Assessing the impact of gait speed on gait stability using multi-scale entropy fused with plantar pressure signals.

Introduction: Walking speed can affect gait stability and increase the risk of falling. Methods: In this study, we design a device to measure the distribution of the plantar pressure to investigate the impact of the walking speed on the stability of the human gait and movements of the body. We fused the entropy acquired at multiple scales with signals of the plantar pressure to evaluate the effects of the walking speed on the stability of the human gait. We simultaneously collected data on the motion-induced pressure from eight plantar regions to obtain the fused regional pressure. To verify their accuracy, we obtained data on the plantar pressure during walking by using the force table of the Qualisys system. We then extracted the peak points and intervals of the human stride from pressure signals fused over three regions, and analyzed the mechanics of their regional fusion by using the regional amplitude-pressure ratio to obtain the distribution of the plantar pressure at an asynchronous walking speed. Furthermore, we introduced multi-scale entropy to quantify the complexity of the gait and evaluate its stability at different walking speeds. Results: The results of experiments showed that increasing the speed from 2 to 6 km/h decreased the stability of the gait, with a 26.7% increase in the amplitude of pressure in the region of the forefoot. The hindfoot and forefoot regions were subjected to the minimal pressure at a speed of 2 km/h, while the most consistent stress was observed in regions of the forefoot, midfoot, and hindfoot. Moreover, the curve of entropy at a speed of 2 km/h exhibited a slow decline at a small scale and high stability at a large scale. Discussion: The multi-scale entropy increased the variation in the stability of the synchronous velocity of walking compared with the sample entropy and the analysis of regional fusion mechanics. Multi-scale entropy can thus be used to qualitatively assess the relationship between the speed and stability of the gait, and to identify the most stable gait speed that can ensure gait stability and posture control.

Analysis of cardiovascular function in diabetic patients using EEMD-ICA fusion multi-scale percussion entropy.

BACKGROUND: Diabetes is a chronic disease that can lead to a variety of complications and even cause death. The signal characteristics of the photoplethysmography signals (PPG) and electrocardiogram signals (ECG) can reflect the autonomic and vascular aspects of the effects of diabetes on the body. OBJECTIVE: Based on the complex mechanism of interaction between PPG and ECG, a set of ensemble empirical mode decomposition-independent component analysis (EEMD-ICA) fusion multi-scale percussion entropy index (MSPEI) method was proposed to analyze cardiovascular function in diabetic patients. METHODS: Firstly, the original signal was decomposed into multiple Intrinsic Mode Function (IMFs) by ensemble empirical mode decomposition EEMD, principal components of IMF were extracted by independent component analysis (ICA), then the extracted principal components were reconstructed to eliminate the complex high and low frequency noise of physiological signals. In addition, the MSPEI was calculated for the ECG R-R interval and PPG amplitude sequence.(RRI and Amp) The results showed that, compared with EEMD method, the SNR of EEMD-ICA method increases from 2.1551 to 11.3642, and the root mean square error (RMSE) decreases from 0.0556 to 0.0067. This algorithm can improve the performance of denoising and retain more feature information. The large and small scale entropy of MSPEI (RRI,Amp) was significantly different between healthy and diabetic patients (p< 0.01). RESULTS: Compared with arteriosclerosis index (AI) and multi-scale cross-approximate entropy (MCAE): MSPEISS (RRI,Amp) indicated that diabetes can affect the activity of human autonomic nervous system, while MSPEILS (RRI,Amp) indicated that diabetes can cause or worsen arteriosclerosis. CONCLUSION: Multi-scale Percussion Entropy algorithm has more advantages in analyzing the influence of diabetes on human cardiovascular and autonomic nervous function.

Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm.

PURPOSE: Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD. AIM: To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD. METHODS: Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme. RESULTS: ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8. CONCLUSIONS: Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram.

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S I , THI, and SHI, where S I is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S I ,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.

Vegetation History and Survival Patterns of the Earliest Village on the Qinghai-Tibetan Plateau.

