Active learning applied to patient-adaptive heartbeat classification

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Wiens, Jenna ^a   Guttag, John V ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); SUPERVISED LEARNING;

ACTIVE LEARNING; BENCHMARK DATABASE; CLASS IMBALANCE; CLASSIFICATION TASKS; ELECTROCARDIOGRAM RECORDINGS; HEARTBEAT CLASSIFICATIONS; HIGH COSTS; MEDICAL INSTRUMENTATION; PERFORMANCE; SUPERVISED MACHINE LEARNING;

ELECTROCARDIOGRAMS;

EID: 85161990649     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. D. V. Exner, K. M. Kavanagh, M. P. Slawnych et al, and for the REFINE Investigators. Noninvasive risk assessment early after a myocardial infarction: The REFINE study. J Am Coll Cardiol, 50(24):2275-2284, 2007.
2. Z. Syed, B. Scirica, S. Mohanavel, P. Sung, C. Cannon, P. Stone, C. Stultz, and J. V. Guttag. Relation to death within 90 days of non-st-elevation acute coronary syndromes to variability in electrocardiographic morphology. Am J of Cardiol, 103(3), 2009.
3. P. de Chazal and R. B. Reilly. A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features. Biomedical Engineering, IEEE Transactions on, 53(12):2535-2543, Dec. 2006.
4. Y. H. Hu, S. Palreddy, and W.J. Tompkins. A Patient-Adaptable ECG Beat Classifier Using a Mixture of Experts Approach. Biomedical Engineering, IEEE Transactions on, 44(9):891-900, Sept. 1997.
5. T. Ince, S. Kiranyaz, and M. Gabbouj. A generic and robust system for automated patient-specific classification of ecg signals. IEEE Transactions on Biomedical Engineering, 56(5), May 2009.
6. P. Hamilton. Open Source ECG Analysis. In Computers in Cardiology, volume 29, pages 101-104, 2002.
7. J. Bigger, F. Dresdale, and R. Heissenbuttel et. al. Ventricular arrhythmias in ischemic heart disease: mechanism, prevalence, significance, and management. Prog Cardiovasc Dis, 19:255, 1977.
8. T. Smilde, D. van Veldhuisen, and M. van den Berg. Prognostic value of heart rate variability and ventricular arrhythmias during 13-year follow up in patients with mild to moderate heart failure. Clinical Research in Cardiology, 98(4):233-239, 2009.
9. A. L. Goldberger, L. A. N. Amaral, and L. Glass et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000 (June 13). Circulation Electronic Pages: http://circ.ahajournals.org/cgi/content/full/101/23/e215.
10. P. de Chazal, M. O'Dwyer, R. B. Reilly, and Senior Member. Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features. IEEE Transactions on Biomedical Engineering, 51:1196-1206, 2004.
11. K. Sternickel. Automatic pattern recognition in ecg time series. In Computer Methods and Programs in Biomedicine, Vol: 68, pages 109-115, 2002.
12. Z. Syed, J. Guttag, and C. Stultz. Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-term Data Using Limited Prior Knowledge. EURASIP Journal on Advances in Signal Processing, 2007:97-112, 2007.
13. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2:45-66, 2002.
14. S. Dasgupta and D. Hsu. Hierarchical sampling for active learning. In ICML '08: Proceedings of the 25th international conference on Machine learning, pages 208-215, New York, NY, USA, 2008. ACM.
15. Z. Xu, K. Yu, V. Tresp, X. Xu, and J. Wang. Representative sampling for text classification using support vector machines. In Proceedings of the twenty-fifth European Conference on Information Retrieval, pages 393-407. Springer, 2003.
16. H.T. Nguyen and A. Smeulders. Active learning using pre-clustering. In Proceedings of the twenty-first international conference on Machine learning, page 79, New York, NY, USA, 2004. ACM.
17. J. H. Ward. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301):234-244, 1963.
18. S. Kamvar, D. Klein, and C. Manning. Interpreting and extending classical agglomerative clustering algorithms using a model-based approach. In Proceedings of nineteenth International Conference on Machine Learning, pages 283-290, 2002.
19. T. Joachims. Making Large-scale Support Vector Machine Learning Practical. MIT Press, Cambridge, MA, USA, 1999.
20. M. Sokolova, N. Japkowicz, and S. Szpakowicz. Beyond Accuracy, F-score and ROC: a Family of Discriminant Measures for Performance Evaluation, volume 4304 of Lecture Notes in Computer Science, pages 1015-1021. Springer Berlin/Heidelberg, 2006.