Abstract
Automated ECG analysis and classification are nowadays a fundamental tool for monitoring patient heart activity properly. The most important features used in literature are the raw data of a time window, the temporal attributes and the frequency information from the eigenvector techniques. This paper compares these approaches from a topological point of view, by using linear and nonlinear projections and a neural network for assessing the corresponding classification quality. The nonlinearity of the feature data manifold carries most of the QRS-complex information. Indeed, it yields high rates of classification with the smallest number of features. This is most evident if temporal features are used: Nonlinear dimensionality reduction techniques allow a very large data compression at the expense of a slight loss of accuracy. It can be an advantage in applications where the computing time is a critical factor. If, instead, the classification is performed offline, the raw data technique is the best one.
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References
Thakor, N.V., Zhu, Y.S.: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991)
Arif, M., Akram, M., Minhas, F.: Pruned fuzzy K-nearest neighbor classifier for beat classification. J. Biomed. Sci. Eng. 3, 380–389 (2010)
El-Khafif, S.H., El-Brawany, M.A.: Artificial Neural Network-Based Automated ECG Signal Classifier. ISRN Biomedical Engineering (2013)
Vijaya, G., Kumar, V., Verma, H.K.: ANN-based QRS-complex analysis of ECG. J. Med. Eng. Technol. 22(4), 160–167 (1998)
Randazzo, V., Pasero, E., Navaretti, S.: VITAL-ECG: a portable wearable hospital. In: 2018 IEEE Sensors Applications Symposium (SAS), pp. 1–6, Seoul, Korea (South) (2018)
Gambarotta, N., Aletti, F., Baselli, G., et al.: A review of methods for the signal quality assessment to improve reliability of heart rate and blood pressures derived parameters. Med. Biol. Eng. Comput. 1025(7), 1025–1035 (2016)
Bevilacqua, V., Carnimeo, L., Mastronardi, G., Santarcangelo, V., Scaramuzzi, R.: On the comparison of NN-based architectures for diabetic damage detection in retinal images. J. Circuits Syst. Comput. 18(08), 1369–1380 (2009)
Brunetti, A., Buongiorno, D., Trotta, G.F., Bevilacqua, V.: Computer vision and deep learning techniques for pedestrian detection and tracking: a survey. Neurocomputing 300, 17–33 (2018)
Bevilacqua, V., Pietroleonardo, N., Triggiani, V., Brunetti, A., Di Palma, A.M., Rossini, M., Gesualdo, L.: An innovative neural network framework to classify blood vessels and tubules based on Haralick features evaluated in histological images of kidney biopsy. Neurocomputing 228, 143–153 (2017)
Cavalcanti Roza, V.C., De Almeida, A.M., Postolache, O.A.: Design of an artificial neural network and feature extraction to identify arrhythmias from ECG. In: 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 391–396. Rochester, MN (2017)
Cuesta-Frau, D., Pérez-Cortés, J.C., Andreu-Garcı́a, G.: Clustering of electrocardiograph signals in computer-aided Holter analysis. Comput. Methods Programs Biomed. 72(3), 179–196 (2003)
Pisarenko, V.F.: The retrieval of harmonics from a covariance function. Geophys. J. Int. 33(3), 347–366 (1973)
Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)
Übeyli, E.D.: Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digit. Signal Proc. 19(2), 320–329 (2009)
Übeyli, E.D., Cvetkovic, D., Cosic, I.: Analysis of human PPG, ECG and EEG signals by eigenvector methods. Digit. Signal Proc. 20(3), 956–963 (2010)
Zgallai, W., Sabry-Rizk, M., Hardiman, P., O’Riordan, J.: MUSIC-based bispectrum detector: a novel non-invasive detection method for overlapping fetal and mother ECG signals. In: 19th Annual International Conference Proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 72–75. IEEE, Chicago, IL (1997)
Demartines, P., Herault, J.: Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Trans. Neural Netw. 8(1), 148–154 (1997)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH Arrhythmia Database. IEEE Eng. Med. Biol. 20(3), 45–50 (2001)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, PCh., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)
MIT-BIH Arrhythmia Database. https://www.physionet.org/physiobank/database/mitdb/. Last accessed 19 April 2018
MIT-BIH Arrhythmia Database Directory. https://www.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm
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Cirrincione, G., Randazzo, V., Pasero, E. (2020). A Neural Based Comparative Analysis for Feature Extraction from ECG Signals. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_23
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DOI: https://doi.org/10.1007/978-981-13-8950-4_23
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