Cardiac Arrhythmia Prediction by Adaptive Analysis via Bluetooth
In this work, the development of a data acquisition system for adaptive monitoring based on a dynamic quadratic neural unit is presented. Acquisition of the continuous signal is achieved with the BITalino biomedical data acquisition card. The system is trained sample-by-sample with a real time recurrent learning method. Then, possible cardiac arrhythmia is predicted by implementing the adaptive monitoring in real time to recognize patterns that predict cardiac arrhythmia up to 1 second in advance. For the evaluation of the interface, tests are performed using the obtained signal in real time
Afkhami, R., Azarnia, G., and Ali, M. Cardiac arrhythmia classification using statistical and mixture modeling features of ecg signals. Pattern Recognition Letters 70 (2016), 45-51.
Benes, P. M., Erben, M., Vesely, M., Liska, O., and Bukovsky, I. Honu and supervised learning algorithms in adaptive feedback control. Applied Artificial Higher Order Neural Networks for Control and Recognition (2016), 35-60.
Bukovsky, I., Homma, N., Ichiji, K., Cejnek, M., Slama, M., Benes, P. M., and Bila, J. A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications. BioMed research international 2015 (2015), 1-13.
Castillo, O., Melin, P., Ramirez, E., and Soria, J. Hybrid intelligent system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and neural networks combined with fuzzy system. Expert Syst. Appl. 39, 3 (2012), 2947-2955.
Cejnek, M., Bukovsky, I., Homma, N., and Liska, O. Adaptive polynomial filters with individual learning rates for computationally efficient lung tumor motion prediction. International Workshop on Computational Intelligence for Multimedia Understanding (2015), 1-5.
Chang, P., Lin, J., Hsieh, J., and Weng, J. Myocardial infarction classification with multilead ecg using hidden markov models and gaussian mixture models. Appl. Soft Comput. 12, 10 (2012), 3165-3175.
Chen, H., Cheng, B., Liao, G., and Kuo, T. Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare management. J. Visual Lang. Comput. 25, 6 (2014), 745-753.
Cordero, R., Suemitsu, W., and Pinto, J. Analysis and convergence theorem of complex quadratic form as decision boundary for data classification. Electronics Letters 51, 7 (2015), 561-562.
Daamouche, A., Hamami, L., Alajlan, N., and Melgani, F. A wavelet optimization approach for ecg signal classification. Biomed. Signal Process. Control 7, 4 (2012), 342-349.
Dogan, B., and Korurek, M. A new ecg beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Appl. Soft Comput. 12, 11 (2012), 3442-3451.
Elgendi, M., Eskofier, B., Dokos, S., and Abbott, D. Revisiting qrs detection methodologies for portable, wearable, battery-operated, and wireless ecg systems. PLOS ONE 9, 1 (2014), 1-18.
Herrera, J., Rodriguez-Jorge, R., Villegas, O., Cruz, V., Bila, J., Nandayapa, M. J., Ponce, I., Soto, A. I., and Flores, A. Monitoring of cardiac arrhythmia patterns by adaptive analysis. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PG-CIC 2016. Lecture Notes on Data Engineering and Communications Technologies 1 (2017), 885-894.
Homaeinezhad, M., Atyabi, S., Tavakkoli, E., Toosi, H., Ghaffari, A., and Ebrahimpour, R. Ecg arrhythmia recognition via a neurosvm-knn hybrid classifier with virtual qrs imagebased geometrical features. Expert Syst. Appl. 39, 2 (2012), 2047-2058.
Karimipour, M., and Homaeinezhad, M. R. Real-time electrocardiogram p-qrs-t detectiondelineation algorithm based on quality-supported analysis of characteristic templates. Computers in Biology and Medicine 52 (2014), 153-165.
Kutlu, Y., and Kuntalp, D. A multi-stage automatic arrhythmia recognition and classification system. Comput. Biol. Med. 41, 1 (2011), 37-45.
Luz, E., Schwartz, W., Camara-Chavez, G., and Menotti, D. Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine 127 (2016), 144-164.
