Abstract
Cardiac arrhythmias are not life-threatening straight away but can cause serious heart-related complications if not medically handled appropriately. An electrocardiogram (ECG) captures the heart’s electric activity and has widespread usage due to its easy deployment and non-invasive aspect. Arrhythmia classification through manual analysis of electrocardiogram (ECG) is troublesome, tedious, and prone to human errors that can lead to serious repercussions. Hence, it’s a more effective alternative to deploy computational techniques to automatically perform the classification. Traditional techniques are data-driven and require an immense amount of data to train and then perform identification. This paper presents an ontology-driven knowledge model to automatically diagnose arrhythmias based on the patient’s sensor-based ECG data. The proposed approach models the arrhythmia domain knowledge and the conceptual relationships relevant to classification of heartbeat into corresponding cardiac arrhythmias and to facilitate decision making with respect to the patients. The newly developed arrhythmia ontology consists of three different modules, each semantically annotating a different aspect of the arrhythmia detection process. A SWRL (Semantic Web Rule Language) based ontology classifier performs classification of patient’s ECG data into corresponding cardiac arrhythmia types. The constructed knowledge base is ontologically aligned with some benchmarked top-level ontologies that promotes the semantic interoperability across multiple domains. The resultant ontological model is validated with a real-world ECG dataset and compared with the existing approaches showing higher precision rate and comparable performance. The developed model establishes a standardized ontology, that promotes the exchange and shareability of consensual domain knowledge about arrhythmic conditions, supports information retrieval and knowledge discovery.
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The dataset(s) generated during and/or analysed during the current study are available in the [Physionet] repository, [https://www.physionet.org/content/mitdb/1.0.0/].
References
Atal, D. K., & 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.
Anwar, S.M., Gul, M., Majid, M., & Alnowami, M. (2018). Arrhythmia Classification of ECG signals using hybrid features. Computational and Mathematical Methods in Medicine, 2018, 1380348.
Abdoli, M., Ahmadian, A., Karimifard, S., Sadoughi, H., & Rizi, F.Y. (2009). An efficient piecewise modeling of ECG signals based on critical samples using Hermitian basis functions. In 4th European conference of the international federation for medical and biological engineering (pp. 1188–1191). Berlin: Springer.
Ali, F., Kwak, D., Khan, P., Islam, S.R., Kim, K.H., & Kwak, K.S. (2017). Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling. Transportation Research Part C: Emerging Technologies, 77, 33–48.
Al Rahhal, M. M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., & Yager, R.R. (2016). Deep learning approach for active classification of electrocardiogram signals. Information Sciences, 345, 340–354.
Banerjee, S., & Singh, G.K. (2021). Deep neural network based missing data prediction of electrocardiogram signal using multiagent reinforcement learning. Biomedical Signal Processing and Control, 67, 102508.
Beers, M.H. (2001). The Merck manual of diagnosis and therapy. Whitehouse Station, N.J. : Merck Research Laboratories, division of Merck & Co., Inc., 1999.
Bennett, D.H. (2012). Bennett’s cardiac arrhythmias: practical notes on interpretation and treatment. S.H. pennefather, British Journal of Anaesthesia.
Bouchikhi, S., Boublenza, A., & Chikh, M.A. (2015). Discrete hidden Markov model classifier for premature ventricular contraction detection. International Journal of Biomedical Engineering and Technology, 17, 371–386.
Borgo, S., & Masolo, C. (2009). Foundational choices in DOLCE. In Handbook on ontologies, (Vol. 361–381 p. Berlin). Springer.
Brickley, D, & Miller, L. (2007). FOAF Vocabulary specification 0.91 W3C.
Breslin, J.G., Decker, S., Harth, A., & Bojars, U. (2006). SIOC: An approach to connect web-based communities. International Journal of Web Based Communities, 2, 133–142.
Chen, Y., Yu, C., Liu, X., Xi, T., Xu, G., Sun, Y., & Shen, B. (2021). PCLiON: An ontology for data standardization and sharing of prostate cancer associated lifestyles. International Journal of Medical Informatics, 145, 104332.
Castro-Lopez, O., Lopez-Barron, D.E., & Vega-Lopez, I.F. (2020). Next-generation heartbeat classification with a column-store DBMS and UDFs. Journal of Intelligent Information Systems, 54, 363–390.
