Clasificación de Arritmias Cardíacas mediante Redes Neuronales Convolucionales y Optimización por Enjambre de Partículas
Resumen
Una arritmia cardíaca es un latido irregular del corazón que se traduce en un impulso eléctrico anormal, y su tipo se define por el ritmo y duración. Su clasificación ha sido abordada en diferentes campos de la ciencia, destacando el uso de algoritmos de aprendizaje profundo. La presente investigación, utilizó un modelo híbrido entre Redes Neuronales Convolucionales y el algoritmo metaheurístico de Optimización por Enjambre de Partículas; para la clasificación de arritmias cardíacas. El metaheurístico se encargó de optimizar la arquitectura de capas de la red neuronal, a través de la minización de la pérdida durante el entrenamiento y prueba. Los datos se obtuvieron del MIT-BIH Arrhythmia dataset, donde se describen cinco categorías de arritmias. Los resultados logrados demostraron que el metaheurístico es un algoritmo confiable en la búsqueda de la mejor arquitectura de capas, logrando obtener una exactitud del 97%, lo que significa que el uso de técnicas metaheurísticas es una opción que se debe tomar en consideración a la hora de optimizar el rendimiento de las redes neuronales convolucionales.
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Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., and Nahavandi, S. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion, 76:243–297.
Agrafioti, F. et al. (2011). ECG in biometric recognition: Time dependency and application challenges. University of Toronto. ANSI/AAMI (2012). Testing and reporting performance results of cardiac arhythm and st segment measure algorithms.
Atal, D. K. and Singh, M. (2020). Arrhythmia classification with ecg signals based on the optimization enabled deep convolutional neural network. Computer Methods and Programs in Biomedicine, 196:105607.
Ayar, M. and Sabamoniri, S. (2018). An ecg-based feature selection and heart- beat classification model using a hybrid heuristic algorithm. Informatics in Medicine Unlocked, 13:167–175.
Banerjee, R., Ghose, A., and Khandelwal, S. (2019). A novel recurrent neural network architecture for classification of atrial fibrillation using single-lead ecg. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1–5. IEEE.
Biel, L., Pettersson, O., Philipson, L., and Wide, P. (2001). Ecg analysis: a new approach in human identification. IEEE Transactions on Instrumentation and Measurement, 50(3):808–812.
Borghi, P. H., Borges, R. C., and Teixeira, J. P. (2021). Atrial fibrillation classification based on mlp networks by extracting jitter and shimmer parameters. Procedia Computer Science, 181:931–939.
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, 51:1196–1206.
Chen, B., Guo, Y., Chen, Y., Zheng, H., and Liu, T. (2019). Ecg classification based on unfixed-length segmentation of heartbeat. In 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), pages 1–2.
Chen, C., Hua, Z., Zhang, R., Liu, G., andWen,W. (2020). Automated arrhythmia classification based on a combination network of cnn and lstm. Biomedical Signal Processing and Control, 57:101819.
Dutta, S., Chatterjee, A., and Munshi, S. (2010). Correlation technique and least square support vector machine combine for frequency domain based ecg beat classification. Medical Engineering Physics, 32(10):1161–1169.
D´avila, C. A. (2017). Tendencia e impacto de la mortalidad por enfermedades cardiovasculares en m´exico, 1990-2015. In Revista Cubana de Salud P´ublica, volume 45.
Ebrahimi, Z., Loni, M., Daneshtalab, M., and Gharehbaghi, A. (2020). A review on deep learning methods for ecg arrhythmia classification. Expert Systems with Applications: X, 7:100033.
Essam, H. H., Moataz, K., and Aboul, E. H. (2017). Ecg signals classification: a review. International Journal of Intelligent Engineering Informatics, 5(4):376–396.
Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., and Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161:1–13.
Fernandes Junior, F. E. and Yen, G. G. (2019). Particle swarm optimization of deep neural networks architectures for image classification. Swarm and Evolutionary Computation, 49:62–74.
Ferretti, J., Randazzo, V., Cirrincione, G., and Pasero, E. (2021). 1-d convolutional neural network for ecg arrhythmia classification. In Progresses in Artificial Intelligence and Neural Systems, pages 269–279. Springer.