The upper Yellow River valley in the northeastern Qinghai-Tibetan Plateau (QTP) is an important corridor for prehistoric migration to the hinterland plateau. However, most studies have focused on the Neolithic Age, with limited evidence for earlier periods. The Shalongka (SLK) site on the northeastern QTP spans the Epipaleolithic to Bronze Age and contains cultural deposits, so provides a good basis for unraveling the evolutionary history of the human-land relationship. In this study, we sampled the 420-cm-thick section T1406E at the SLK site and undertook lithologic stratigraphic description and analysis of grain size, redness, magnetic susceptibility, geochemical elements, pollen and charcoal. Dating control was provided by accelerated mass spectrometry 14C and optically stimulated luminescence methods. Results show that SLK site was affected by the local fluvial sedimentary environment. The absolute dating results of the SLK site have revealed that humans occupied the site during the Epipaleolithic (8.5-7.3 cal ka BP), Yangshao culture (5.9-5.1 ka) and Qijia Culture (4.1-3.9 cal ka BP). Pollen analysis showed that the humans lived in a landscape that was predominated by forest-steppe. Consolidating with multidisciplinary evidence, we learned that Epipaleolithic sites were occupied by microlithic hunter-gatherers and comprised by relatively fixed seasonal central campsites, and their mobility was significantly decreased from the early to late period. Subsequently, farmers of the Yangshao culture migrated from the low elevation (Chinese Loess Plateau) to the upper Yellow River valleys on the QTP and founded the earliest settlement villages (~5.9 ka) on the QTP. People of the Qijia culture adopted diversified survival strategies under the settled lifestyle. In all, we infered that SLK site may play an important role in the communication and integration between different people and cultures.

Assessment of arteriosclerosis based on multiscale cross approximate entropy of human finger pulse wave.

BACKGROUND: Arteriosclerosis is one of the diseases that endanger human health. There is a large amount of information in pulse wave signals to reflect the degree of arteriosclerosis. OBJECTIVE: The degree of arteriosclerosis is assessed by analyzing pulse wave signal and calculating multi-scale entropy values. METHODS: A method based on the multiscale cross-approximate entropy of the pulse wave of the human finger is proposed to assess the degree of arteriosclerosis. A total of 86 subjects were divided into three groups. The data of 1000 pulse cycles were selected in the experiment, and the multiscale cross-approximate entropy was calculated for the climb time and pulse wave peak interval. Independent sample t-test analysis gives the small-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval as p< 0.001 in Groups 1 and 2. The large-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval is p< 0.017 in Groups 2 and 3. RESULTS: Using the proposed algorithm, the results showed that the small-scale cross-approximate entropy of climb time and pulse wave peak interval could reflect the degree of arteriosclerosis in the human body from the perspective of autonomic nerve function. The large-scale cross-approximate entropy of climb time and pulse wave peak interval confirmed the effect of diabetes on the degree of arteriosclerosis. CONCLUSIONS: The results demonstrate the multiscale cross-approximate entropy is a comprehensive index to evaluate the degree of human arteriosclerosis.

Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation.

A gait feature analysis method based on AlphaPose human pose estimation fused with sample entropy is proposed to address complicated, high-cost, and time-consuming postoperative rehabilitation of patients with joint diseases. First, TensorRT was used to optimize the inference of AlphaPose, which consists of the target detection algorithm YOLOv3 and the pose estimation algorithm. It can speed up latency and throughput by about 2.5 times while maintaining the algorithm's accuracy. Second, the optimized human posture estimation algorithm AlphaPose_trt was used to process gait videos of healthy people and patients with knee arthritis. The joint point motion trajectories of the two groups were extracted, and the sample entropy algorithm quantified the joint trajectory signals for feature analysis. The experimental results showed significant differences in the entropy of the heel and ankle joint motion signals between healthy people and arthritic patients (p < 0.01), which can be used to identify patients with knee arthritis. This technique can assist doctors in determining needed postoperative joint surgery rehabilitation.

Geochemical and Statistical Analyses of Trace Elements in Lake Sediments from Qaidam Basin, Qinghai-Tibet Plateau: Distribution Characteristics and Source Apportionment.

