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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
Identification of different stages of diabetic retinopathy using retinal optical images
Information Sciences: an International Journal
Information Sciences: an International Journal
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Cross-fuzzy entropy: A new method to test pattern synchrony of bivariate time series
Information Sciences: an International Journal
Classification of the electrocardiogram signals using supervised classifiers and efficient features
Computer Methods and Programs in Biomedicine
ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
Superiority real-time cardiac arrhythmias detection using trigger learning method
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Compressed sampling for heart rate monitoring
Computer Methods and Programs in Biomedicine
Information Sciences: an International Journal
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Heart rate variability (HRV), a widely adopted quantitative marker of the autonomic nervous system can be used as a predictor of risk of cardiovascular diseases. Moreover, decreased heart rate variability (HRV) has been associated with an increased risk of cardiovascular diseases. Hence in this work HRV signal is used as the base signal for predicting the risk of cardiovascular diseases. The present study concerns nine cardiac classes that include normal sinus rhythm (NSR), congestive heart failure (CHF), atrial fibrillation (AF), ventricular fibrillation (VF), preventricular contraction (PVC), left bundle branch block (LBBB), complete heart block (CHB), ischemic/dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). A total of 352 cardiac subjects belonging to the nine classes were analyzed in the frequency domain. The fast Fourier transforms (FFT) and three other modeling techniques namely, autoregressive (AR) model, moving average (MA) model and the autoregressive moving average (ARMA) model are used to estimate the power spectral densities of the RR interval variability. The spectral parameters obtained from the spectral analysis of the HRV signals are used as the input parameters to the artificial neural network (ANN) for classification of the different cardiac classes. Our findings reveal that the ARMA modeling technique seems to give better resolution and would be more promising for clinical diagnosis.