Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
Artificial Neural Network Expert System for Integrated Heart Rate Variability
Wireless Personal Communications: An International Journal
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An automated diagnostic system for diabetes mellitus (DM), from heart rate variability (HRV) measures, using feed forward neural network has been developed. Changes in autonomic nervous system activity caused by DM are quantified by means of time domain and frequency domain analysis of HRV. Electrocardiograms of 70 DM patients and 65 healthy volunteers were recorded. Nine time domain measures--standard deviation of all NN intervals, square root of mean of sum of squares of differences between adjacent NN interval (RMSSD), number of adjacent NN intervals differing more than 50 ms. (NN50 count), percentage of NN50 count, R-R triangular index, triangular interpolation of NN intervals (TINN), standard deviation of the mean heart rate, mean R-R interval and mean heart rate--were used as the input features to the neural network. This diagnostic system classifies DM patients and normal volunteers from morphologically identical ECGs. Diagnostic results show that the system is performing well with an accuracy of 93.08%, specificity of 96.92% and sensitivity of 89.23%.