Detection of Oxygen Levels (SpO2) and Heart Rate Using a Pulse Oximeter for Classification of Hypoxemia Based on Fuzzy Logic

This study made the digital system to perform screening (early prediction) of Hypoxemia using MAX30102 sensor with the fuzzy value from SpO2 level and heart rate. This research also uses the Internet of Things (IoT) system to gather data from devices to the cloud. Hypoxemia is a lack of oxygen in the blood flowing in the body. Hypoxemia conditions in the body due to lack of oxygen levels in the blood will cause an increased heart rate. Hypoxemia conditions that are not immediately recognized cause damage to cells, tissues, and organs. Hypoxemia is an essential condition because information about oxygen levels in the blood is closely related to health conditions. In this project, researchers built a Hypoxemia early detection system. From the research results, it is found that the accuracy rate of the system to detect hypoxemia is 80%, with 60% sensitivity and 100% specificity. Based on the experiment, this research is able to help screening detection (early prediction) of Hypoxemia

A multiplexed electronic architecture for opto-electronic patch sensor to effectively monitor heart rate and oxygen saturation

To effectively capture human vital signs, a multi-wavelength optoelectronic patch sensor (MOEPS), together with a schematic architecture of electronics, was developed to overcome the drawbacks of present photoplethysmographic (PPG) sensors. To obtain a better performance of in vivo physiological measurement, the optimal illuminations, i.e., light emitting diodes (LEDs) in the MOEPS, whose wavelength is automatically adjusted to each specific subject, were selected to capture better PPG signals. A multiplexed electronic architecture has been well established to properly drive the MOEPS and effectively capture pulsatile waveforms at rest. The protocol was designed to investigate its performance with the participation of 11 healthy subjects aged between 18 and 30. The signals obtained from green (525nm) and orange (595nm) illuminations were used to extract heart rate (HR) and oxygen saturation (SpO2%). These results were compared with data, simultaneously acquired, from a commercial ECG and a pulse oximeter. Considering the difficulty for current devices to attain the SpO2%, a new computing method, to obtain the value of SpO2%, is proposed depended on the green and orange wavelength illuminations. The values of SpO2% between the MOEPS and the commercial Pulse Oximeter devics showed that the results were in good agreement. The values of HR showed close correlation between commercial devices and the MOEPS (HR: r1=0.994(Green); r2=0.992(Orange); r3=0.975(Red); r4=0.990(IR))

Implementation of IoT of an Electric Infant Warmer to Prevent Hypothermia in Newborns

Hypothermia is a drop in body temperature below 36.5°C in newborns. It results in an internal distribution of body heat from the nucleus to the periphery, followed by heat loss greater than metabolic production. Hypothermia is one of the factors predisposing to metabolic disorders, intracranial hemorrhage, respiratory distress, and Necrotizing enterocolitis. Hypothermia problems can be treated with infant warmers. Thus, the need for a infant warmer is considered to improve survival in newborns. This study aims to improve the accuracy of temperature monitoring, increase security, and enable remote monitoring. The temperature sensor of the device is calibrated with comparable devices such as Incubator Analyzer and Thermo hygrometer while the SpO2 sensor is calibrated with Spotlight SpO2 Functional Tester and Thermo hygrometer. Achievement and validation of temperature and oxygen saturation use a calibration comparison tool. The results of the temperature sensor measurements, including air temperature and skin sensor temperature, namely: air temperature error tolerance ≤2°C and skin sensor temperature error tolerance ± 0.5 ° C. All two indicators have the same standard deviation value of ±0.49. The SpO2 indicator reached an error tolerance value of ± 1% O2 with a standard deviation value of ± 0.6-0.9 from six trials. Then the pulse rate indicator obtained an error tolerance of ±5% with a standard deviation value of ±0.6. The smart infant warmer tool provides benefits to avoid excessive heat from the heater and minimize low temperatures that cause hypothermia through the Internet of Things technology. Furthermore, this research can be improved with machine learning technology to increase efficiency and effectiveness in patient treatment