Gao, J., Zhang, H., Lu, P., andWang, Z. (2019). An efective lstm recurrent network to detect arrhythmia on imbalanced ecg dataset. Journal of healthcare engineering, 2019.
Gharehbaghi, S. and Khatibinia, M. (2015). Optimal seismic design of reinforced concrete structures under time-history earthquake loads using an intelligent hybrid algorithm. Earthquake Engineering and Engineering Vibration, 14(1):97–109.
Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249–256. JMLR Workshop and Conference Proceedings.
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., and Stanley, H. E. (2000). Physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. In PhysioBank, pages e215–e220.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
Heinze, M. G., Olmedo-Canchola, V., Baz´an-Miranda, G., Bernard-Fuentes, N., and Gu´izar-S´anchez, D. (2018). Los m´edicos especialistas en m´exico. Gaceta medica de Mexico, 154(3):342–351.
Hou, B., Yang, J., Wang, P., and Yan, R. (2019). Lstm-based auto-encoder model for ecg arrhythmias classification. IEEE Transactions on Instrumentation and Measurement, 69(4):1232–1240.
Houssein, E. H., Abdelminaam, D. S., Hassan, H. N., Al-Sayed, M. M., and Nabil, E. (2021a). A hybrid barnacles mating optimizer algorithm with support vector machines for gene selection of microarray cancer classification. IEEE Access, 9:64895–64905.
Houssein, E. H., AbdElminaam, D. S., Ibrahim, I. E., Hassaballah, M., andWazery, Y. M. (2021b). A hybrid heartbeats classification approach based on marine predators algorithm and convolution neural networks. IEEE Access.
Houssein, E. H., Ewees, A. A., and Abd ElAziz, M. (2018). Improving twin support vector machine based on hybrid swarm optimizer for heartbeat classification. Pattern Recognition and Image Analysis, 28(2):243–253.
Huikuri, H. V., Castellanos, A., and Myerburg, R. J. (2001). Sudden death due to cardiac arrhythmias. New England Journal of Medicine, 345(20):1473–1482. PMID: 11794197.
Ince, T., Kiranyaz, S., and Gabbouj, M. (2009). A generic and robust system for automated patient-specific classification of ecg signals. IEEE Transactions on Biomedical Engineering, 56(5):1415–1426.
Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448 456. PMLR.
Izci, E., Ozdemir, M. A., Degirmenci, M., and Akan, A. (2019). Cardiac arrhythmia detection from 2d ecg images by using deep learning technique. In 2019 Medical Technologies Congress (TIPTEKNO), pages 1–4. IEEE.
Jain, P., Arjun Babu, C. S., Mohandoss, S., Anisham, N., Gadade, S., Srinivas, A., and Mohan, R. (2021). A novel approach to classify cardiac arrhythmia using different machine learning techniques. In Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A. E., Anand, S., and Jaiswal, A., editors, International Conference on Innovative Computing and Communications, pages 517–526, Singapore. Springer Singapore.
Jun, T. J., Nguyen, H. M., Kang, D., Kim, D., Kim, D., and Kim, Y.-H. (2018). Ecg arrhythmia classification using a 2-d convolutional neural network. ar- Xiv preprint arXiv:1804.06812.
Kachuee, M., Fazeli, S., and Sarrafzadeh, M. (2018). Ecg heartbeat classification: A deep transferable representation. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), pages 443–444. IEEE.
Kennedy, J. and Eberhart, R. (2006). Swarm intelligence. In Handbook of nature-inspired and innovative computing, pages 187–219. Springer.
Kim, B.-H. and Pyun, J.-Y. (2020). Ecg identification for personal authentication using lstm-based deep recurrent neural networks. Sensors, 20(11):3069.
Kim, M.-G., Ko, H., and Pan, S. B. (2019). A study on user recognition using 2d ecg image based on ensemble networks for intelligent vehicles. Wireless Communications and Mobile Computing, 2019.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kropf, M., Hayn, D., and Schreier, G. (2017). Ecg classification based on time and frequency domain features using random forests. In 2017 Computing in Cardiology (CinC), pages 1–4. IEEE.
Kumar, G., Pawar, U., and O’Reilly, R. (2019). Arrhythmia detection in ecg signals using a multilayer perceptron network. In AICS, pages 353–364.
