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Automated Diagnosis of Diabetes Using Heart Rate Variability Signals

Abstract

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%.

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Acknowledgement

We are grateful to Dr. Jaisy Thomas, MD., RMO, PHC Pulingome for providing necessary facilities for recording her diabetic patients ECGs and other details of the patients including their BP, habits and other relevant information.

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Correspondence to Ahamed Seyd P.T..

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P.T., A.S., Joseph, P.K. & Jacob, J. Automated Diagnosis of Diabetes Using Heart Rate Variability Signals. J Med Syst 36, 1935–1941 (2012). https://doi.org/10.1007/s10916-011-9653-x

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Keywords

  • Autonomic nervous system
  • Diabetes mellitus
  • Feed forward neural network
  • Heart rate variability