The bilateral photoplethysmography (PPG) analysis for arteriovenous fistula (AVF) dysfunction screening with a fractional-order feature and a cooperative game (CG)-based embedded detector is proposed. The proposed detector uses a feature extraction method and a CG to evaluate the risk level for AVF dysfunction for patients undergoing haemodialysis treatment. A Sprott system is used to design a self-synchronisation error formulation to quantify the differences in the changes of blood volume for the sinister and dexter thumbs' PPG signals. Bilateral PPGs exhibit a significant difference in rise time and amplitude, which is proportional to the degree of stenosis. A less parameterised CG model is then used to evaluate the risk level. The proposed detector is also studied using an embedded system and bilateral optical measurements. The experimental results show that the risk of AVF stenosis during haemodialysis treatment is detected earlier.
Hemodialysis (HD) is a clinical treatment that requires the puncturing of the body surface. However, needle dislodgement can cause a high risk of blood leakage and can be fatal to patients. Previous studies proposed several devices for blood leakage detection using optical or electrical techniques. Nonetheless, these methods used single-point detection and the design was not suitable for multi-bed monitoring. This study proposed a novel wearable device for blood leakage monitoring during HD using an array sensing patch. The array sensing patch combined with a mapping circuit and a wireless module could measure and transmit risk levels. The different risk levels could improve the working process of healthcare workers, and enhance their work efficiency and reduce inconvenience due to false alarms. Experimental results showed that each point of the sensing array could detect up to 0.1 mL of blood leakage and the array sensing patch supports a risk level monitoring system up to 8 h to alert healthcare personnel of pertinent danger to the patients.
Cardiotocograph (CTG) contains uterine contraction (UC) and fetal heart rate (FHR) signals, which is an important information of clinical pregnant woman care. National Institute of Child Health and Human Development (NICHD) is one of the reference guides for clinical care and it classified pregnant woman into three categories including I, II, and III to evaluate the status of fetus. However, when it comes to manual interpretation was time-consuming and not easy to observe the slight differences. In this study, we combined rule-based method and eXtreme Gradient Boosting (XGBoost) analysis for intrapartum cardiotocograph classification. Because the category II of NICHD is defined unknown status, XGBoost analysis was used to classify the category II into IIa and IIb, and analyze their probability of fetal distress (FD). From the clinical trial of 68 pregnant women, the results of three categories (I, II and III) were consistent and no statistical difference with the clinicians’ interpretation and the average Kappa was about 0.72. The results also indicated that the probability of FD was 28.8% and 71.2% in category IIa and IIb, respectively. It shows the proposed method has potential to provide a clinical assistant tool for pregnant women care.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.