Kumar, R. G., Kumaraswamy, Y., et al. (2012). Investigating cardiac arrhythmia in ecg using random forest classification. Int. J. Comput. Appl, 37(4):31–34.
Lannoy, G., Franc¸ois, D., Delbeke, J., and Verleysen, M. (2011). Weighted svms and feature relevance assessment in supervised heart beat classification. In Fred, A., Filipe, J., and Gamboa, H., editors, Biomedical Engineering Systems and Technologies, pages 212–223, Berlin, Heidelberg. Springer Berlin Heidelberg.
Li, H., Yuan, D., Ma, X., Cui, D., and Cao, L. (2017). Genetic algorithm for the optimization of features and neural networks in ecg signals classification. Scientific Reports, 7:2045–2322.
Limam, M. and Precioso, F. (2017). Atrial fibrillation detection and ecg classification based on convolutional recurrent neural network. In 2017 Computing in Cardiology (CinC), pages 1–4. IEEE.
Liu, J., Song, S., Sun, G., and Fu, Y. (2019). Classification of ecg arrhythmia using cnn, svm and lda. In International Conference on Artificial Intelligence and Security, pages 191–201. Springer.
Loni, M., Sinaei, S., Zoljodi, A., Daneshtalab, M., and Sj¨odin, M. (2020). Deepmaker: A multi-objective optimization framework for deep neural networks in embedded systems. Microprocessors and Microsystems, 73:102989.
Lugo-Reyes, S. O., Maldonado-Col´ın, G., and Murata, C. (2014). Inteligencia artificial para asistir el diagn´ostico cl´ınico en medicina. Revista Alergia M´exico, 61(2):110–120.
Ma, F., Zhang, J., Chen, W., Liang, W., and Yang, W. (2020). An automatic system for atrial fibrillation by using a cnn-lstm model. Discrete Dynamics in Nature and Society, 2020.
Mathunjwa, B. M., Lin, Y.-T., Lin, C.-H., Abbod, M. F., and Shieh, J.-S. (2021). Ecg arrhythmia classification by using a recurrence plot and convolutional
neural network. Biomedical Signal Processing and Control, 64:102262.
Mond´ejar-Guerra, V., Novo, J., Rouco, J., Penedo, M., and Ortega, M. (2019). Heartbeat classification fusing temporal and morphological information of
ecgs via ensemble of classifiers. Biomedical Signal Processing and Control, 47:41–48.
Moody, G. and Mark, R. (2001). The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):45–50.
Mostayed, A., Luo, J., Shu, X., and Wee, W. (2018). Classification of 12-lead ecg signals with bi-directional lstm network. arXiv preprint arXiv:1811.02090.
Murat, F., Yildirim, O., Talo, M., Baloglu, U. B., Demir, Y., and Acharya, U. R. (2020). Application of deep learning techniques for heartbeats detection using ecg signals-analysis and review. Computers in Biology and Medicine, 120:103726.
Navaneeth, B. and Suchetha, M. (2019). Pso optimized 1-d cnn-svm architecture for real-time detection and classification applications. Computers in Biology and Medicine, 108:85–92.
Osowski, S., Hoai, L., and Markiewicz, T. (2004). Support vector machinebased expert system for reliable heartbeat recognition. IEEE Transactions on Biomedical Engineering, 51(4):582–589.
Park, Y. and Yun, I. D. (2019). Arrhythmia detection in electrocardiogram based on recurrent neural network encoder–decoder with lyapunov exponent. IEEJ Transactions on Electrical and Electronic Engineering, 14(8):1273–1274.
Parveen, A., Vani, R. M., Hunagund, P. V., and Soher-wardy, M. A. (2021). Classification of ecg arrhythmia using different machine learning approach. In Gao, X.-Z., Kumar, R., Srivastava, S., and Soni, B. P., editors, Applications of Artificial Intelligence in Engineering, pages 319–325, Singapore. Springer Singapore.
Piccialli, F., Somma, V. D., Giampaolo, F., Cuomo, S., and Fortino, G. (2021). A survey on deep learning in medicine: Why, how and when? Information Fusion, 66:111–137.