Deng, Y., Gao, Z., Xu, S., Ren, P., Wen, Y., Mao, Y., & Li, Z. (2020). ST-Net: Synthetic ECG tracings for diagnosing various cardiovascular diseases. Biomedical Signal Processing and Control, 61, 101997.
Dokur, Z., & Tamer, O. (2001). ECG Beat classification by a novel hybrid neural network. Computer Methods and Programs in Biomedicine, 66, 167–181.
Dokur, Z., Olmez, T., & Yazgan, E. (1999). Comparision of discrete wavelet and fourier transform for ECG beat classification. Electronics Letters, 35, 1502–1504.
Fujita, H., & Cimr, D. (2019). Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. Applied Intelligence, 49, 3383–3391.
Ferranti, N., Mouro, J.R., Mendonça, F.M., de Souza, J.F., & Soares, S.S.R.F. (2021). A framework for evaluating ontology meta-matching approaches. Journal of Intelligent Information Systems, 56, 207–231.
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., & Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101, e215–e220.
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., & Stanley, H.E. (2000). Physiobank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101, e215–e220.
Guizzardi, G., & Halpin, T. (2008). Ontological foundations for conceptual modelling. Applied Ontology, 3, 1–12.
Hartmann, J., Spyns, P., Giboin, A., Maynard, D., Cuel, R., Suárez-Figueroa, M.C., & Sure, Y. (2005). D1. 2.3 Methods for ontology evaluation. EU-IST Network of Excellence (NoE) IST-2004-507482 KWEB Deliverable D.1.
Horrocks, I., Peter, F.P., Harold, B., Said, T., Benjamin, G., & Mike, D. (2004). SWRL: A Semantic web rule language combining OWL and ruleML. W3C Member Submission, 21, 1–31.
Houssein, E.H., Ewees, A.A., & Abd ElAziz, M. (2018). Improving twin support vector machine based on hybrid swarm optimizer for heartbeat classification. Pattern Recognition and Image Analysis, 28, 243–253.
Hu, Y.H., Palreddy S., & Tompkins, W. (1997). A patient adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering, 44, 891–900.
Inan, O.T., Giovangrandi, L., & Kovacs, G.T.A. (2006). Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Transactions on Biomedical Engineering, 53, 2507–2515.
Jung, H., & Kim, W. (2020). Automated conversion from natural language query to SPARQL query. Journal of Intelligent Information Systems, 55, 501–520.
Kiranyaz, S., Ince, T., Pulkkinen, J., & Gabbouj, M. (2011). Personalized long-term ECG classification: a systematic approach. Expert Systems with Applications, 38, 3220–3226.
LePendu, P., & Dou, D. (2011). Using ontology databases for scalable query answering, inconsistency detection, and data integration. Journal of Intelligent Information Systems, 37, 217–244.
Li, T., & Zhou, M. (2016). ECG Classification using wavelet packet entropy and random forests. Entropy, 18, 285.
Martis, R.J., Acharya, U.R., Mandana, K.M., Ray, A.K., & Chakraborty, C (2012). Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Systems with Applications, 39, 11792–11800.
Martis, R.J., Acharya, U.R., Mandana, K.M., Ray, A.K., & Chakraborty, C. (2013). Cardiac decision making using higher order spectra. Biomedical Signal Processing and Control, 8, 193–203.
Mathunjwa, B.M., Lin, Y.T., Lin, C.H., Abbod, M.F., & Shieh, J.S. (2021). ECG Arrhythmia classification by using a recurrence plot and convolutional neural network. Biomedical Signal Processing and Control, 64, 102262.
Martínez-García, M., Valls, A., & Moreno, A. (2019). Inferring preferences in ontology-based recommender systems using WOWA. Journal of Intelligent Information Systems, 52, 393–423.
Martis, R.J., Acharya, U.R., Mandana, K.M., Ray, A.K., & Chakraborty, C. (2013). Cardiac decision making using higher order spectra. Biomedical Signal Processing and Control, 8, 193–203.
Minami, K.I., Nakajima, H., & Toyoshima, T. (1999). Real-time discrimination of ventricular tachyarrhythmia with fourier-transform neural network. IEEE Transactions on Biomedical Engineering, 46, 179–185.
Moody, G.B., & Mark, R.G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20, 45–50.
Mondjar-Guerra, V., Novo, J., Rouco, J., Penedo, M.G., & Ortega, M. (2019). Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomedical Signal Processing and Control, 47, 41–48.