The safety of lake ecosystems on the Qinghai-Tibet Plateau (QTP) has attracted increasing attention, owing to its unique location and ecological vulnerability. Previous studies have shown that the aquatic systems on the QTP have been polluted to varying degrees by trace elements. However, little is known of the distribution and sources of trace elements in lakes in the northeast QTP. Here, 57 sediment samples were collected from six lakes (Dasugan Lake, Xiaoqaidam Lake, Kreuk Lake, Toson Lake, Gahai Lake and Xiligou Lake) in the Qaidam Basin, northeast QTP, and the trace elements (V, Cr, Ni, Cu, Zn, As, Ba, Tl, Cd, Pb, and U) were analyzed. The results indicated that Ba, Zn, V, and Cr had a higher content and a wider distribution relative to the other tested elements. Correlation coefficient matrix results showed that the trace elements in the study area were strongly correlated, revealing their source of similarity. Self-organizing maps (SOM, an artificial neural network algorithm) results indicated that the degree of pollution in Xiaoqaidam Lake was the highest, and that of Dasugan Lake was the lowest. Furthermore, all sampling points were clustered into four categories through K-means clustering. The positive matrix factorization (PMF) results indicated that atmospheric deposition and anthropogenic inputs were the main trace elements sources in these lakes, followed by traffic emission and geological sources. Collectively, trace elements of six lakes in Qinghai-Tibet Plateau presented high-content and pollution characteristics. This research provides a scientific basis for better water environment management and ecological protection on the QTP.

Hub Genes Associated with the Diagnosis of Diabetic Retinopathy.

PURPOSE: This study aimed to identify genes that may be effective in diagnosing or treating diabetic retinopathy (DR), the most common complication of diabetes mellitus (DM). METHODS: Differentially expressed genes (DEGs) were identified between DR and DM in GSE146615 dataset. DEGs that were consistently up- or down-regulated under both standard glucose and high glucose conditions were identified as common genes and used to generate a protein-protein interaction network and modules. The module genes were assessed for the area under the receiver operating characteristic curve (AUC), leading to the identification of hub genes. Differentially methylated probes in GSE76169 were also compared with common DEGs to identify specific methylation markers of DR. Enrichment analysis was used to explore the biological characteristics. The Short Time-series Expression Miner algorithm was used to identify genes that were progressively dysregulated in the sequence: healthy controls < DM < DR. RESULTS: A total of 1917 common genes were identified for seven modules. The eight genes with AUC > 0.8 under high glucose and standard glucose conditions were considered as hub genes. The module genes were significantly enriched during vascular smooth muscle cell development and regulation of oxygen metabolism, while 92 methylation markers were involved in the similar terms. Among the progressively dysregulated genes, three intersection genes under both standard glucose and high glucose conditions were found to be module genes and were considered as key genes. CONCLUSION: We identified eight potential DR-specific diagnostic and therapeutic genes, whose abnormal expression can cause oxidative stress, thus favoring the course of the disease.

Prognosis of Diabetic Peripheral Neuropathy via Decomposed Digital Volume Pulse from the Fingertip.

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 (PEIDVP) 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 PEIDVP values than those with DPN, as determined by logistic regression model (PEIDVP: odds ratio 0.913, 95% CI 0.850 to 0.980). This study shows that PEIDVP 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).

Characterization of the retinal vasculature in fundus photos using the PanOptic iExaminer system.

