Prediction of Sudden Cardiac Death using support vector machine

Sudden death from cardiac arrest is a major health problem and is responsible for almost half of all heart disease deaths. In Sudden Cardiac Death (SCD), the cardiac arrest occurs for a very short time which is preceded and followed by normal ECG. Thus, it is difficult to detect such conditions, using only ECG. This work predicts sudden cardiac arrest before 30 minutes of its occurrence on the basis of time domain and frequency domain features of Heart rate variability (HRV) obtained from ECG and using SVM classifier to classify SCD patient from Normal patient The database of cardiac patients obtained from physionet is used to check the validity of the proposed work Performance of SVM is better giving the classification efficiency of 88%.

[1]  M Malik,et al.  Short- and long-term assessment of heart rate variability for risk stratification after acute myocardial infarction. , 1996, The American journal of cardiology.

[2]  P. Ponikowski,et al.  Depressed heart rate variability as an independent predictor of death in chronic congestive heart failure secondary to ischemic or idiopathic dilated cardiomyopathy. , 1997, The American journal of cardiology.

[3]  J. C. BurgesChristopher.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[4]  Brahm Goldstein,et al.  Heart rate variability and critical illness: potential and problems. , 2000, Critical care medicine.

[5]  R. Maestri,et al.  Short-Term Heart Rate Variability Strongly Predicts Sudden Cardiac Death in Chronic Heart Failure Patients , 2003, Circulation.

[6]  S. Gunn,et al.  Machine Learning Can Improve Prediction of Severity in Acute Pancreatitis Using Admission Values of APACHE II Score and C-Reactive Protein , 2006, Pancreatology.

[7]  M. van Gils,et al.  Prediction of poor outcome using detector of epileptiform EEG in ICU patients resuscitated after cardiac arrest , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  N. Thakor,et al.  Predict the neurological recovery under hypothermia after cardiac arrest using C0 complexity measure of EEG signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  U. Rashed,et al.  Identification of sudden cardiac death using spectral domain analysis of Electrocardiogram(ECG) , 2008, 2008 4th International Conference on Emerging Technologies.

[10]  J. Vincent,et al.  Clinical review: Scoring systems in the critically ill , 2010, Critical care.

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  Nan Liu,et al.  Patient Outcome Prediction with Heart Rate Variability and Vital Signs , 2011, J. Signal Process. Syst..

[13]  Mithun M. Nayak,et al.  Mortality risk assessment for ICU patients using logistic regression , 2012, 2012 Computing in Cardiology.

[14]  Yongqin Li,et al.  An Algorithm Used for Ventricular Fibrillation Detection Without Interrupting Chest Compression , 2012, IEEE Transactions on Biomedical Engineering.