Mustaqeem, A., Anwar, S.M., Khan, A.R., & Majid, M. (2017). A statistical analysis based recommender model for heart disease patients. International Journal of Medical Informatics, 108, 134–145.
Mustaqeem, A., Anwar, S.M., & Majid, M. (2018). Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants. Computational and Mathematical Methods in Medicine, 2018, 7310496.
Prineas, R.J., Crow, R.S., & Zhang, Z.M. (2009). The Minnesota code manual of electrocardiographic findings. Springer Science and Business Media, (p. 2010). London: Springer.
Pławiak, P., & Acharya, U.R. (2020). Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Computing and Applications, 32, 11137–11161.
Sangaiah, A.K., Arumugam, M., & Bian, G.B. (2020). An intelligent learning approach for improving ECG signal classification and arrhythmia analysis. Artificial Intelligence in Medicine, 103, 101788.
Senhadji, L., Carrault, G., Bellanger, J.J., & Passariello, G. (1995). Comparing wavelet transforms for recognizing cardiac patterns. IEEE Engineering in Medicine and Biology Magazine, 14, 167–173.
Sayadi, O, Shamsollahi, M.B., & Clifford, G.D. (2010). Robust detection of premature ventricular contractions using a wave-based Bayesian framework. IEEE Transactions on Biomedical Engineering, 57, 353–362.
Shvaiko, P., & Euzenat, J. (2005). A survey of schema-based matching approaches. Journal on Data Semantics, IV, 146–171. Springer, Berlin, Heidelberg.
Stearns, M.Q., Price, C., Spackman, K.A., & Wang, A.Y. (2001). SNOMED clinical terms: overview of the development process and project status. In Proceedings of the AMIA Symposium, 662, American Medical Informatics Association.
Sun, Y. (2001). Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques, Ph.D. dissertation, Nanyang Technological University, 2001.
Tuncer, T., Dogan, S., Pławiak, P., & Acharya, U.R. (2019). Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowledge-Based Systems, 186, 104923.
Tantawi, M.M., Revett, K., Salem, A., & Tolba, M. F. (2013). Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition. Journal of Intelligent Information Systems, 40, 17–39.
The Dublin Core Metadata Element Set. (2007). Dublin core metadata initiative august.
Trahanias, P., & Skordalakis, E. (1990). Syntactic pattern recognition of the ECG. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 648–657.
Wang, X., Li, R., Wang, S., Shen, S., Zhang, W., Zhou, B., & Wang, Z. (2021). Automatic diagnosis of ECG disease based on intelligent simulation modeling. Biomedical Signal Processing and Control, 67, 102528.
Wang, X., Li, R., Wang, S., Shen, S., Zhang, W., Zhou, B., & Wang, Z. (2021). Automatic diagnosis of ECG disease based on intelligent simulation modeling. Biomedical Signal Processing and Control, 67, 102528.
Willems, J.L., & Lesaffre, E. (1987). Comparison of multigroup logistic and linear discriminant ECG and VCG classification. Journal of Electrocardiology, 20, 83–92.
Wheeler, T.S., Vallis, T.M., Giacomantonio, N.B., & Abidi, S.R. (2018). Feasibility and usability of an ontology-based mobile intervention for patients with hypertension. International Journal of Medical Informatics, 119, 8–16.
Wilk, S., Kezadri-Hamiaz, M., Amyot, D., Michalowski, W., Kuziemsky, C., Catal, N., & Giffen, R. (2020). An ontologyframework to support the dynamic formation of an interdisciplinary healthcare team. International Journal of Medical Informatics, 136, 104075.
Yildirim, O., Talo, M., Ciaccio, E.J., San Tan, R., & Acharya, U.R. (2020). Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. Computer Methods and Programs in Biomedicine, 197, 105740.
Zhang, Z., Dong, J., Luo, X., Choi, K.S., & Wu, X. (2014). Heartbeat classification using disease-specific feature selection. Computers in Biology and Medicine, 46, 79–89.
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Hooda, D., Rani, R. An Ontology driven model for detection and classification of cardiac arrhythmias using ECG data. J Intell Inf Syst 58, 405–431 (2022). https://doi.org/10.1007/s10844-021-00685-2
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DOI: https://doi.org/10.1007/s10844-021-00685-2