Abstract
Diabetes is a malfunctioning disease which increases the risk of cardiac autonomic neuropathy. People with Diabetes mellitus are over two times as probable to ensure a heart stroke as compared to those people who don’t have diabetes. This research work is to examine heart rate changeability in type-2 diabetes mellitus patients (T2DM) versus non-diabetic patients. The linear and Poincare plot analytical techniques are applied on ECG signals to determine the pathological and physiological status, like sympathetic variation and parasympathetic variation in the heart. At the initial stages of the analysis, the removal of various noises from the corrupted ECG signal by different types of filters such as Savitzky-Golay least square polynomial filter, Butterworth filter and Wavelet technique are employed. The electrocardiogram signal is performed for efficient detection of all peaks by QRS segment, P-wave, Q-wave, T-wave and ON-set, OFF-set on each beats. In the Next phase of the analysis RR interval, QT-interval, QT-dispersion, ST-depressions are computed from the filtered ECG signal for identification of Diabetes. The comparison is carried out on the basis of Signal to Noise Ratio among filter techniques for their better results.
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Saraswat, M., Wadhwani, A.K., Wadhwani, S. (2020). Non-invasive Estimation of HRV Performance for Diabetes Mellitus with Cardiac Disorder on the Basis of Time-Frequency and Poincare Plot Analysis. In: Pandit, M., Srivastava, L., Venkata Rao, R., Bansal, J. (eds) Intelligent Computing Applications for Sustainable Real-World Systems. ICSISCET 2019. Proceedings in Adaptation, Learning and Optimization, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-44758-8_42
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DOI: https://doi.org/10.1007/978-3-030-44758-8_42
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