Abstract
Cardiovascular disease (CVD) is the leading cause of death around the world. Researches on assessing patients death risk from Electrocardiographic (ECG) data has attracted increasing attention recently. In this paper, we summarize long-term overwhelming ECG data using morphological concern of overall evolution. And then assessing patients death risk from high value density ECG summarization instead of raw data. Our method is totally unsupervised without the help of expert knowledge. Moreover, it can assist in clinical practice without any additional burden like buy new devices or add more caregivers. Comprehensive results show effectiveness of our method.
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Mozaffarian, D., Benjamin, E.J., Go, A.S., Arnett, D.K., Blaha, M.J., Cushman, M., de Ferranti, S., Despres, J.P., Fullerton, H.J., Howard, V.J., et al.: Heart disease and stroke statistics-2015 update: a report from the american heart association. Circulation 131(4), e29 (2015)
Nichols, M., Townsend, N., Scarborough, P., Rayner, M.: Cardiovascular disease in Europe 2014: epidemiological update. Eur. Heart J. 35(42), 2950–2959 (2014)
Ballantyne, C.M., Hoogeveen, R.C., Bang, H., Coresh, J., Folsom, A.R., Heiss, G., Sharrett, A.R.: Lipoprotein-associated phospholipase A2, high-sensitivity C-reactive protein, and risk for incident coronary heart disease in middle-aged men and women in the Atherosclerosis Risk in Communities (aric) study. Circulation 109(7), 837–842 (2004)
Vasan, R.S., Larson, M.G., Benjamin, E.J., Evans, J.C., Reiss, C.K., Levy, D.: Congestive heart failure in subjects with normal versus reduced left ventricular ejection fraction: prevalence and mortality in a population-based cohort. J. Am. Coll. Cardiol. 33(7), 1948–1955 (1999)
Chia, C.C., Blum, J., Karam, Z., Singh, S., Syed, Z.: Predicting postoperative atrial fibrillation from independent ECG components. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Bender, J., Russell, K., Rosenfeld, L., Chaudry, S.: Oxford American Handbook of Cardiology. Oxford University Press, New York (2010)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)
Zong, W., Moody, G., Jiang, D.: A robust open-source algorithm to detect onset and duration of QRS complexes. In: Computers in Cardiology, pp. 737–740. IEEE (2003)
Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: VLDB (2000)
Keogh, E., Lin, J., Fu, A.: HOT SAX: efficiently finding the most unusual time series subsequence. In: Fifth IEEE International Conference on Data Mining, 8 p. IEEE (2005)
Wu, H., Salzberg, B., Zhang, D.: Online event-driven subsequence matching over financial data streams. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 23–34. ACM (2004)
Tang, L.a., Cui, B., Li, H., Miao, G., Yang, D., Zhou, X.: Effective variation management for pseudo periodical streams. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 257–268. ACM (2007)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings IEEE International Conference on Data Mining, ICDM 2001, pp. 289–296. IEEE (2001)
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., et al.: PhysioBank, PhysioToolkit, and PhysioNet – components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Chiang, H.S., Shih, D.H., Lin, B., Shih, M.H.: An APN model for arrhythmic beat classification. Bioinformatics 30(12), 1739–1746 (2014)
Tafreshi, R., Jaleel, A., Lim, J., Tafreshi, L.: Automated analysis of ECG waveforms with atypical QRS complex morphologies. Biomed. Sig. Process. Control 10, 41–49 (2014)
Mar, T., Zaunseder, S., MartÃnez, J.P., Llamedo, M., Poll, R.: Optimization of ECG classification by means of feature selection. IEEE Trans. Biomed. Eng. 58(8), 2168–2177 (2011)
Chia, C.C., Syed, Z.: Scalable noise mining in long-term electrocardiographic time-series to predict death following heart attacks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 125–134. ACM (2014)
Huikuri, H.V., Stein, P.K.: Heart rate variability in risk stratification of cardiac patients. Prog. Cardiovasc. Dis. 56(2), 153–159 (2013)
Knaus, W.A., Zimmerman, J.E., Wagner, D.P., Draper, E.A., Lawrence, D.E.: Apache-acute physiology and chronic health evaluation: a physiologically based classification system. Crit. Care Med. 9(8), 591–597 (1981)
Le Gall, J.R., Loirat, P., Alperovitch, A., Glaser, P., Granthil, C., Mathieu, D., Mercier, P., Thomas, R., Villers, D.: A simplified acute physiology score for ICU patients. Crit. Care Med. 12(11), 975–977 (1984)
Teasdale, G., Jennett, B.: Assessment of coma and impaired consciousness: a practical scale. Lancet 304(7872), 81–84 (1974)
Grmec, S., Gasparovic, V.: Comparison of APACHE II, MEES and Glasgow Coma Scale in patients with nontraumatic coma for prediction of mortality. CRITICAL CARE-LONDON- 5(1), 19–23 (2001)
Sakr, Y., Krauss, C., Amaral, A., Réa-Neto, A., Specht, M., Reinhart, K., Marx, G.: Comparison of the performance of SAPS II, SAPS 3, APACHE II, and their customized prognostic models in a surgical intensive care unit. Br. J. Anaesth. 101(6), 798–803 (2008)
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This work was supported by Natural Science Foundation of China (No. 61170003).
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Hong, S., Wu, M., Zhang, J., Li, H. (2017). Assessing Death Risk of Patients with Cardiovascular Disease from Long-Term Electrocardiogram Streams Summarization. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_52
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DOI: https://doi.org/10.1007/978-3-319-57454-7_52
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