfin 132 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 15 es_ES
dc.description.issue 1 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\8833 es_ES
dc.description.references Classen, T. A. C. M., Mecklenbrauker, W. F. G., 1980. The Wigner Distribution: A Tool for Time-Frequency Signal Analysis - Part 2: Discrete-Time Signals. Philips Journal of Research 35, 276-350. es_ES
dc.description.references Cohen, L., Jul 1989a. Time frequency distributions a review. Proceedings of the IEEE 77 (7), 941-981. https://doi.org/10.1109/5.30749 es_ES
dc.description.references Cohen, L., 1989b. Time-frequency distributions-a review. Proceeding of the IEEE 77 (7), 941-981. https://doi.org/10.1109/5.30749 es_ES
dc.description.references Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T., Ahmed, T., 2016. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine 127, 52- 63. https://doi.org/10.1016/j.cmpb.2015.12.024 es_ES
dc.description.references Hlawatsch, F., Boudreaux-Bartels, G. F., April 1992. Linear and quadratic time-frequency signal representations. IEEE Signal Processing Magazine 9 (2), 21-67. https://doi.org/10.1109/79.127284 es_ES
dc.description.references Ibaida, A., Khalil, I., Aug 2010. Distinguishing between Ventricular Tachycardia and Ventricular Fibrillation from Compressed ECG Signal in Wireless body Sensor Networks. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. pp. 2013-2016. https://doi.org/10.1109/IEMBS.2010.5627888 es_ES
dc.description.references Jekova, I., 2007a. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomedical Signal Processing and Control 2 (1), 25 - 33. https://doi.org/10.1016/j.bspc.2007.01.002 es_ES
dc.description.references Jekova, I., 2007b. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomedical Signal Processing and Control 2 (1), 25-33. https://doi.org/10.1016/j.bspc.2007.01.002 es_ES
dc.description.references Jekova, I., Krasteva, V., 2004. Real time detection of ventricular fibrillation and tachycardia. Physiological measurement 25 (5), 1167. https://doi.org/10.1088/0967-3334/25/5/007 es_ES
dc.description.references Jin, D., Dai, C., Gong, Y., Lu, Y., Zhang, L., Quan, W., Li, Y., 2017. Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis? Resuscitation 111, 48 - 54. https://doi.org/10.1016/j.resuscitation.2016.11.022 es_ES
dc.description.references Kabir, M. A., Shahnaz, C., 2012. Denoising of ecg signals based on noise reduction algorithms in emd and wavelet domain. Biomedical Signal Processing and Control l 7 (5). https://doi.org/10.1016/j.bspc.2011.11.003 es_ES
dc.description.references Kao, T.-P., Wang, J.-S., Lin, C.-W., Yang, Y.-T., Juang, F.-C., June 2012. Using bootstrap adaboost with knn for ecg-based automated obstructive sleep apnea detection. In: The 2012 International Joint Conference on Neural Networks (IJCNN). pp. 1-5. https://doi.org/10.1109/IJCNN.2012.6252716 es_ES
dc.description.references Kaur, L., Singh, V., May 2013. Ventricular Fibrillation Detection using Empirical Mode Decomposition and Approximate Entropy. International Journal of Emerging Technology and Advanced Engineering 3 (5), 260-268. es_ES
dc.description.references Kaur, M., Singh, B., Seema, 2011. Comparison of different approaches for removal of baseline wander from ecg signal. In: Proceedings of the International Conference &Workshop on Emerging Trends in Technology. ICWET'11. ACM, New York, NY, USA, pp. 1290-1294. https://doi.org/10.1145/1980022.1980307 es_ES
dc.description.references Labatut, V., Cherifi, H., May 2011. Accuracy measures for the comparison of classifiers. In: Ali, A.-D. (Ed.), The 5th International Conference on Information Technology. Al-Zaytoonah University of Jordan, amman, Jordan, pp.1,5. es_ES
dc.description.references Li, Q., Rajagopalan, C., Clifford, G. D., 2014. Ventricular fibrillation and tachycardia classification using a machine learning approach. Biomedical Engineering, IEEE Transactions on 61 (6), 1607-1613. es_ES
dc.description.references Mahmoud, S. S., Hussain, Z. M., Cosic, I., Fang, Q., 2006. Time-frequency analysis of normal and abnormal biological signals. Biomedical Signal Processing and Control 1 (1), 33 - 43. https://doi.org/10.1016/j.bspc.2006.02.001 es_ES
dc.description.references Martin, W., Flandrin, P., Dec 1985. Wigner-ville spectral analysis of nonstationary processes. IEEE Transactions on Acoustics, Speech, and Signal Processing 33 (6), 1461-1470. https://doi.org/10.1109/TASSP.1985.1164760 es_ES
dc.description.references Mateo, J., Torres, A., Aparicio, A., Santos, J., 2016. An efficient method for ECG beat classification and correction of ectopic beats. Computers and Electrical Engineering 53, 219 - 229. https://doi.org/10.1016/j.compeleceng.2015.12.015 es_ES
dc.description.references Mjahad, A., Rosado-Mu-oz, A., Guerrero-Martinez, J., Bataller-Mompean, M., Frances-Villora, J. V., 2015. ECG Analysis for Ventricular Fibrillation Detection Using a Boltzmann Network. In: Braidot, A., Hadad, A. (Eds.), VI Latin American Congress on Biomedical Engineering CLAIB 2014, Parana, Argentina 29, 30, 31 October 2014. Vol. 49 of IFMBE Proceedings. Springer International Publishing, pp. 532-535. https://doi.org/10.1007/978-3-319-13117-7 es_ES
