On designing a biosignal-based fetal state assessment system: A systematic mapping study

Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network

Myocarditis is cardiac damage caused by a viral infection. Its result often leads to a variety of arrhythmias. However, rapid and reliable identification of myocarditis has a great impact on early diagnosis, expedited treatment, and improved patient survival rates. Therefore, a novel strategy for the autonomous detection of myocarditis is suggested in this work. First, the improved quantum genetic algorithm (IQGA) is proposed to extract the optimal features of ECG beat and heart rate variability (HRV) from raw ECG signals. Second, the backpropagation neural network (BPNN) is optimized using the adaptive differential evolution (ADE) algorithm to classify various ECG signal types with high accuracy. This study examines analogies among five different ECG signal types: normal, abnormal, myocarditis, myocardial infarction (MI), and prior myocardial infarction (PMI). Additionally, the study uses binary and multiclass classification to group myocarditis with other cardiovascular disorders in order to assess how well the algorithm performs in categorization. The experimental results demonstrate that the combination of IQGA and ADE-BPNN can effectively increase the precision and accuracy of myocarditis autonomous diagnosis. In addition, HRV assesses the method's robustness, and the classification tool can detect viruses in myocarditis patients one week before symptoms worsen. The model can be utilized in intensive care units or wearable monitoring devices and has strong performance in the detection of myocarditis.

Current Innovative Methods of Fetal pH Monitoring—A Brief Review

In this study, we explore the “why?”, and “how?”, monitoring the pH of the fetal scalp is used, and show its limitations. In addition, we review the development of new devices based on the modern physics and nanomaterials serving this topic. Most of the works we found in our search have focused on improving the prognostic of fetal heart rate monitoring, because it is the “golden standard” in determining fetal distress. Although the best-known screening method, it can only provide limited information about the actual status of the fetus. The best predictive assessment, with the highest reproducibility, states that a normal fetal heart rate is indicative of a healthy baby. However, its excellent sensitivity is much reduced when identifying the actual “distress”. This is when second-line monitoring methods come into play to guide the diagnostics and direct the obstetrician towards an action plan. Although a historic method, fetal scalp pH sampling is still under review as to its efficiency and place in the current obstetrics. Continuous surveillance of the fetal parameters is important, especially for the fetuses undergoing intrauterine growth restricted (IUGR). Since fetal scalp blood sampling is still under research and is a randomized controlled trial, which compares the relevance of pH and lactates to the obstetrical situation, the maternal-fetal medicine could greatly benefit from the introduction of engineered nanomedicines to the field.