Electrophotonic Imaging Based Analysis of Diabetes

Abstract

All metabolic processes involve electron transfer mechanisms Eletrophotonic Imaging EPI captures the coronal discharge around the finger as a result of electron capture from the ten fingers The coronal discharge around each fingertip supposedly represents the energy state of the corresponding organs and organ systems The Periodic assessment of blood sugar is essential for the management of diabetes An EPI based measurement process could be an alternative nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp Electrophotonic Imaging EPI data is collected from subjects including males and females in the age group of to years from a diabetic center in Bangalore The EPI data was captured from all the ten fingers from the subjects who came for the regular blood test The EPI data corresponding to the meridians of the ring finger chakras organs and organ systems related to diabetes were analyzed using general linear model in IBM SPSS version A built in neural network classifier from IBM SPSS was used to classify diabetic subjects from non diabetic subjects The mean and standard deviation values for pancreas were and for the diabetic subjects and and for the non diabetic subjects Similarly for the hypothalamus the mean and standard deviation values were and for diabetes and and for non diabetic subjects The units for all the measurements are pixel units and represent empirical value for photonic energy The classification accuracy of the neural network classifier was in the range of to EPI parameters had a statistically significant difference from the non diabetic subjects EPI data could be refined more to make use of it as a bio metric for diabetes