Heart Rate Variability measured during rest and after orthostatic challenge to detect autonomic dysfunction in Type 2 Diabetes Mellitus using the Classification and Regression Tree model

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.

Accessing the biotechnological potential of a novel isolated microalga from a semi-arid region of Brazil

The use of microalgae as a source of food and pharmaceutical ingredients has garnered growing interest in recent years. Despite the rapid growth of the nutraceutical market, knowledge about the potential of bioactive molecules from microalgae remains insufficient. The present study aimed to investigate the biotechnological potential of the green microalga Desmodesmus armatus isolated from a semi-arid region of Brazil. The algal biomass was characterized in terms of gross biochemical composition, exopolysaccharide content, enzymatic inhibition capacity, and antioxidant, antibacterial, and hemolytic activities from solvents of different polarities (water, ethanol, acetone, and hexane). D armatus biomass had 40% of crude protein content, 25.94% of lipids, and 25.03% of carbohydrates. The prebiotic potential of exopolysaccharides from D armatus was demonstrated, which stimulated the growth of Lacticaseibacillus rhamnosus and Lactiplantibacillus plantarum bacteria strains. Moreover, the enzyme inhibition capacity for the proteases chymotrypsin (34.78%-45.8%) and pepsin (16.64%-27.27%), in addition to α-amylase (24.79%) and lipase (31.05%) was confirmed. The antioxidant potential varied between the different extracts, with 2,2-diphenyl-1-picrylhydrazyl sequestration values varying between 17.51% and 63.12%, and those of the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) method between 6.82% and 22.89%. In the antibacterial activity test, only the ethanolic extract showed inhibition against Listeria sp. (at minimum inhibitory concentration [MIC] = 256 µg mL-1). This fraction also presented the highest significant levels of hemolysis (31.88%-52.45%). In summary, the data presented in the study suggest the presence of biocompounds with biotechnological and nutraceutical potential in the D armatus biomass. Future studies may evaluate the inclusion of this biomass in foods in order to increase their biological value.

Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.