Global Rate of Willingness to Volunteer Among Medical and Health Students During Pandemic: Systemic Review and Meta-Analysis.

BACKGROUND: During health crises such as the COVID-19 pandemic, shortages of health care workers often occur. Recruiting students as volunteers could be an option, but it is uncertain whether the idea is well-accepted. OBJECTIVE: This study aims to estimate the global rate of willingness to volunteer among medical and health students in response to the COVID-19 pandemic. METHODS: A systematic search was conducted on PubMed, Embase, Scopus, and Google Scholar for studies reporting the number of health students willing to volunteer during COVID-19 from 2019 to November 17, 2023. The meta-analysis was performed using a restricted maximum-likelihood model with logit transformation. RESULTS: A total of 21 studies involving 26,056 health students were included in the meta-analysis. The pooled estimate of the willingness-to-volunteer rate among health students across multiple countries was 66.13%, with an I2 of 98.99% and P value of heterogeneity (P-Het)<.001. Removing a study with the highest influence led to the rate being 64.34%. Our stratified analyses indicated that those with older age, being first-year students, and being female were more willing to volunteer (P<.001). From highest to lowest, the rates were 77.38%, 77.03%, 65.48%, 64.11%, 62.71%, and 55.23% in Africa, Western Europe, East and Southeast Asia, Middle East, and Eastern Europe, respectively. Because of the high heterogeneity, the evidence from this study has moderate strength. CONCLUSIONS: The majority of students are willing to volunteer during COVID-19, suggesting that volunteer recruitment is well-accepted.

Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram.

Background: Hypertension is associated with severe complications, and its detection is important to provide early information about a hypertension event, which is essential to prevent further complications. Objective: This study aimed to investigate a strategy for hypertension detection without a cuff using parameters of bioelectric signals, i.e., Electrocardiogram (ECG), Photoplethysmogram (PPG,) and an algorithm of Swarm-based Support Vector Machine (SSVM). Material and Methods: This experimental study was conducted to develop a hypertension detection system. ECG and PPG bioelectrical records were collected from the Medical Information Mart for Intensive Care (MIMIC) from normal and hypertension participants and processed to find the parameters, used for the inputs of SSVM and comprised Pulse Arrival Time (PAT) and the characteristics of PPG signal derivatives. The SSVM was n Support Vector Machine (SVM) algorithm optimized using particle swarm optimization with Quantum Delta-potential-well (QDPSO). The SSVMs with different inputs were investigated to find the optimal detection performance. Results: The proposed strategy was performed at 96% in terms of F1-score, accuracy, sensitivity, and specificity with better performance than the other methods tested and methods and also could develop a cuff-free hypertension monitoring system. Conclusion: Hypertension using SSVM, ECG, and PPG parameters is acceptably performed. The hypertension detection had lower performance utilizing only PPG than both ECG and PPG.

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms.

Background: Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient's body is fitted with many sensors. Objective: This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms. Material and Methods: In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). Results: The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied. Conclusion: ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.

A New Automatic QT-Interval Measurement Method for Wireless ECG Monitoring System Using Smartphone.

QT-interval prolongation is an important parameter for heart arrhythmia diagnosis. It is the time interval from QRS-onset to the T-end of electrocardiogram (ECG). Manual measurement of QT-interval, especially for 12-leads ECG, is time-consuming. Hence, an automatic QT-interval measurement is necessary. A new method for automatic QT-interval measurement is presented in this paper, which mainly consists of three parts, including QRS-complex detection, determination of QRS-onset, and T-end determination. The QRS-complex detection is based on the modified Pan-Tompkins algorithm. The T-end is defined based on Region of Interest (ROI) maximum limit. We compare and test our proposed QT-interval measurement method with reference measurement in term of correlation coefficient and range of 95% LoA. The correlation coefficient and the range of 95% LoA are 0.575 and 0.290, respectively. The proposed method is successfully implemented in ECG monitoring system using smartphone with high performance. The accuracy, positive predictive, and sensitivity of the QRS-complex detection in the system are 99.70%, 99.78%, and 99.92%, respectively. The range of 95% LoA for the comparison between manual and the system's QT-interval measurement is 0.216. The results show that the proposed method is dependable on the measure of the QT-interval and outperforms the other methods in term of correlation coefficient and range of 95% LoA.

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.