BACKGROUND: The goal was to characterize retinal vasculature by quantitative analysis of arteriole-to-venule (A/V) ratio and vessel density in fundus photos taken with the PanOptic iExaminer System. METHODS: The PanOptic ophthalmoscope equipped with a smartphone was used to acquire fundus photos centered on the optic nerve head. Two fundus photos of a total of 19 eyes from 10 subjects were imaged. Retinal vessels were analyzed to obtain the A/V ratio. In addition, the vessel tree was extracted using deep learning U-NET, and vessel density was processed by the percentage of pixels within vessels over the entire image. RESULTS: All images were successfully processed for the A/V ratio and vessel density. There was no significant difference of averaged A/V ratio between the first (0.77 ± 0.09) and second (0.77 ± 0.10) measurements (P = 0.53). There was no significant difference of averaged vessel density (%) between the first (6.11 ± 1.39) and second (6.12 ± 1.40) measurements (P = 0.85). CONCLUSIONS: Quantitative analysis of the retinal vasculature was feasible in fundus photos taken using the PanOptic ophthalmoscope. The device appears to provide sufficient image quality for analyzing A/V ratio and vessel density with the benefit of portability, easy data transferring, and low cost of the device, which could be used for pre-clinical screening of systemic, cerebral and ocular diseases.

Evolution of vegetation and climate variability on the Tibetan Plateau over the past 1.74 million years.

The Tibetan Plateau exerts a major influence on Asian climate, but its long-term environmental history remains largely unknown. We present a detailed record of vegetation and climate changes over the past 1.74 million years in a lake sediment core from the Zoige Basin, eastern Tibetan Plateau. Results show three intervals with different orbital- and millennial-scale features superimposed on a stepwise long-term cooling trend. The interval of 1.74-1.54 million years ago is characterized by an insolation-dominated mode with strong ~20,000-year cyclicity and quasi-absent millennial-scale signal. The interval of 1.54-0.62 million years ago represents a transitional insolation-ice mode marked by ~20,000- and ~40,000-year cycles, with superimposed millennial-scale oscillations. The past 620,000 years are characterized by an ice-driven mode with 100,000-year cyclicity and less frequent millennial-scale variability. A pronounced transition occurred 620,000 years ago, as glacial cycles intensified. These new findings reveal how the interaction of low-latitude insolation and high-latitude ice-volume forcing shaped the evolution of the Tibetan Plateau climate.

Percussion Entropy Analysis of Synchronized ECG and PPG Signals as a Prognostic Indicator for Future Peripheral Neuropathy in Type 2 Diabetic Subjects.

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 (MEISS), large-scale multiscale entropy index (MEILS), electrocardiogram-based pulse wave velocity (PWVmean), 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.

Major ion and dissolved heavy metal geochemistry, distribution, and relationship in the overlying water of Dongting Lake, China.

Deteriorating lake water quality has become a serious environmental issue around the globe. Heavy metals dissolved in the overlying water of lakes are notably more toxic than those found in lake sediment. Given this, we sought to better understand the characteristics of particular major ion and dissolved heavy metal in Dongting Lake-the second largest freshwater lake in China. Overlying water samples were collected from Dongting Lake to investigate the major ion geochemistry and to examine the relationship between the major ions and dissolved heavy metals. Chemical analysis of the overlying water showed that the average cation concentration was the highest for Ca2+, followed by Mg2+, Na+, and K+. Similarly, the highest anion concentration was SO42-, followed by Cl-, NO3-, and HCO3-. Total dissolved solids in the overlying water of Dongting Lake ranged from 66.19 to 159.20 mg/L, with an average value of 93.13 mg/L. The predominant hydrochemical type was Ca-SO4. The mean concentrations of dissolved heavy metal in both surface and deep waters decreased in the following order: Zn > Cr > Ni > Cu > Pb > Cd. Importantly, all of the selected heavy metals tested in the overlying water were lower than the corresponding toxicity reference values. Co-occurrence network analyses were performed and compared the correlations between all measured major ions and heavy metals. Results of the subsequent principal component analyses revealed that heavy metal levels in the aquatic environment primarily originated from natural processes and were enhanced by anthropogenic activities.

Application of multiscale Poincaré short-time computation versus multiscale entropy in analyzing fingertip photoplethysmogram amplitudes to differentiate diabetic from non-diabetic subjects.

