Computationally generated cardiac biomarkers: Heart rate patterns to predict death following coronary attacks

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 735-746

  Chia, Chih Chun ^a   Syed, Zeeshan ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMARKERS; CARDIOLOGY; DATA MINING; DEVELOPING COUNTRIES; DISEASES; ELECTROCARDIOGRAPHY; LARGE DATASET; PATIENT TREATMENT; POPULATION STATISTICS; SPACE DIVISION MULTIPLE ACCESS;

CARDIAC BIOMARKERS; CLINICAL APPLICATION; DEVELOPING WORLD; PATTERN DISCOVERY; RISK STRATIFICATION; SELECTION BASED; SEQUENTIAL PATTERNS; SURGICAL INTERVENTIONS;

HEART;

EID: 84880118725     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.63     Document Type: Conference Paper

References (23)

1. D. Lloyd-Jones, R. J. Adams, T. M. Brown, M. Carnethon, S. Dai, G. De Simone, T. B. Ferguson, E. Ford, K. Furie and C. Gillespie, Heart disease and stroke statistics-2010 update. A report from the American Heart Association, Circulation (2009) pp. e46-e215.
2. K. S. Reddy, Cardiovascular disease in non-Western countries, N Eng J Med (2004) pp. 2438-2440.
3. R. J. Myerburg, Sudden cardiac death: exploring the limits of our knowledge, J Cardiovasc Electrophysiol (2001) pp. 369-381.
4. B. J. Feder, Defibrillators are lifesaver, but risks give pause, New York Times (2008).
5. M. E. Josephson, Clinical Cardiac Electrophysiology: Techniques and Interpretations, Lippincott Williams & Wilkins (2008).
6. M. Malik and A. J. Camm, Dynamic Electrocardiography, Wiley-Blackwell (2004).
7. G. Clifford, F. Azuaje and P. McSharry, Advanced Methods And Tools for ECG Data Analysis, Artech House Publishers (2006).
8. Z. Syed, J. Guttag and C. Stultz, Clustering and symbolic analysis of cardiovascular signals: discovery and visualization of medically relevant patterns in longterm data using limited prior knowledge, EURASIP J Adv Sig Pr (2007), pp. 1-16.
9. Z. Syed, B. M. Scirica, S. Mohanavelu, P. Sung, E. L. Michelson, C. P. Cannon, P. H. Stone, C. M. Stultz and J. V. Guttag, Relation of death within 90 days of non-ST-elevation acute coronary syndromes to variability in electrocardiographic morphology, Am J Cardiol (2009), pp. 307-311.
10. Z. Syed and J. Guttag, Identifying patients at risk of major adverse cardiovascular events using symbolic mismatch, Neur Inf Proc Sys (2010).
11. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability: Standards of measurement, physiological interpretation and clinical use, Circulation (1996), pp. 1043-1065.
12. Z. Syed, P. Sung, B. M. Scirica, D. A. Morrow, C. M. Stultz and J. V. Guttag, Spectral energy of ECG morphologic differences to predict death, Cardiovasc Eng (2009), pp. 18-26.
13. C. P. Cannon, S. Husted, R. A. Harrington, B. M. Scirica, H. Emanuelsson, G. Peters and R. F. Storey, Safety, tolerability, and initial efficacy of AZD6140, the first reversible oral adenosine diphosphate receptor antagonist, compared With clopidogrel, in patients with non-ST-segment elevation acute coronary syndrome primary results of the DISPERSE-2 trial, J Am Coll Cardiol (2007), pp. 1844-1851.
14. D. A. Morrow, B. M. Scirica, E. Karwatowska-Prokopczuk, S. A. Murphy, A. Budaj, S. Varshavsky, A. A. Wolff, A. Skene, C. H. McCabe and E. Braunwald, Effects of ranolazine on recurrent cardiovascular events in patients with non-ST-elevation acute coronary syndromes: the MERLIN-TIMI 36 randomized trial, J Am Med Assoc (2007), pp. 1775-1783.
15. P.S. Hamilton and W.J. Tompkinsm, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database, IEEE Trans Biomed Eng (1986), pp. 1157-1165.
16. W. Zong, G.B. Moody and D. Jiang, A robust open-source algorithm to detect onset and duration of QRS complexes, Comput Cardiol (2003), pp. 737-740.
17. J. Lin, E. Keogh, S. Lonardi and B. Chiu. A symbolic representation of time series, with implications for streaming algorithms, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2003.
18. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions, Comm ACM (2006), pp. 117-122.
19. M. Datar, N. Immorlica, P. Indyk and V.S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions, Annual Symposium on Computational Geometry (2004), pp. 253-262.
20. Z. Syed, P. Indyk and J. Guttag. Learning approximate sequential patterns for classification, J Mach Learn Res (2009), pp. 1913-1936.
21. Q. Lv, W. Josephson, Z. Wang, M. Charikas and K. Li. Multi-probe LSH: efficient indexing for high-dimensional similar search, Very Large Data Bases (2007).
22. M.G. Shlipak, J.H. Ix, K. Bibbins-Domingo, F. Lin and M.A. Whooley. Biomarkers to predict recurrent cardiovascular disease: the Heart and Soul Study, Am J Med (2008), pp. 50-57.
23. Z. Syed, C. Stultz, M. Kellis, P. Indyk and J. Guttag. Motif discovery in physiological datasets: a methodology for inferring predictive elements, ACM Trans Knowl Discover Data (2010), pp. 1-23.