Device for remote and realtime monitoring of neonatal vital signs in neonatal intensive care unit using internet of things: proof-of-concept study.

BACKGROUND: Realtime and remote monitoring of neonatal vital signs is a crucial part of providing appropriate care in neonatal intensive care units (NICU) to reduce mortality and morbidity of newborns. In this study, a new approach, a device for remote and real-time monitoring of neonatal vital signs (DRRMNVS) in the neonatal intensive care unit using the internet of things (IoT), was proposed. The system integrates four vital signs: oxygen saturation, pulse rate, body temperature and respiration rate for continuous monitoring using the Blynk app and ThingSpeak IoT platforms. METHODS: The Wemos D1 mini, a Wi-Fi microcontroller, was used to acquire the four biological biomarkers from sensors, process them and display the result on an OLED display for point of care monitoring and on the Blynk app and ThingSpeak for remote and continuous monitoring of vital signs. The Bland-Altman test was employed to test the agreement of DRRMNVS measurement with reference standards by taking measurements from ten healthy adults. RESULTS: The prototype of the proposed device was successfully developed and tested. Bias [limits of agreement] were: Oxygen saturation (SpO2): -0.1 [- 1.546 to + 1.346] %; pulse rate: -0.3 [- 2.159 to + 1.559] bpm; respiratory rate: -0.7 [- 0.247 to + 1.647] breaths/min; temperature: 0.21 [+ 0.015˚C to + 0.405˚C] ˚C. The proof-of-concept prototype was developed for $33.19. CONCLUSION: The developed DRRMNVS device was cheap and had acceptable measurement accuracy of vital signs in a controlled environment. The system has the potential to advance healthcare service delivery for neonates with further development from this proof-of-concept level.

Early detection and treatment device for diabetic foot neuropathy.

BACKGROUND: Diabetic foot neuropathy is one of the complications of diabetes that affects around 50% of diabetic people. Because peripheral neuropathy involves nerve loss around the foot areas, patients with diabetic neuropathy frequently lose sensation in their feet while walking or standing. Furthermore, since sensory nerves are damaged, the area that holds the majority of the foot pressure and temperature is at high risk of injury. If not diagnosed and treated properly, it can cause foot injury and eventually lead to edema, gangrene, ulcers, amputation, and even death. There are now several techniques of detecting diabetic neuropathy, but they are limited in their availability, cost-effectiveness, and complexity. AIMS: The primary goal of this research was to develop devices for early detection and treatment of diabetic foot neuropathy. METHODS: The proposed device combines a foot pressure monitoring method and a foot temperature measurement method to diagnose diabetic neuropathy early on, with red light therapy added as a treatment method. For 2 weeks, the device measures the patient's foot pressure and temperature, and light therapy is provided if a change in pressure or temperature at a specific area is observed. RESULTS: The device prototype was successfully developed, and numerous tests were carried out in accordance with the design specifications. For pressure measurement and temperature measurement, measurement accuracy of 99.05% and 99.30%, respectively, were attained. CONCLUSION: The early detection and treatment device developed in this study could be used at home by diabetic patients as well as in hospitals to test for and treat diabetic foot neuropathy at an early stage. The device incorporates two different methods of diabetic foot neuropathy detection with high measurement accuracy which makes it suitable for use in resource-limited areas at low cost. The incorporation of red light therapy together with the two methods of diabetic neuropathy detection gives another unique feature for our device.

Evaluation of a New Smartphone Powered Low-cost Pulse Oximeter Device.

Background: Measurement of blood oxygen saturation is a vital part of monitoring coronavirus 2019 (COVID-19) patients. Pulse oximetry is commonly used to measure blood oxygen saturation and pulse rate for appropriate clinical intervention. But the majority of direct-to-consumer grade pulse oximeters did not pass through in-vivo testing, which results in their accuracy being questionable. Besides this, the ongoing COVID-19 pandemic exposed the limitations of the device in resource limited areas since independent monitoring is needed for COVID-19 patients. The purpose of this study was to perform an in-vivo evaluation of a newly developed smartphone powered low-cost pulse oximeter. Methods: The new prototype of a smartphone powered pulse oximeter was evaluated against the standard pulse oximeter by taking measurements from fifteen healthy volunteers. The accuracy of measurement was evaluated by calculating the percentage error and standard deviation. A repeatability and reproducibility test were carried out using the ANOVA method. Results: The average accuracy for measuring spot oxygen saturation (SPO2) and pulse rate (PR) was 99.18% with a standard deviation of 0.57 and 98.78% with a standard deviation of 0.61, respectively, when compared with the standard pulse oximeter device. The repeatability and reproducibility of SPO2 measurements were 0.28 and 0.86, respectively, which is in the acceptable range. Conclusion: The new prototype of smartphone powered pulse oximeter demonstrated better performance compared to the existing low-cost fingertip pulse oximeters. The device could be used for independent monitoring of COVID-19 patients at health institutions and also for home care.

Development of Low-Cost and Portable Pulse Oximeter Device with Improved Accuracy and Accessibility.

