The following is a summary of “Electrodermal signal analysis using continuous wavelet transform as a tool for quantification of sweat gland activity in diabetic kidney disease,” published in the July 2023 issue of Nephrology by Singaram et al.
EDA is a measure of sudomotor function, which is the ability of the autonomic nervous system to control sweat gland activity. EDA signals can be used to quantify sudomotor function because they measure changes in the electrical conductance of the skin, which can be affected by sweat production.
Researchers performed a retrospective study to develop a methodology that uses electrical stimulation, a specific sampling frequency, and a signal-processing algorithm. Study included 20 volunteers with different types of diabetes. Volunteers were divided into 4 groups: controls, people with diabetes, people with diabetic nephropathy, and people with diabetic neuropathy. They used a method to stimulate the volunteers’ skin conductance activity (SG). They measured the resulting electrodermal activity (EDA) signal patterns and found that the EDA signal patterns of 4 groups were different. They used a scalogram and time-averaged spectra to visualize and quantify these differences. Results showed that the energy value in controls was high and gradually decreased in other groups. This indicated SG activity declines in diabetes prognosis. The correlation between the acquired results and the standard lab procedure was 0.99. Cohen’s d-value for all groups was less than 0.25, indicating a minimal effect size. Therefore, the obtained result is validated and statistically analyzed for individual variations.
Study concluded that the findings have the potential to be developed into a device that could prevent diabetic kidney disease.
