Current Issue : October - December Volume : 2017 Issue Number : 4 Articles : 6 Articles
Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time\nconsumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV)\nsignal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining\nBSEA and sliding window iterative theory. The blood pressure signals of healthy young and old subjects are chosen from the\nauthoritative international database MIT/PhysioNet/Fantasia to generate PRV signals as the experimental data. Then, the BSEA\nand the SWIBSEA are used to analyze the experimental data; the results show that the SWIBSEA reduces the time consumption\nand the buffer cache space while it gets the same entropy as BSEA. Meanwhile, the changes of base scale entropy (BSE) for\nhealthy young and old subjects are the same as that of HRV signal. Therefore, the SWIBSEA can be used for deriving some\ninformation from long-term and short-term PRV signals in real time, which has the potential for dynamic PRV signal analysis\nin some portable and wearable medical devices....
Sleep spindle is the characteristic waveform of electroencephalogram (EEG)\nwhich is important for clinical diagnosis. In this study, an automatic sleep\nspindle detection method was developed. The EEG signals were recorded\nbased on the standard polysomnogram (PSG) measurement. A preprocessing\nprocedure is introduced to exclude the unnecessary data segments and normalized\nthe necessary data segments. Complex demodulation method is\nadopted to detect the candidate sleep spindle waveforms and calculate the\nfeatures. The sleep spindles are recognized based on a decision tree model.\nFinally, the detected sleep spindles were utilized to amend the sleep stage recognition\nresults. The sleep EEG data from 3 patients with sleep disorders were\nanalyzed. The obtained results showed that the detected sleep spindles in EEG\nsignal improved the accuracy of sleep stage recognition....
This design focuses on developing ANN based diet prescription system which measures the Quetelet Index (Body Mass Index, BMI) automatically by using height and weight, categories it and prescribes diet plan for particular person without help of nutritionist. Calculating BMI is one of the best methods for population assessment of overweight and obesity. Because the calculation requires only height and weight, it is cheap and it’s simple to use for clinicians and for the general public. The use of BMI allows individuals to compare their own weight status to that of the general population. BMI is based on two main factors - height and weight. Height is measured by using ultrasonic sensor and weight is measured by using load sensor. Based on this calculated BMI, the diet plan is displayed on LCD with the help of artificial neural network (ANN)....
This research aims to simulate a gravity flow fractionationââ?¬â?the process to\nfractionate erythrocytes through gravitational field using ANSYS simulation\nsoftware. A particular microfluidic channel was designed as a separation device.\nThe gravitational equilibrium conditions of the erythrocytes and gravitational\nfield as the parameters were chosen, then deriving the erythrocytesââ?¬â?¢\npath through numerical simulations. After the actual analog measurements,\nthere is no big difference between the flow velocity and the pressure under\n+/âË?â??10% atmosphere condition. According to the simulation results, the particle\nwith the size 8 Ã?¼m (similar to the erythrocyte size) can be separated to the\noutside channel and discharged from the collecting area, other particles with\nthe size 9 Ã?¼m will stay in the fluid motion and can be collected in the final\ncollection area for preservation. Through the analog analysis by using the\nsoftware-ANSYS-Fluent, the complete flowing path of the particles and the\nfeasibility of the Gravity-Flow Fractionation can be directly proven....
We present an image-guided laparoscopic surgical tool (IGLaST) to prevent bleeding.\nBy applying optical frequency domain imaging (OFDI) to a specially designed laparoscopic surgical\ntool, the inside of fatty tissue can be observed before a resection, and the presence and size of blood\nvessels can be recognized. The optical sensing module on the IGLaST head has a diameter of less\nthan 390 �¼m and is moved back and forth by a linear servo actuator in the IGLaST body. We proved\nthe feasibility of IGLaST by in vivo imaging inside the fatty tissue of a porcine model. A blood vessel\nwith a diameter of about 2.2 mm was clearly observed. Our proposed scheme can contribute to safe\nsurgery without bleeding by monitoring vessels inside the tissue and can be further expanded to\ndetect invisible nerves of the laparoscopic thyroid during prostate gland surgery....
Background: Pulse oximeters continuously monitor arterial oxygen saturation. Continuous\nmonitoring of venous oxygen saturation (SvO2) would enable real-time assessment\nof tissue oxygen extraction (O2E) and perfusion changes leading to improved\ndiagnosis of clinical conditions, such as sepsis.\nMethods: This study presents the proof of concept of a novel pulse oximeter method\nthat utilises the compliance difference between arteries and veins to induce artificial\nrespiration-like modulations to the peripheral vasculature. These modulations make the\nvenous blood pulsatile, which are then detected by a pulse oximeter sensor. The resulting\nphotoplethysmograph (PPG) signals from the pulse oximeter are processed and\nanalysed to develop a calibration model to estimate regional venous oxygen saturation\n(SpvO2), in parallel to arterial oxygen saturation estimation (SpaO2). A clinical study with\nhealthy adult volunteers (n = 8) was conducted to assess peripheral SvO2 using this\npulse oximeter method. A range of physiologically realistic SvO2 values were induced\nusing arm lift and vascular occlusion tests. Gold standard, arterial and venous blood\ngas measurements were used as reference measurements. Modulation ratios related to\narterial and venous systems were determined using a frequency domain analysis of the\nPPG signals.\nResults: A strong, linear correlation (r2 = 0.95) was found between estimated venous\nmodulation ratio (RVen) and measured SvO2, providing a calibration curve relating\nmeasured RVen to venous oxygen saturation. There is a significant difference in gradient\nbetween the SpvO2 estimation model (SpvO2 = 111 âË?â?? 40.6*R) and the empirical SpaO2\nestimation model (SpaO2 = 110 âË?â?? 25*R), which yields the expected arterial-venous differences.\nMedian venous and arterial oxygen saturation accuracies of paired measurements\nbetween pulse oximeter estimated and gold standard measurements were 0.29\nand 0.65%, respectively, showing good accuracy of the pulse oximeter system.\nConclusions: The main outcome of this study is the proof of concept validation of\na novel pulse oximeter sensor and calibration model to assess peripheral SvO2, and\nthus O2E, using the method used in this study. Further validation, improvement, and\napplication of this model can aid in clinical diagnosis of microcirculation failures due to\nalterations in oxygen extraction....
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