Predicting heart beats using co-occurring constrained sequential patterns

Computing in Cardiology

Volumn 41, Issue January, 2014, Pages 265-268

  Ghosh, Shameek ^a   Feng, Mengling ^b,c   Nguyen, Hung ^a   Li, Jinyan ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIOLOGY; ELECTROCARDIOGRAPHY; STATISTICAL TESTS;

FALSE POSITIVE; FREQUENT SEQUENTIAL PATTERNS; HEART BEAT DETECTIONS; SEQUENTIAL PATTERNS; SEQUENTIAL-PATTERN MINING; SIGNAL ACTIVITY; SYMBOLIC SEQUENCE; TEST STATISTICS;

SIGNAL DETECTION;

EID: 84931375024     PISSN: 23258861     EISSN: 2325887X     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Ye C, Kumar BY, Coimbra MT. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Transactions on Biomedical Engineering 2012; 59: 2930-2941.
2. Osowski S, Hoai L T, Markiewicz T. Support vector machine-based expert system for reliable heartbeat recognition. IEEE Transactions on Biomedical Engineering 2004; 51: 582-589.
3. Osowski S, Markiewicz T, Hoai LT. Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement 2008; 41: 610-617.
4. Lin J, Keogh E, Lonardi S, Chiu B. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery 2003; 2-11.
5. Syed Z, Guttag J, Stultz C. 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 Applied Signal Processing 2007; 1: 97-97.
6. Syed Z, Stultz C, Kellis M, Indyk P, Guttag J. Motif discovery in physiological datasets: a methodology for inferring predictive elements. ACM Transactions on Knowledge Discovery from Data (TKDD) 20 10; 4:2.
7. Ayres J, Flannick J, Gehrke J, Yiu T. Sequential pattern mining using a bitmap representation. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining 2002; 429-435.
8. Ji X, Bailey J, Dong G. Mining minimal distinguishing subsequence patterns with gap constraints. Knowledge and Information Systems 2007; 11: 259-286.
9. Ohsaki M, Abe H, Tsumoto S, Yokoi H, Yamaguchi T. Evaluation of rule interestingness measures in medical knowledge discovery in databases. Artificial Intelligence in Medicine 2007; 41: 177-196.
10. Li C, Wang J. Efficiently Mining closed subsequences with gap constraints. In SDM 2008: 313-322.
11. Li C, Yang Q, Wang J, Li M. Efficient mining of gapconstrained subsequences and its various applications. ACM Transactions on Knowledge Discovery from Data (TKDD) 2012; 6: 2.
12. McMillan S, Chia C-C, Esbroeck AY, Rubinfeld I, Syed Z. ICU Mortality prediction using time series motifs. Computing in Cardiology 2012; 39:265-268.