Assessing Death Risk of Patients with Cardiovascular Disease from Long-Term Electrocardiogram Streams Summarization

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.

[1]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[2]  Hongyan Li,et al.  Effective variation management for pseudo periodical streams , 2007, SIGMOD '07.

[3]  J. L. Gall,et al.  A simplified acute physiology score for ICU patients , 1984, Critical care medicine.

[4]  Sebastian Zaunseder,et al.  Optimization of ECG Classification by Means of Feature Selection , 2011, IEEE Transactions on Biomedical Engineering.

[5]  K. Reinhart,et al.  Comparison of the performance of SAPS II, SAPS 3, APACHE II, and their customized prognostic models in a surgical intensive care unit. , 2008, British journal of anaesthesia.

[6]  D. E. Lawrence,et al.  APACHE—acute physiology and chronic health evaluation: a physiologically based classification system , 1981, Critical care medicine.

[7]  B. Jennett,et al.  Assessment of coma and impaired consciousness. A practical scale. , 1974, Lancet.

[8]  George B. Moody,et al.  A robust open-source algorithm to detect onset and duration of QRS complexes , 2003, Computers in Cardiology, 2003.

[9]  Donghui Zhang,et al.  Online event-driven subsequence matching over financial data streams , 2004, SIGMOD '04.

[10]  Binshan Lin,et al.  An APN model for Arrhythmic beat classification , 2014, Bioinform..

[11]  Mark D. Huffman,et al.  AHA Statistical Update Heart Disease and Stroke Statistics — 2012 Update A Report From the American Heart Association WRITING GROUP MEMBERS , 2010 .

[12]  D. Levy,et al.  Congestive heart failure in subjects with normal versus reduced left ventricular ejection fraction: prevalence and mortality in a population-based cohort. , 1999, Journal of the American College of Cardiology.

[13]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[14]  P. Stein,et al.  Heart rate variability in risk stratification of cardiac patients. , 2013, Progress in cardiovascular diseases.

[15]  Zeeshan Syed,et al.  Scalable noise mining in long-term electrocardiographic time-series to predict death following heart attacks , 2014, KDD.

[16]  Heejung Bang,et al.  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 , 2004, Circulation.

[17]  Eamonn J. Keogh,et al.  HOT SAX: efficiently finding the most unusual time series subsequence , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[18]  Zeeshan Syed,et al.  Predicting Postoperative Atrial Fibrillation from Independent ECG Components , 2014, AAAI.

[19]  S. Grmec,et al.  Comparison of APACHE II, MEES and Glasgow Coma Scale in patients with nontraumatic coma for prediction of mortality , 2000, Critical care.

[20]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[21]  J. Bender,et al.  Oxford American Handbook Of Cardiology , 2010 .

[22]  Christos Faloutsos,et al.  Fast Time Sequence Indexing for Arbitrary Lp Norms , 2000, VLDB.

[23]  Peter Scarborough,et al.  Cardiovascular disease in Europe: epidemiological update. , 2014, European heart journal.

[24]  Reza Tafreshi,et al.  Automated analysis of ECG waveforms with atypical QRS complex morphologies , 2014, Biomed. Signal Process. Control..