Martis, B., Acharya, U., Prasad, H., Chua, C., Lim, C., and Suri, J. Application of higher order statistics for atrial arrhythmia classification. Biomed. Signal Process. Control 8 (2013), 888-900.
Mohan, H., Trivedi, A., and Shukla, S. Ecg signal processing for abnormalities detection using multi-resolution wavelet transform and neural network classifier. Measurement 46, 9 (2013), 3238-3246.
Mora, H., Gil, D., Terol, R. M., Azorn, J., and Szymanski, J. An iot-based computational framework for healthcare monitoring in mobile environments. Sensors 17, 10 (2017), 2302.
Organization, W. H. Noncommunicable diseases: progress monitor 2020. World Health Organization, 2020.
Oswald, C., Cejnek, M., Vrba, J., and Bukovsky, I. Novelty detection in system monitoring and control with honu. Applied Artificial Higher Order Neural Networks for Control and Recognition (2016), 61-78.
Ozbay, Y., Ceylan, R., and Karlik, B. Integration of type-2 fuzzy clustering and wavelet transform neural network based ecg classier. Expert Syst. Appl. 38, 1 (2011), 1004-1010.
Pandit, D., Zhang, L., Liu, C., Chattopadhyay, S., Aslam, N., and Lim, C. A lightweight qrs detector for single lead ecg signals using a maxmin difference algorithm. Computer Methods and Programs in Biomedicine 144 (2017), 61-75.
Rodriguez, R., Bukovsky, I., and Homma, N. Potentials of quadratic neural unit for applications. Advances in Abstract Intelligence and Soft Computing (2013), 343-354.
Rodriguez-Jorge, R. Artificial neural networks: Challenges in science and engineering applications. Frontiers in Artificial Intelligence and Applications 295 (2017), 25-35.
Rodriguez-Jorge, R., Leon-Damas, I. D., and Bila, J. Detection of the qrs complexity in real time with bluetooth communication. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems 158 (2021), 429-439.
Sahoo, P. K., Thakkar, H. K., and Lee, M. Y. A cardiac early warning system with multi channel scg and ecg monitoring for mobile health. Sensors (Basel) 17, 4 (2017), 711.
Salles, G., Cardoso, C., Fonseca, L., Fiszman, R., and Muxfeldt, E. Prognostic significance of baseline heart rate and its interaction with beta-blocker use in resistant hypertension: a cohort study. Am. J. Hypertens 26, 2 (2013), 218-226.
Shadmand, S., and Mashouf, B. A new personalized ecg signal classification algorithm using block-based neural network and particle swarm optimization. Biomedical Signal Processing and Control 25 (2016), 12-23.
Sun, S. An innovative intelligent system based on automatic diagnostic feature extraction for diagnosing heart disease. Knowl-Based Syst. 75 (2015), 224-238.
Wang, J., Chiang, W., Hsu, Y., and Yang, Y. Ecg arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116 (2013), 38-45.
Wee, T., and Wan, W. Non-singleton genetic fuzzy logic system for arrhythmias classification. Eng. Appl. Artif. Intell. 24, 2 (2011), 251-259.
Yang, Z., Zhou, Q., Lei, L., Zheng, K., and Xiang, W. An iot-cloud based wearable ecg monitoring system for smart healthcare. Journal of medical systems 40, 12 (2016), article ID 286.
Zhu, B., Ding, Y., and Hao, K. Multiclass maximum margin clustering via immune evolutionary algorithm for automatic diagnosis of electrocardiogram arrhythmia. Appl. Math Comput. 227 (2014), 428-436.
Copyright (c) 2020 MENDEL
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
MENDEL open access articles are normally published under a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ . Under the CC BY-NC-SA 4.0 license permitted 3rd party reuse is only applicable for non-commercial purposes. Articles posted under the CC BY-NC-SA 4.0 license allow users to share, copy, and redistribute the material in any medium of format, and adapt, remix, transform, and build upon the material for any purpose. Reusing under the CC BY-NC-SA 4.0 license requires that appropriate attribution to the source of the material must be included along with a link to the license, with any changes made to the original material indicated.