ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

A Smart Health (sHealth)-Centric Method toward Estimation of Sleep Deficiency Severity from Wearable Sensor Data Fusion

Sleep deficiency impacts the quality of life and may have serious health consequences in the long run. Questionnaire-based subjective assessment of sleep deficiency has many limitations. On the other hand, objective assessment of sleep deficiency is challenging. In this study, we propose a polysomnography-based mathematical model for computing baseline sleep deficiency severity score and then investigated the estimation of sleep deficiency severity using features available only from wearable sensor data including heart rate variability and single-channel electroencephalography for a dataset of 500 subjects. We used Monte-Carlo feature selection (MCFS) and inter-dependency discovery for selecting the best features and removing multi-collinearity. For developing the Regression model we investigated both the frequentist and the Bayesian approaches. An artificial neural network achieved the best performance of RMSE = 5.47 and an R-squared value of 0.67 for sleep deficiency severity estimation. The developed method is comparable to conventional methods of Functional Outcome of Sleep Questionnaire and Epworth Sleepiness Scale for assessing the impact of sleep apnea on sleep deficiency. Moreover, the results pave the way for reliable and interpretable sleep deficiency severity estimation using single-channel EEG

Inkjet Printed Fully-Passive Body-Worn Wireless Sensors for Smart and Connected Community (SCC)

Future Smart and Connected Communities (SCC) will utilize distributed sensors and embedded computing to seamlessly generate meaningful data that can assist individuals, communities, and society with interlocking physical, social, behavioral, economic, and infrastructural interaction. SCC will require newer technologies for seamless and unobtrusive sensing and computation in natural settings. This work presents a new technology for health monitoring with low-cost body-worn disposable fully passive electronic sensors, along with a scanner, smartphone app, and web-server for a complete smart sensor system framework. The novel wireless resistive analog passive (WRAP) sensors are printed using an inkjet printing (IJP) technique on paper with silver inks (Novacentrix Ag B40, sheet resistance of 21 mΩ/sq) and incorporate a few discrete surface mounted electronic components (overall thickness of <1 mm). These zero-power flexible sensors are powered through a wireless inductive link from a low-power scanner (500 mW during scanning burst of 100 ms) by amplitude modulation at the carrier signal of 13.56 MHz. While development of various WRAP sensors is ongoing, this paper describes development of a WRAP temperature sensor in detail as an illustration. The prototypes were functionally verified at various temperatures with energy consumption of as low as 50 mJ per scan. The data is analyzed with a smartphone app that computes severity (Events-of-Interest, or EoI) using a real-time algorithm. The severity can then be anonymously shared with a custom web-server, and visualized either in temporal or spatial domains. This research aims to reduce ER visits of patients by enabling self-monitoring, thereby improving community health for SSC

Protection of hepatotoxicity using Spondias pinnata by prevention of ethanol-induced oxidative stress, DNA-damage and altered biochemical markers in Wistar rats

Background: Traditional systems of medicine use herbal drugs for hepatoprotection. Thus, the study was designed to evaluate the hepatoprotective and antioxidant effects of Spondias pinnata bark extracts against ethanol-induced liver injury in Wistar rats. Methods: Group I animals were treated with 1 mL/kg 0.3% carboxymethyl cellulose and Group II with 12 mL/kg 50% ethanol for 8 consecutive days. Groups III–VII animals were first treated with 400 mg/kg petroleum ether extract, chloroform extract, acetone extract (AE), ethanol extract (EE), and 100 mg/kg silymarin, and then 12 mL/kg 50% ethanol orally after 2 hours pretreatment each day for 8 consecutive days. Six hours after the last dose, blood was withdrawn. The hepatoprotective activity was assessed by several biochemical and antioxidant parameters. It was accomplished by the histopathology and DNA fragmentation study of liver tissues. Results: Treatment with S. pinnata extracts, mainly AE and EE significantly (p < 0.05–0.01) and dose-dependently prevented the ethanol-induced increase in serum levels of aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, lactate dehydrogenase, cholesterol, bilirubin, and malondialdehyde, and decrease in reduced glutathione, catalase, superoxide dismutase, and albumin. They also attenuated the ethanol-induced DNA damage. Hepatoprotective potential of the extract was less than that of standard drug silymarin. Results of the study were well supported by the histopathological observations. Conclusion: S. pinnata extracts AE and EE possess a potent hepatoprotective effect against ethanol-induced liver injury in Wistar rats, and protect them from hepatotoxicity by prevention of ethanol-induced oxidative stress, DNA-damage and altered biochemical markers