Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes. It has become an essential public health crisis, especially for care in the home. Synchronized electrocardiogram (ECG) and photoplethysmography (PPG) signals were obtained from healthy non-diabetic (n = 37) and diabetic (n = 85) subjects without peripheral neuropathy, recruited from the diabetic outpatient clinic. The conventional parameters, including low-/high-frequency power ratio (LHR), small-scale multiscale entropy index (MEI_SS), large-scale multiscale entropy index (MEI_LS), electrocardiogram-based pulse wave velocity (PWV_mean), and percussion entropy index (PEI), were computed as baseline and were then followed for six years after the initial PEI measurement. Three new diabetic subgroups with different PEI values were identified for the goodness-of-fit test and Cox proportional Hazards model for relative risks analysis. Finally, Cox regression analysis showed that the PEI value was significantly and independently associated with the risk of developing DPN after adjustment for some traditional risk factors for diabetes (relative risks = 4.77, 95% confidence interval = 1.87 to 6.31, p = 0.015). These findings suggest that the PEI is an important risk parameter for new-onset DPN as a result of a chronic complication of diabetes and, thus, a smaller PEI value can provide valid information that may help identify type 2 diabetic patients at a greater risk of future DPN.

This study investigated the application of a modified percussion entropy index (PEI_PPI) in assessing the complexity of baroreflex sensitivity (BRS) for diabetic peripheral neuropathy prognosis. The index was acquired by comparing the obedience of the fluctuation tendency in the change between the amplitudes of continuous digital volume pulse (DVP) and variations in the peak-to-peak interval (PPI) from a decomposed intrinsic mode function (i.e., IMF6) through ensemble empirical mode decomposition (EEMD). In total, 100 middle-aged subjects were split into 3 groups: healthy subjects (group 1, 48–89 years, n = 34), subjects with type 2 diabetes without peripheral neuropathy within 5 years (group 2, 42–86 years, n = 42, HbA1c ≥ 6.5%), and type 2 diabetic patients with peripheral neuropathy within 5 years (group 3, 37–75 years, n = 24). The results were also found to be very successful at discriminating between PEI_PPI values among the three groups (p < 0.017), and indicated significant associations with the anthropometric (i.e., body weight and waist circumference) and serum biochemical (i.e., triglycerides, glycated hemoglobin, and fasting blood glucose) parameters in all subjects (p < 0.05). The present study, which utilized the DVP signals of aged, overweight subjects and diabetic patients, successfully determined the PPI intervals from IMF6 through EEMD. The PEI_PPI can provide a prognosis of peripheral neuropathy from diabetic patients within 5 years after photoplethysmography (PPG) measurement.

The percussion entropy index (PEI_orginal) was recently introduced to assess the complexity of baroreflex sensitivity. This study aimed to investigate the ability of a speedy modified PEI (i.e., PEI_NEW) application to distinguish among age-controlled subjects with or without diabetes. This was carried out using simultaneous photo-plethysmo-graphy (PPG) pulse amplitude series and the R wave-to-R wave interval (RRI) series acquired from healthy subjects (Group 1, number = 42), subjects diagnosed as having diabetes mellitus type 2 with satisfactory blood sugar control (Group 2, number = 38), and type 2 diabetic patients with poor blood sugar control (Group 3, number = 35). Results from PEI_orginal and multiscale cross-approximate entropy (MCAE) were also addressed with the same datasets for comparison. The results show that optimal prolongation between the amplitude series and RRI series could be delayed by one to three heartbeat cycles for Group 2, and one to four heartbeat cycles for Group 3 patients. Group 1 subjects only had prolongation for one heartbeat cycle. This study not only demonstrates the sensitivity of PEI_NEW and PEI_orginal in differentiating between Groups 2 and 3 compared with MCAE, highlighting the feasibility of using percussion entropy applications in autonomic nervous function assessments, it also shows that PEI_NEW can considerably reduce the computational time required for such processes.

Diabetic peripheral neuropathy (DPN) is a very common neurological disorder in diabetic patients. This study presents a new percussion-based index for predicting DPN by decomposing digital volume pulse (DVP) signals from the fingertip. In this study, 130 subjects (50 individuals 44 to 89 years of age without diabetes and 80 patients 37 to 86 years of age with type 2 diabetes) were enrolled. After baseline measurement and blood tests, 25 diabetic patients developed DPN within the following five years. After removing high-frequency noise in the original DVP signals, the decomposed DVP signals were used for percussion entropy index (PEI_DVP) computation. Effects of risk factors on the incidence of DPN in diabetic patients within five years of follow-up were tested using binary logistic regression analysis, controlling for age, waist circumference, low-density lipoprotein cholesterol, and the new index. Multivariate analysis showed that patients who did not develop DPN in the five-year period had higher PEI_DVP values than those with DPN, as determined by logistic regression model (PEI_DVP: odds ratio 0.913, 95% CI 0.850 to 0.980). This study shows that PEI_DVP can be a major protective factor in relation to the studied binary outcome (i.e., DPN or not in diabetic patients five years after baseline measurement).

A microcirculation microscope has recently been introduced to reveal finger blood flow changes by visualization, before and after using far-infrared fabric. Digital volume pulses (DVPs) from the dominant index fingertip of healthy young subjects (Group 1, n = 66) and healthy upper middle-aged subjects (Group 2, n = 33) were acquired through a photoplethysmographic electrical device (PED). By using the one intrinsic mode function (i.e., IMF5), an instantaneous frequency difference (Δf_Emax) was revealed through the second part of the Hilbert–Huang transformation. Parameters from DVPs in the time domain, i.e., the stiffness index, crest time, crest time ratio, and finger perfusion index, were also obtained for comparison. The results showed significant differences in FPI and Δf_Emax between the two groups (p = 0.002 and p = 0.043, respectively). A significant Δf_Emax was also noted for the two groups under the effects of far-infrared radiation (FIR) (Group 1: p = 0.046; Group 2: p = 0.002). In conclusion, this study aimed to validate a self-developed and economical device, with a good extensibility, which can be operated in a domestic setting, and to demonstrate that the PED performed quantitative indexes on finger blood flow comparable to those investigated through a microcirculation microscope.

Early non-invasive approaches for detecting diabetic peripheral neuropathy (DPN) are crucial to preventing its severe complications. However, these approaches have been limited by insufficient dynamic feature capture, low model efficiency, and poor portability. To improve the non-invasive detection capability for DPN, a novel combined method based on the fusion of PPG and ECG signals is proposed. Firstly, an adaptive denoising method integrating ICEEMDAN-based signal decomposition, wavelet thresholding, and particle swarm optimization is adopted to improve signal quality. Secondly, a combined encoding framework, integrating spatial position encoding, Grampian angular field, and recurrence plot, is employed to transform one-dimensional time-series signal segments into RGB color maps. Finally, an enhanced lightweight network named Afsharid, incorporating multi-branch depth wise convolution and a spatial hybrid self-attention mechanism, is designed to generate fused RGB representations. On the multi-cycle dataset, the proposed model achieved an accuracy of 93.89%, a sensitivity of 93.21%, and a precision of 94.52%. Compared with the best-performing baseline model EfficientNetV2, the accuracy was improved by 6.52%. The results show the feasibility and potential of the combined method as a new solution for early detection and daily monitoring of DPN.