Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. This study used 64 anesthesia and non-anesthesia patient data (32 cases each), extracted from Queensland and MIMIC-II databases, respectively. The key waveform features (total area, rising time, width 75%, 50%, and 25%) were extracted from 16,310 signal recordings (5-s duration). Discriminant analysis, support vector machine (SVM), and K-nearest neighbor (KNN) were evaluated by splitting the dataset into halve training (11 patients, 8570 segments) and halve testing dataset (11 patients, 7740 segments). Significant differences exist between PPG waveform features of anesthesia and non-anesthesia groups (p < 0.05) except total area feature (p > 0.05). The KNN classifier achieved 91.7% (AUC = 0.95) anesthesia detection accuracy with the highest sensitivity (0.88) and specificity (0.90) as compared to other classifiers. Kohen’s kappa also shows almost perfect agreement (0.79) with the KNN classifier. The KNN classifier trained with significant PPG features has the potential to be used as a reliable, non-invasive, and low-cost method for the detection of anesthesia drugs for depth analysis during surgical operations and postoperative monitoring.
Graphical abstract
Polyphenolic compounds were isolated from the aqueous extract of green coconut shell. Benzoyl ester derivatives were prepared with these polyphenols. Monobenzoyl and dibenzoyl derivatives of a polyphenol were separated and characterized.
BackgroundUltraviolet radiations (UV) absorbed by the skin can drive photochemical reactions which range from sunburn to skin cancer. The repeated exposure to Infrared radiations (IR) induces the heat into the skin, which causes dehydration and erythema as an immediate effect. This heat activates the metalloproteinase enzyme that reduces the number of procollagen and collagen fibers in the dermal skin, which results premature skin aging. This work aims to design a protective measure in order to avoid these damages.MethodThe proposed protective measure is a wristwatch with an alert alarm which can sense UV and IR radiations. Whenever UV/IR radiation levels exceed beyond the defined limits, alarm will be activated that warns the user to apply protective measures. These radiations are detected by SI1145 digital UV Index/IR/visible light sensor and assigned, using Arduino, to an appropriate UV index and IR radiation levels.ResultsThe IR and UV readings were recorded several times and at four different hours through the day. The readings showed its highest value at 10 am and 2 pm, which are considered the highest sun intensity. The other readings were at 6 am and 5 pm and considered the least dangerous hours.ConclusionThe data collected from the sensor are used to program the alarm. To combine all components, a PCB and a prototype were designed and printed. The UV/IR wristwatch is applicable to alert the user from the continuous and accumulated harmful effects of the radiations and enable them to seek protective measures.
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