Polat, K., Akdemir, B., and G¨unes¸, S. (2008). Computer aided diagnosis of ecg data on the least square support vector machine. Digital Signal Processing, 18(1):25–32.
Rai, H. M. and Chatterjee, K. (2021). Hybrid cnn-lstm deep learning model and ensemble technique for automatic detection of myocardial infarction using big ecg data. Applied Intelligence, pages 1–19.
Rajani Kumari, L., Padma Sai, Y., et al. (2021). Classification of arrhythmia beats using optimized k-nearest neighbor classifier. In Intelligent Systems, pages 349–359. Springer.
Rajesh, K. N. and Dhuli, R. (2017). Classification of ecg heartbeats using nonlinear decomposition methods and support vector machine. Computers in Biology and Medicine, 87:271–284.
Rajesh, K. N. and Dhuli, R. (2018). Classification of imbalanced ecg beats using re-sampling techniques and adaboost ensemble classifier. Biomedical Signal Processing and Control, 41:242 254.
Ramkumar, M., Babu, C. G., Kumar, K. V., Hepsiba, D., Manjunathan, A., and Kumar, R. S. (2021). ECG cardiac arrhythmias classification using DWT, ICA and MLP neural networks. Journal of Physics: Conference Series, 1831(1):012015.
Rohmantri, R. and Surantha, N. (2020). Arrhythmia classification using 2d convolutional neural network. Int. J. Adv. Comput. Sci. Appl, 11:201–208.
Rosas-Peralta, M., Palomo-Pi˜n´on, S., Borrayo-S´anchez, G., Madrid-Miller, A., Almeida-Guti´errez, E., Galv´an-Oseguera, H., Maga˜na-Serrano, J. A., Saturno-Chiu, G., Ram´ırez-Arias, E., Santos-Mart´ınez, E., et al. (2016). Consenso de hipertensi´on arterial sist´emica en m´exico. Revista M´edica del Instituto Mexicano del Seguro Social, 54(S1):6–51.
Saadatnejad, S., Oveisi, M., and Hashemi, M. (2019). Lstm-based ecg classification for continuous monitoring on personal wearable devices. IEEE journal of biomedical and health informatics, 24(2):515–523.
Salem, M., Taheri, S., and Yuan, J.-S. (2018). Ecg arrhythmia classification using transfer learning from 2-dimensional deep cnn features. In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pages 1–4. IEEE.
Savalia, S. and Emamian, V. (2018). Cardiac arrhythmia classification by multilayer perceptron and convolution neural networks. Bioengineering, 5(2):35.
Schwab, P., Scebba, G. C., Zhang, J., Delai, M., and Karlen, W. (2017). Beat by beat: Classifying cardiac arrhythmias with recurrent neural networks. In 2017 Computing in Cardiology (CinC), pages 1–4. IEEE.
S¸EN, S. Y. and O¨ ZKURT, N. (2019). Ecg arrhythmia classification by using convolutional neural network and spectrogram. In 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), pages 1–6. IEEE.
Sharma, R. R., Kumar, M., and Pachori, R. B. (2019). Joint time-frequency domain-based cad disease sensing system using ecg signals. IEEE Sensors Journal, 19(10):3912–3920.
Shi, H., Wang, H., Huang, Y., Zhao, L., Qin, C., and Liu, C. (2019). A hierarchical method based on weighted extreme gradient boosting in ecg heartbeat classification. Computer Methods and Programs in Biomedicine, 171:1–10.
Shimpi, P., Shah, S., Shroff, M., and Godbole, A. (2017). A machine learning approach for the classification of cardiac arrhythmia. In 2017 international conference on computing methodologies and communication (ICCMC), pages 603–607. IEEE.
Shiraishi, J., Li, Q., Appelbaum, D., and Doi, K. (2011). Computer-aided diagnosis and artificial intelligence in clinical imaging. Seminars in Nuclear Medicine, 41(6):449–462. Image Perception in Nuclear Medicine.
Sigurthorsdottir, H., Van Zaen, J., Delgado-Gonzalo, R., and Lemay, M. (2020). Ecg classification with a convolutional recurrent neural network. In 2020 Computing in Cardiology, pages 1–4. IEEE.
