Mathematical Model of Sino-Atrial Node Used in Assessment of Neuropathy and Cardiac Health in Diabetics

Manjusha Joshi; K.D. Desai; M.S. Menon
This paper proposes a novel mathematical model to demonstrate the change in neural signal conduction in diabetic condition with and without hypertension. It assesses the risk factors of neuropathy and associated cardiac diseases like myocardial ischemia/infarction. Background: Patients with prolonged diabetes are more likely to develop diabetic neuropathy. Neuropathy is the slow deterioration of the functioning of the nerves. Proximal neuropathy causes lack of sensation in peripheral nerves. Initially, no clinical symptoms are visible. The slow and progressive deterioration may lead to gangrene and foot amputation. To stop this irreversible process, early diagnosis at the preclinical stage is critical. The mathematical model developed in this paper demonstrates the reduced signal conduction under diabetic conditions. Reduction of signal conduction is a preclinical marker to peripheral neuropathy. Methodology: Signal conduction changes can be verified by changes in power spectral density of the electrocardiogram ECG signal by heart rate variability (HRV) analysis. The Effects of these changes on the performance of the heart can be evaluated by the Left Ventricular Ejection Fraction (LVEF). Extraction of inter-beat R-R intervals (R wave to R interval) from 3-5 minutes long ECG signal is used to generate the information about the neural signal conduction. Total power is then obtained from conducting an HRV analysis of the R-R interval samples. This analysis is performed using Kubios HRV simulator. The performance index of the heart is acquired from the echocardiograms of the samples. Data samples of different cohorts mentioned in the Table 1 are acquired. Details of data samples: The sample size of each cohort and the average age group is as stated in Table 1. All the cases are recorded at Fortis-S.L. Raheja hospital, Mahim (W). Randomness in age, class, sex and other parameters was ensured on the basis of the data collection as per the registration of the subjects. TABLE 1 DETAILS OF THE DATA Type of cohort Normal Diabetic Diabetic with IHD and INHD Diabetic and hypertensive Hypertensive Sample size 27 20 20 27 23 Average age group 47 58 61 62 68 Experimental Results: In this paper, a mathematical model is proposed in the paper for calculating the strength of neural signal conduction. The average neural signal conduction of diabetic cohort is found to be reduced compared to the non-diabetic cohort. The diabetic cohort with myocardial ischemia/infarction is found to have with least average neural signal conduction followed by the diabetic and hypertensive cohort. In case of hypertensive cohorts, the average neural signal conduction is found to be equal to that of the normal cohort. The effect of the neural conduction can be observed on the total power spectral density from the frequency domain AR analysis of R-R intervals. The average Left Ventricular Ejection Fraction (LVEF) index retrieved from the echocardiogram in all the cohorts is found to be consistent with the reduced neural signal conduction. The above mentioned indices can be preclinical indicators of assessment of neuropathy and cardiac health. Conclusion: preclinical diagnosis of autonomic neuropathy and cardiac health assessment is possible in diabetic subjects.
Neuropathy; Myocardial Ischemia/Infarction; HRV Analysis; Left Ventricular Ejection Fraction
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