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.
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.
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