BACKGROUND AND OBJECTIVES: Multiscale Poincaré (MSP) plots have recently been introduced to facilitate the visualization of time series of physiological signals. This study aimed at investigating the feasibility of MSP application in distinguishing subjects with and without diabetes. METHODS: Using photoplethysmogram (PPG) waveform amplitudes acquired from unilateral fingertip of non-diabetic (n = 34) and diabetic (n = 30) subjects, MSP indices (MSPI) of the two groups were compared using 1000, 500, 250, 100 data points. Data from Poincaré index (short-term variability/long-term variability [i.e. SD1/SD2] ratio, SSR) and multiscale entropy (MSE) were also obtained with the four corresponding data points for comparison. RESULTS: SSR and MSPI were both negatively related to glycated hemoglobin (HbA1c) and fasting blood sugar levels. Significant negative correlation was also noted between MSPI and pulse pressure. When only 500 and 250 data points were included, significant elevations in the non-diabetic group were only noted in MSPI (both p < 0.01). Furthermore, MSPI was significantly higher in non-diabetic than that in diabetic subjects on all scales (i.e., 1-10) but not using MSE when utilizing 1000 data points. CONCLUSIONS: The results demonstrated enhanced sensitivity of MSP in differentiating between non-diabetic and diabetic subjects compared to SSR and MSE, highlighting the feasibility of MSP application in biomedical data analysis to reduce computational time and enhance sensitivity.

Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects.

To investigate the value of decomposed short-time digital volume pulse (DVP) signals in discerning systemic vascular anomaly in diabetic patients, demographic and anthropometric parameters, serum lipid profile, fasting blood glucose and glycated hemoglobin (HbA1c) levels were obtained from 29 healthy adults (Group 1) and 29 age-matched type 2 diabetes mellitus patients (Group 2). Six-second DVP signals from right index finger acquired through photoplethysmography were decomposed using ensemble empirical mode decomposition. Using one intrinsic mode function (IMF5), stiffness index (SI) and instantaneous energy of maximal energy (fEmax) were obtained. Other indicators of arterial stiffness, including electrocardiogram-pulse wave velocity of foot (ECG-PWVfoot), crest time (CT) and crest time ratio (CTR), were obtained from the testing subjects for comparison. The mean body weight, body mass index, waist circumference, HbA1c and fasting blood sugar levels were higher in Group 2 than those in Group 1, whereas values of systolic and diastolic blood pressure were lower in Group 2 than those in Group 1. SI and fEmax were significantly higher in Group 2 than those in Group 1. Moreover, fEmax was positively associated with HbA1c concentration, CT and SI in Group 2 (p < 0.05) but not in Group 1. When all subjects were considered, fEmax was highly significantly associated with HbA1c and fasting blood sugar levels, and SI (all p < 0.001). After Hilbert-Huang transformation, short-time DVP signals could give significant information on arterial stiffness and vascular anomaly in diabetic patients.

Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects.

The present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and crest time (CT, the time interval from foot to peakof a pulse wave)] could yield information on complexity related to diabetes-associated vascular changes. Signals of a single waveform parameter (i.e., CT) from photoplethysmography and RRI from electrocardiogram were simultaneously acquired within a period of one thousand cardiac cycles for the computation of different multiscale entropy indices from healthy young adults (n = 22) (Group 1), upper-middle aged non-diabetic subjects (n = 34) (Group 2) and diabetic patients (n = 34) (Group 3). The demographic (i.e., age), anthropometric (i.e., body height, body weight, waist circumference, body-mass index), hemodynamic (i.e., systolic and diastolic blood pressures), and serum biochemical (i.e., high- and low-density lipoprotein cholesterol, total cholesterol, and triglyceride) parameters were compared with different multiscale entropy indices including small- and large-scale multiscale entropy indices for CT and RRI [MEISS(CT), MEILS(CT), MEISS(RRI), MEILS(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEISS, MCEILS, respectively]. The results demonstrated that both MEILS(RRI) and MCEILS significantly differentiated between Group 2 and Group 3 (all p < 0.017). Multivariate linear regression analysis showed significant associations of MEILS(RRI) and MCEILS(RRI,CT) with age and glycated hemoglobin level (all p < 0.017). The findings highlight the successful application of a novel multiscale cross-approximate entropy index in non-invasively identifying diabetes-associated subtle changes in vascular functional integrity, which is of clinical importance in preventive medicine.