Purpose: In a clinical setting, blood oxygen saturation is one of the most important vital sign indicators. A pulse oximeter is a device that measures the blood oxygen saturation and pulse rate of patients with various disorders. However, due to ethical concerns, commercially available pulse oximeters are limited in terms of calibration on critically sick patients, resulting in a significant error rate for measurement in the critical oxygen saturation range. The device's accessibility in developing countries' healthcare settings is also limited due to portability, cost implications, and a lack of recognized need. The purpose of this study was to develop a reliable, low-cost, and portable pulse oximeter device with improved accuracy in the critical oxygen saturation range. Methods: The proposed device measures oxygen saturation and heart rate using the reflectance approach. The rechargeable battery and power supply from the smartphone were taken into account, and the calibration in critical oxygen saturation values was performed using Prosim 8 vital sign simulator, and by comparing with a standard pulse oximeter device over fifteen iterations. Results: The device's prototype was successfully developed and tested. Oxygen saturation and heart rate readings were both accurate to 97.74% and 97.37%, respectively, compared with the simulator, and an accuracy of 98.54% for the measurement of blood oxygen saturation was obtained compared with the standard device. Conclusion: The accuracy of oxygen measurement attained in this study is significant for measuring oxygen saturation for patients in critical care, anesthesia, pre-operative and post-operative surgery, and COVID-19 patients. The advancements made in this research have the potential to increase the accessibility of pulse oximeter in resource limited areas.

Development of a low-cost device for testing glove and condom leakage.

Background: Currently, hospital-acquired infections in healthcare workers and patients are a major concern. On the other hand, sexually transmitted infections and diseases, unwanted pregnancies, and unsafe abortions continue to be a public health concern, particularly in developing countries. Gloves are among the most commonly used personal protective equipment to safeguard healthcare workers' hands from contagious infections, and using a condom is strongly advised for people who have sexual relations with more than one partner. However, the quality of gloves and condoms in developing countries is a subject of concern. The usage of quality test instruments such as glove leakage test apparatus (GLTA), leakage testers by water level, the Ammonia leak testing method (ALTM), conductivity-based leakage testers, and water hang testers in developing countries is limited owing to cost, accessibility, and safety. The main purpose of this study was to develop and test a low-cost integrated device to test glove and condom leakage that is safe and easily accessible in resource poor settings. Method: In this study, an integrated glove and condom leakage testing device for detecting pin holes and leakages is proposed. The device automatically fills a randomly selected condom and glove with a predetermined volume of water based on International Organization for Standardization (ISO) criteria. Results: The prototype of the proposed device was successfully developed and tested. The accuracy of 98.66% for filling condom samples with 300 ml of water and 99.29% for filling glove samples with 1000 ml of water was achieved. Conclusion: The implementation of the developed prototype in resource poor settings to test gloves and condom leakage has the potential to improve the safety of healthcare workers, patients, and the general public.

Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases.

Purpose: Lung diseases are the third leading cause of death worldwide. Stethoscope-based auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic approach for assessing lung conditions. However, the manual auscultation-based diagnosis procedure is prone to error, and its accuracy is dependent on the physician's experience and hearing capacity. Moreover, the stethoscope recording is vulnerable to different noises that can mask the important features of lung sounds which may lead to misdiagnosis. In this paper, a method for the acquisition of lung sound signals and classification of the top 7 lung diseases has been proposed for improving the efficacy of auscultation diagnosis of pulmonary disease. Methods: An electronic stethoscope has been constructed for signal acquisition. Lung sound signals were then collected from people with COPD, upper respiratory tract infections (URTI), lower respiratory tract infections (LRTI), pneumonia, bronchiectasis, bronchiolitis, asthma, and healthy people. Lung sounds were analyzed using a wavelet multiresolution analysis. To choose the most relevant features, feature selection using one-way ANOVA was performed. The classification accuracy of various machine learning classifiers was compared, and the Fine Gaussian SVM was chosen for final classification due to its superior performance. Model optimization was accomplished through the application of Bayesian optimization techniques. Results: A test classification accuracy of 99%, specificity of 99.2%, and sensitivity of 99.04%, have been achieved for the 7 lung diseases using the optimized Fine Gaussian SVM classifier. Conclusion: Our experimental results demonstrate that the proposed method has the potential to be used as a decision support system for the classification of lung diseases, especially in those areas where the expertise and the means are limited.

Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO2 signals.

Sleep apnoea is a potentially serious sleep disorder that is characterised by repetitive episodes of breathing interruptions. Traditionally, sleep apnoea is commonly diagnosed in an attended sleep laboratory setting using polysomnography (PSG). The manual diagnosis of sleep apnoea using PSG is, however complex, and time-consuming, as many physiological variables are usually measured overnight using numerous sensors attached to patients. In PSG sleep laboratories, an expert human observer is required to work overnight, and the diagnosis accuracy is dependent on the physician's experience. A quantitative and objective method is required to improve the diagnosis efficacy, decrease the complexity and diagnosis time and to ensure a more accurate diagnosis. The purpose of this study was then to develop an automatic sleep apnoea and severity classification using a simultaneously recorded electrocardiograph (ECG) and saturation of oxygen (SpO2) signals based on a machine learning algorithm. Different ECG and SpO2 time domain and frequency domain features were extracted for training different machine learning algorithms. For sleep apnoea classification, an accuracy of 99.1%, specificity of 98.1% and sensitivity of 100% were achieved using a support vector machine (SVM) based on combined ECG and SpO2 features. Similarly, for severity classification, an 88.9% accuracy, 90.9% specificity and 85.7% sensitivity have been obtained. For both apnoea and severity classification, using the combined features was found to be more accurate, and this is typically important when either channel is poor quality, the system can make an analysis based on the other channel and achieve good accuracy.