Abstract
An investigation into the use of a unifying Homogeneous Feature Vector Representation (HFVR), to address the challenge of applying machine learning and/or deep learning to heterogeneous data, is presented. To act as a focus, Atrial Fibrillation classification is considered which features both tabular and Electrocardiogram (ECG) time series data. The challenge of constructing HFVRs is the process for selecting features. A mechanism where by this can be achieved, in terms of motifs and discords, with respect to ECG time series data is presented. The presented evaluation demonstrates that more effective AF classification can be achieved using the idea of HFVR than would otherwise be achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aldosari, H., Coenen, F., Lip, G., Zheng, Y.: Motif based feature vectors: towards a homogeneous data representation for cardiovascular diseases classification. In: Proceedings of the 23rd International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2021 (2021)
Cabrera, D., et al.: Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation. Appl. Soft Comput. 58, 53–64 (2017)
Christov, I., Krasteva, V., I. Simova, T.N., Schmid, R.: Multi-parametric analysis for atrial fibrillation classification in ECG. In: IEEE Computing in Cardiology, CinC 2017, pp. 1–4 (2017)
Coyle, D., Prasad, G., McGinnity, T.M.: A time-series prediction approach for feature extraction in a brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 13(4), 461–467 (2005)
Das, M.K., Ari, S.: ECG beats classification using mixture of features. Int. Sch. Res. Not. 2014, 178436 (2014)
Dau, H.A., Keogh, E.: Matrix profile V: a generic technique to incorporate domain knowledge into motif discovery. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 125–134 (2017)
Ding, S., Du, M., Sun, T., Xu, X., Xue, Y.: An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood. Knowl. Based Syst. 133, 294–313 (2017)
Golinko, E., Sonderman, T., Zhu, X.: CNFL: categorical to numerical feature learning for clustering and classification. In: 2017 IEEE 2nd International Conference on Data Science in Cyberspace (DSC), pp. 585–594. IEEE (2017)
Inoue, H., et al.: Impact of gender on the prognosis of patients with nonvalvular atrial fibrillation. Am. J. Cardiol. 113(6), 957–962 (2014)
Jain, A., Jain, V.: Voting ensemble classifier for sentiment analysis. In: Abraham, A., Castillo, O., Virmani, D. (eds.) Proceedings of 3rd International Conference on Computing Informatics and Networks. LNNS, vol. 167, pp. 255–261. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9712-1_22
Jovic, A., Bogunovic, N.: Feature extraction for ECG time-series mining based on chaos theory. In: Proceedings of the 29th International Conference on Information Technology Interfaces (2007)
Keogh, E.J., Lin, J., Fu, A.: HOT SAX: efficiently finding the most unusual time series subsequence. In: Proceedings of the 5th IEEE International Conference on Data Mining, ICDM 2005, pp. 226–233 (2005)
Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: Proceedings of the Science and Information Conference, SAI 2014, pp. 372–378 (2014)
Kumar, D., Batra, U.: Breast cancer histopathology image classification using soft voting classifier. In: Abraham, A., Castillo, O., Virmani, D. (eds.) Proceedings of 3rd International Conference on Computing Informatics and Networks. LNNS, vol. 167, pp. 619–631. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9712-1_53
Li, P., et al.: High-performance personalized heartbeat classification model for long-term ECG signal. IEEE Trans. Biomed. Eng. 64(1), 78–86 (2016)
Lip, G., et al.: Atrial fibrillation. Nat. Rev. Dis. Primers. 31, 16016 (2016). https://doi.org/10.1038/nrdp.2016.16
Liu, F., et al.: An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J. Med. Imaging Health Inf. 8(7), 1368–1373 (2018)
Maletzke, A.G., et al.: Time series classification using motifs and characteristics extraction: a case study on ECG databases. In: Proceedings of the 4th International Workshop on Knowledge Discovery, Knowledge Management and Decision Support (2013)
Mueen, A., Keogh, E.J., Zhu, Q., Cash, S., Westover, B.: Exact discovery of time series motifs. In: Proceedings of the SIAM International Conference on Data Mining, SDM 2009, pp. 473–484 (2009)
Naderi, S., et al.: The impact of age on the epidemiology of atrial fibrillation hospitalizations. Am. J. Med. 127(2), 158.e1–158.e7 (2014)
Nady, S., Moness, M., Massoud, M., Gharieb, R.: Combining continuous wavelet transform and Teager-Kaiser Energy operator for ECG arrhythmia detection. In: 8th Cairo International Biomedical Engineering Conference (CIBEC), pp. 76–79. IEEE (2016)
Padmavathi, S., Ramanujam, E.: Naïve Bayes classifier for ECG abnormalities using multivariate maximal time series Motif. Procedia Comput. Sci. 47, 222–228 (2015)
Sánchez-Cauce, R., Pérez-MartÃn, J., Luque, M.: Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Comput. Meth. Program. Biomed. 204, 106045 (2021)
Sun, Y., Zhu, L., Wang, G., Zhao, F.: Multi-input convolutional neural network for flower grading. J. Electr. Comput. Eng. 2017, 9240407:1–9240407:8 (2017)
Ventura, G., Benvenuti, E. (eds.): Advances in Discretization Methods. SSSS, vol. 12. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41246-7
Wang, X., Smith, K., Hyndman, R.: Characteristic-based clustering for time series data. Data Min. Knowl. Disc. 13, 335–364 (2006). https://doi.org/10.1007/s10618-005-0039-x
Wankhedkar, R., Jain, S.K.: Motif discovery and anomaly detection in an ECG using matrix profile. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, K.-C. (eds.) Progress in Advanced Computing and Intelligent Engineering. AISC, vol. 1198, pp. 88–95. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-6584-7_9
Yeh, C.C.M., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes Motifs, Discords and Shapelets. In: IEEE 16th International Conference on Data Mining (ICDM), pp. 1317–1322. IEEE (2016)
Zhao, Z., Särkkä, S., Rad, A.B.: Spectro-temporal ECG analysis for atrial fibrillation. In: Proceedings of the 28th International Workshop on Machine Learning for Signal Processing, MLSP 2018 (2018)
Zhu, Y., Yeh, C.C.M., Zimmerman, Z., Kamgar, K., Keogh, E.: Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds. In: IEEE International Conference on Data Mining (ICDM), pp. 837–846. IEEE (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Aldosari, H., Coenen, F., Lip, G.Y.H., Zheng, Y. (2021). Addressing the Challenge of Data Heterogeneity Using a Homogeneous Feature Vector Representation: A Study Using Time Series and Cardiovascular Disease Classification. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-91100-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91099-0
Online ISBN: 978-3-030-91100-3
eBook Packages: Computer ScienceComputer Science (R0)