Simanjuntak, J. E. S., Khodra, M. L., and Manullang, M. C. T. (2020). Design methods of detecting atrial fibrillation using the recurrent neural network algorithm on the arduino ad8232 ecg module. In IOP Conference Series: Earth and Environmental Science, volume 537, page 012022. IOP Publishing.
Singh, S., Pandey, S. K., Pawar, U., and Janghel, R. R. (2018). Classification of ecg arrhythmia using recurrent neural networks. Procedia computer science, 132:1290–1297.
Sraitih, M., Jabrane, Y., and Atlas, A. (2021). An overview on machine learning methods for ecg heartbeat arrhythmia classification. In 2021 International Conference on Digital Age Technological Advances for Sustainable Development (ICDATA), pages 8–14.
S´anchez-Arias, A., Bobadilla-Serrano, M., and Dimas-Altamirano, B. (2016). Enfermedad cardiovascular: primera causa de morbilidad en un hospital de tercer nivel. In Revista Mexicana de Cardiolog´ıa, pages 98–102.
Toulni, Y., Belhoussine Drissi, T., and Nsiri, B. (2021). Ecg signal diagnosis using discrete wavelet transform and k-nearest neighbor classifier. In Proceedings of the 4th International Conference on Networking, Information Systems & Security, pages 1–6.
Triqui, B. and Benyettou, A. (2018). Cardiac arrythmia classification by neuronal networks (mlp). The Online Journal of Science and Technology-January, 8(1).
UNAM (2020). Enfermedades del coraz´on, pandemia permanente.
Valupadasu, R. and Chunduri, B. R. R. (2019). Automatic classification of cardiac disorders using mlp algorithm. In 2019 Prognostics and System Health Management Conference (PHM-Paris), pages 253–257. IEEE.
Van Zaen, J., Ch´etelat, O., Lemay, M., Calvo, E. M., and Delgado-Gonzalo, R. (2019). Classification of cardiac arrhythmias from single lead ecg with a convolutional recurrent neural network. arXiv preprint arXiv:1907.01513.
Wang, H., Shi, H., Lin, K., Qin, C., Zhao, L., Huang, Y., and Liu, C. (2020). A high-precision arrhythmia classification method based on dual fully connected neural network. Biomedical Signal Processing and Control, 58:101874.
Wang, J. (2021). An intelligent computer-aided approach for atrial fibrillation and atrial flutter signals classification using modified bidirectional lstm network. Information Sciences, 574:320–332.
WHO (2019). Cardiovascular diseases (cvds).
Ye, C., Vijaya Kumar, B. V. K., and Coimbra, M. T. (2012). Heartbeat classification using morphological and dynamic features of ecg signals. IEEE Transactions on Biomedical Engineering, 59(10):2930–2941.
Yildirim, O., Baloglu, U. B., Tan, R.-S., Ciaccio, E. J., and Acharya, U. R. (2019). A new approach for arrhythmia classification using deep coded features and lstm networks. Computer methods and programs in biomedicine, 176:121–133.
Yildirim, O., Pławiak, P., Tan, R.-S., and Acharya, U. R. (2018). Arrhythmia detection using deep convolutional neural network with long duration ecg signals. Computers in biology and medicine, 102:411–420.
Zabihi, M., Rad, A. B., Katsaggelos, A. K., Kiranyaz, S., Narkilahti, S., and Gabbouj, M. (2017). Detection of atrial fibrillation in ecg hand-held devices using a random forest classifier. In 2017 Computing in Cardiology (CinC), pages 1–4. IEEE.
Zhang, D., Chen, Y., Chen, Y., Ye, S., Cai, W., and Chen, M. (2021). An ecg heartbeat classification method based on deep convolutional neural network. Journal of Healthcare Engineering, 2021.
Zhang, Z., Dong, J., Luo, X., Choi, K.-S., and Wu, X. (2014). Heartbeat classification using disease-specific feature selection. Computers in Biology and Medicine, 46:79–89.
Zihlmann, M., Perekrestenko, D., and Tschannen, M. (2017). Convolutional recurrent neural networks for electrocardiogram classification. In 2017 Computing in Cardiology (CinC), pages 1–4. IEEE.
