Heartbeat Classification based on Combinational Feature Selection Method for Analysing Cardiac Disorders
Conference: Creative Trends in Engineering and Technology
Electrocardiogram (ECG) gives the information of electrical activity of the heart. ECG and heart rate gives the\ncondition of cardiac health. Analysis of non linear features of ECG signal for arrhythmia characterization is considered. Time\ndomain and frequency domain analysis is done on the ECG signal which can be useful in arrhythmia detection. The statistical\nparameters in time domain which have been considered are the standard deviation of the NN intervals (SDNN) and the root\nmean square of successive difference intervals which are taken from heart rate signals (RMSSD). The frequency domain\nparameters which are considered is low frequency (LF), high frequency (HF) and LF/HF ratio and the analysis of normal to\nnormal interval (NN interval) data gives information of significant difference in very low frequency power, low frequency\npower and high frequency power. The indexes based on HRV prove a strong predictor of increased all-cause cardiac and/or\narrhythmic mortality, particularly in patients at risk after MI or with CHF. Algorithm was implemented in such a way that it\nwill surpass the limitations of existing algorithms. The paper also reveals the role physiological situations, various\npathological settings and its role along with HRV indexes for interpretation of subject health.
CTET - 2016