Heart rate is a non-stationary signal and provides a powerful interplay between the sympathetic and parasympathetic nervous systems. The heart rate variation signal can reveal disorders associated with how these nervous systems regulate the heart rate, and hence may contain indicators of this disease state, or warnings about impending or future cardiac diseases. These indicators may be present at all times or may occur at random during certain intervals in the time scale. It is difficult and time consuming to pinpoint these abnormalities in a huge cardiac data set. Heart rate variability (HRV) constitutes a tool for assessing the activities of the autonomic nervous system (ANS). In this work, we have proposed a computer based analytical system to determine the HRV, and analyzed it to obtain HRV Power-spectrum for normal, diabetes and diabetes with neuropathy subjects in deep breathing, standing and supine position. We have then designated indices based on the HRV power-spectra power values and frequency shift of these peaks from their normal frequency values. We have shown the efficacy and sensitivity of these indices, to differentiate between normals, diabetics and diabetics with ischemic heart disease. Thus we have demonstrated how effectively these HRV power-spectral indices can enable diagnosis of diabetic autonomic neuropathy. Finally, we have composed an integrated index made up of these power-spectral indices, to facilitate distinguishing and diagnosing diabetic autonomic neuropathy in terms of just one index or number.
An analysis of spectrum components and CCV of heart rate variability on diabetic autonomic neuropathy
