Support Vector Machine Technique for Risk Assessment in Patients Suffering From Congestive Heart Failure via Heart Rate Variability Variant
- K.Abinaya
- Dr.M.Vijayakumar
Support Vector machine, CART, Optimized SVM, HRV, NYHA
The congestive heart failure is a major cause of concern among all types of cardiovascular problems and is attributed to imbalance in sympathetic and parasympathetic nervous systems. In an effort to improve the reliability of the detection of congestive heart failure, a method for utilizing phase shifts of cardiac and thoracic acoustics coupled with ECG signals is proposed. In this work, an automatic classifier for risk assessment in patients suffering from congestive heart failure is developed which yields consistency and consensus rate to identify risk. The proposed classifier separates lower risk from higher risk ones, using standard long-term heart rate variability (HRV) measures. Patients are labelled as low risk and high risk based on the depressed HRV. An algorithm for detection of congestive heart failure condition using the variability of instantaneous frequency of intrinsic mode functions obtained using Support vector machine will be determined with optimization of CART (Classification and Regression Tree ) System in order to proceed the satisfactory signal quality . The performance of the proposed system will yield better results in terms of sensitivity and specificity rate in identifying the high risk patients.
K.Abinaya, Dr.M.Vijayakumar. "Support Vector Machine Technique for Risk Assessment in Patients Suffering From Congestive Heart Failure via Heart Rate Variability Variant".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.3, Issue 2, pp.925-928, URL :https://rjwave.org/ijedr/papers/IJEDR1502162.pdf
Volume 3 Issue 2
Pages. 925-928