Investigation of the performance of an automatic arterial oxygen controller

Premature infants often require respiratory support with a varying concentration of the fraction of inspired oxygen (FiO2) to keep the arterial oxygen saturation (SpO2) within the desired range to avoid both hypoxemia and hyperoxemia. Currently, manual adjustment of FiO2 is the common practice in neonatal intensive care units (NICUs). The automation of this adjustment is a topic of interest. The research team, at University of Missouri-Columbia (UMC), has developed a novel automatic arterial oxygen saturation controller. In this study, a systematic approach has been developed to investigate both non-clinical and clinical performance of this device. The non-clinical investigation of the performance was performed using a neonatal respiratory model (hardware-in-the-loop test). A factorial experimental design was utilized to generate challenging model responses of SpO2, which were addressed by the controllers. With this study, we demonstrate the stability and ability of the adaptive PI-controller to improve oxygen saturation control over manual control by increasing the proportion of time where SpO2 of the neonatal respiratory model was within the desired range and by minimizing the variability of the SpO2. In addition, the controller ability to significantly reduce the number of hypoxemic events of the neonatal respiratory model was reported. Results of this investigation show the competence of the controller estimation system for estimating neonatal respiratory model parameters while the adaptive PI-controller was in use. Also, the functionality of the controller with no mechanical or communication failure was validated non-clinically before heading forward to the clinical trial. The clinical investigation of the performance was performed by conducting a clinical trial at the NICU of the MU Women's and Children's Hospital. The crossover design was used for the clinical trial to allow within-subject comparison and to eliminate interpatient variability. Two human subjects, with two different target ranges of SpO2, were enrolled in the study. The adaptive automatic PI-controller shows clinical feasibility to improve the maintenance of SpO2 within the intended range. With this study, we demonstrate the potential of the automatic controller to minimize the variability of SpO2. In addition, the controller shows the ability to reduce the bradycardia and the hypoxemia. Moreover, the hardware and software of the controller show an ability to transition from manual to automatic mode, and vice versa with no pronounced “bump” or step variation in the control signal, and stability and performance were not adversely affected during the transitions.Includes bibliographical reference

ZigBee Pulse Oximeter

This work presents a prototype to adapt a standard pulse oximeter by turning it into a wireless device using ZigBee. Patient’s data are extracted and transmitted to the server in real time through a Wireless Sensor Network. This Wireless Sensor Network is deployed using the mesh topology in order to reach the maximum reliability in the communications. The pulse oximeter is based on a Nellcor DS-100a probe and is controlled by an Arduino FIO with a XBee wireless modem. The amplifier circuit which is designed to extract the information of the pulse oximeter probe is included in this work

Bedside Monitor Based on Personal Computer Using STM32F7 Microcontroller

A vital sign examination is one of the important indications used to establish the diagnosis of a disease and is useful for determining the medical treatment plan needed for the patient. An electrocardiograph (ECG) is a parameter in medical equipment used in the process of measuring the electrical activity of the heart muscle by measuring biopotential differences from the body surface. In 2016, cardiovascular disease was the number-one cause of death in the world. This happens because the detection of cardiovascular disease is often late, so a monitoring tool is needed that can monitor the patient's condition quickly and efficiently. The purpose of this research is to create a tool that is used to facilitate the monitoring of patient conditions. The implication of this research is that in the many cases where the signal produced is not perfect and there is still a lot of noise, this can be overcome by using the STM32F7 microcontroller, which has a 16-bit resolution, so that the resulting signal will be better, smoother, and have less noise. produced is very small. The method used in this study was to use a phantom ECG as a comparison and to use five respondents whose BPM values were to be compared with another comparison using a pulse oximeter. The design of this tool uses an ECG analog circuit that is placed on the patient's lead II leads to detect the patient's electrocardiograph signal. Data processing will be done using the STM32F7 microcontroller, and the results of the data processing will be sent to the PC using Visual Basic. The results showed that the BPM error value using a phantom ECG was 2.5%. While the smallest error value is 0,83%. In BPM measurements using 5 respondents, the largest error value was 0.9% and the smallest error value was 0%. The results of these tests indicate that this module can be used to monitor the value of each parameter in accordance with the plan