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

Azeddine Mjahad, Alfredo Rosado Muñoz, Manuel Bataller Mompeán, Jose V. Francés Víllora, Juan F. Guerrero Martínez

Resumen

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.


Palabras clave

Sistemas biomédicos; Señales Electrocardiográficas; Representación tiempo-frecuencia; Señales no estacionarias; Análisis de imágenes; Clasificación

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Referencias

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.

Cohen, L., Jul 1989a. Time frequency distributions a review. Proceedings of the IEEE 77 (7), 941–981. https://doi.org/10.1109/5.30749

Cohen, L., 1989b. Time-frequency distributions-a review. Proceeding of the IEEE 77 (7), 941–981. https://doi.org/10.1109/5.30749

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

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

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

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

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

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

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

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

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

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.

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

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.

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.

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

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

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

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

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

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

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

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.

Rangayyan, R. M., 2002. Biomedical signal analysis: A case-study approach. In: IEEE Press Series in Biomedical Engineering.

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.

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

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

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

Sornmo, L., Laguna, P., 2005. Bioelectrical signal processing in cardiac and neurological applications. In: Elsevier Academic Press.

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

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.

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

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

V.Oppenheim, A., Willsky, A. S., Nawab, S. H., 1998. Signals and systems. Prentice Hall Internationa,Inc, Massachusettes Intitue Technology with Boston Univeristy.

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

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

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

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

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