dc.description.references Murakoshi, N., Aonuma, K., 2013. Epidemiology of arrhythmias and sudden cardiac death in asia. Circulation Journal 77 (10), 2419-2431. https://doi.org/10.1253/circj.CJ-13-1129 es_ES
dc.description.references Othman, M. A., Safri, N. M., Ghani, I. A., Harun, F. K. C., Ariffin, I., 2013. A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation. Biomedical Signal Processing and Control 8 (2), 222 - 227.https://doi.org/10.1016/j.bspc.2012.10.001 es_ES
dc.description.references Phong, P. A., Thien, K. Q., Oct 2009. Classification of Cardiac Arrhythmias Using Interval Type-2 TSK Fuzzy System. In: Knowledge and Systems Engineering, 2009. KSE '09. International Conference on. pp. 1-6. https://doi.org/10.1109/KSE.2009.19 es_ES
dc.description.references Poularikas, A. D., 1999. The transforms and applications handbooks. ACRC Handbook published in cooperatio with IEEE Press, Depatment of electrical an computer engineering the univesity of Alabama in Huntsville. es_ES
dc.description.references Rangayyan, R. M., 2002. Biomedical signal analysis: A case-study approach. In: IEEE Press Series in Biomedical Engineering. es_ES
dc.description.references Ravindra Pratap Narwaria, S. V., Singhal, P. K., 2011. Removal of baseline wander and power line interference from ecg signal - a survey approach. International Journal of Electronics Engineering 3, 107-111. es_ES
dc.description.references Rosado, A., Guerrero, J., Bataller, M., Chorro, J., 2001. Fast non-invasive ventricular fibrillation detection method using pseudo wigner-ville distribution. In: Computers in Cardiology 2001. pp. 237-240. https://doi.org/10.1109/CIC.2001.977635 es_ES
dc.description.references Saini, R., Bindal, N., Bansal, P., May 2015. Classification of heart diseases from ecg signals using wavelet transform and knn classifier. In: Computing, Communication Automation (ICCCA), 2015 International Conference on. pp. 1208-1215. https://doi.org/10.1109/CCAA.2015.7148561 es_ES
dc.description.references Sharma, L., Dandapat, S., Mahanta, A., 2010. Ecg signal denoising using higher order statistics in wavelet subbands. Biomedical Signal Processing and Control 5 (3), 214 - 222. https://doi.org/10.1016/j.bspc.2010.03.003 es_ES
dc.description.references Sornmo, L., Laguna, P., 2005. Bioelectrical signal processing in cardiac and neurological applications. In: Elsevier Academic Press. es_ES
dc.description.references Tan, W., Foo, C. L., Chua, T. W., July 2007. Type-2 Fuzzy System for ECG Arrhythmic Classification. In: Fuzzy Systems Conference, 2007. FUZZIEEE 2007. IEEE International. pp. 1-6. https://doi.org/10.1109/FUZZY.2007.4295478 es_ES
dc.description.references Valenzuela, J. V., 2008. Interpolacion de Formas en Imagenes Usando Morfologia Matematica. Ph.D. thesis, Departamento de Lenguajes, Sistemas Informaticos e Ingeniera de Software Facultad de Informatica Universidad Politecnica de Madrid, Director de Tesis: Jose Crespo del Arco. es_ES
dc.description.references Viitasalo, M., Karjalainen, J., 1992. Q T intervals at Heart rates From 50 to 120 Beats per Minute During 24 Hour Electrocardiographic Recordings in 100 Healthy Men Effects of Atenolol. American Heart Association 86 (5), 1439-1442. https://doi.org/10.1161/01.CIR.86.5.1439 es_ES
dc.description.references von Borries, R. F., Pierluissi, J. H., Nazeran, H., Jan 2005. Wavelet transformbased ecg baseline drift removal for body surface potential mapping. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. pp. 3891-3894. https://doi.org/10.1109/IEMBS.2005.1615311 es_ES
dc.description.references V.Oppenheim, A., Willsky, A. S., Nawab, S. H., 1998. Signals and systems. Prentice Hall Internationa,Inc, Massachusettes Intitue Technology with Boston Univeristy. es_ES
dc.description.references Xia, D., Meng, Q., Chen, Y., Zhang, Z., 2014. Classification of Ventricular T achycardia and Fibrillation Based on the Lempel-Ziv Complexity and EMD 8590, 322-329. https://doi.org/10.1007/978-3-319-09330-7_39 es_ES
dc.description.references Xie, H.-B., Zhong-Mei, G., Liu, H., 2011. Classification of Ventricular Tachycardia and Fibrillation Using Fuzzy Similarity-based Approximate entropy. Expert Systems with Applications 38 (4), 3973 - 3981. https://doi.org/10.1016/j.eswa.2010.09.058 es_ES
dc.description.references Yilmaz, B., Arikan, E., Asyali, M. H., April 2010. Use of knn and quadratic discriminant analysis methods for sleep staging from single lead ecg recordings. Biomedical Engineering Meeting (BIYOMUT), 2010 15th National, 1-4. https://doi.org/10.1109/BIYOMUT.2010.5479833 es_ES
dc.description.references Yochum, M., Renaud, C., Jacquir, S., 2016. Automatic detection of p, QRS and t patterns in 12 leads ECG signal based on CWT. Biomedical Signal Processing and Control 25, 46 - 52. https://doi.org/10.1016/j.bspc.2015.10.011 es_ES


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