37 research outputs found
Non-invasive discrimination between diabetic states (HBA1C<8% and HBA1C>10%) using photoplethysmography
Get PDFDiabetes mellitus is a group of metabolic diseases associated with the production and/or reaction of insulin leading to hyperglycemia. Glycated hemoglobin (HbA1c) level is generally measured for hyperglycemia. The risk of developing complications depends on both the duration of diabetes and hyperglycemia. A trend of increasing arterial stiffness has been identified in type 2 diabetes. Photoplethysmographic (PPG) pulse wave provides a ‘window’ into the properties of small arteries whereas stiffening of these arteries will alter the PPG waveform. In this research, the potential of PPG in discriminating between type 2 diabetic patients at risk of having HbA1c level > 10% has been investigated. To this end, PPG signals recorded from diabetic patients with different levels of HbA1c (HbA1c level 10%) were acquired from the index finger of the right arm of 101 subjects (53 subjects with HbA1c level 10%) at a sampling rate of 275 Hz. The area under the curve of PPG (auc-PPG) was proposed in analyzing the PPG pulse contour. Results of t-test analysis show that auc-PPG is significantly larger in diabetic patients with HbA1c level 10% (p-value 10% (total 56 subjects) show that there is no significant difference in the mean value of auc-PPG between the first measurement and repeated measurement for both groups. Finally, a logistic regression model for estimating the risk of having HbA1c level > 10% among diabetic patients was estimated using data from 51 female diabetic patients. The model shows that the auc-PPG is an independent predictor for estimating the risk of having HbA1c level > 10% (p-value = 0.005) among female diabetic patients
Estimation of HbA1c level among diabetic patients using second derivative of photoplethysmography
No full textDiabetes mellitus has become very important issue over the last decade. It affects several complications such as kidney failure, heart failure, stroke and blindness. The risk of developing diabetic complications is related to hyperglycemia and duration of having diabetes. Therefore, early detection can improve the chances of therapeutic interventions that may alleviate its effects. This research proposed second derivative of Photoplethysmography waveform (SDPPG) technique for monitoring the arterial condition in estimated the states of diabetic (HbA1c level greater than 10 percent). The proposed technique is simple and low cost optical techniques for detecting and tracking blood volume change. Results showed that the risk of having HbA1c level greater than 10 percent can be estimated. We conclude that the proposed technique on extracting the information from PPG signal is useful for estimating HbA1c level greater than 10 percent among diabetic patients
<b>Determining the arterial stiffness through contour analysis of a PPG and its association with HbA1c among diabetic patients in Malaysia</b> - doi: 10.4025/actascitechnol.v36i1.17096
No full textDiabetes mellitus accelerates atherosclerosis. Monitoring arterial condition promises to be an advantage in the detection of any abnormalities. In this study, the characteristics of analysis by photoplethysmogram (PPG) were investigated non-invasively among diabetic patients. An area under the curve (auc-PPG) was compared for two levels of HbA1c, namely HbA1c 10% (Group 2). Auc-PPG was found to be significantly higher among diabetic subjects with HbA1c 10%. Further analysis was performed to investigate the effect of age on auc-PPG. The mean values of auc-PPG were still significantly higher among diabetic patients with HbA1c 10%. These results show that the auc-PPG method could be a parameter in determining the arterial stiffness in relation to the level of HbA1c
Second derivatives of photoplethysmography (PPG) for estimating vascular aging of atherosclerotic patients
No full textRisk prediction of having increased arterial stiffness among diabetic patients using logistic regression
No full textInternet of Things for smart solar energy: An IoT farm development
No full textPopulation growth around the world, has resulted in the demand for food continues to increase from time to time. Enhancing farm productivity is critical for maximizing farm profitability and meeting the growing demand for food supply. To overcome this problem, some thoughtful ideas have been produced by farmers, such as vertical farming which uses less soil and water. Therefore, the purpose of this study conducted is to create an IoT farm also known as the Internet of Things farm. The study consisted of solar farms for renewable energy, automatic and manual irrigation systems for optimal plant growth. The study also helped the problems faced by elderly farmers, where they found it difficult to lift heavy items such as watering and doing manual work. Thus, fully automated farms have been created and the result is IoT farms. By applying IoT farm in agriculture, can help improve farm productivity, increase their harvest rate, and meet the high demand for daily food supply
A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis
Get PDFHandgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age
Depression anxiety stress scale and handgrip using machine learning analysis
No full textStress is an emotional or physical state of tension. Stress is the body's natural response to difficulty or a great deal of work. Each of us has a unique reaction to stress. Our capacity for adaptation can be influenced by our genetics, early life events, personality, and socioeconomic situations. This study used handgrip strength (HGS) reading for stress level screening together with Depression Anxiety Stress Scale (DASS) as an early assessment tool. This data of DASS and HGS were analyzed using Random Forest and Support Vector Machine. The dataset is normalized between 0 to 1 due to different units in different measurement tools. The result shows that Random Forest gives an accuracy of 93.75%, a specificity of 94.90%, and a sensitivity of 93.80%. However, SVM gives 87.50% accuracy, 90.30% specificity, and 87.50% sensitivity. This concludes that the Random Forest is better than SVM in terms of stress level classification
