Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

Background: Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) due to the high rate of progression from MCI to AD. Sensitive neural biomarkers may provide a tool for an accurate MCI diagnosis, enabling earlier and perhaps more effective treatment. Despite the availability of numerous neuroscience techniques, electroencephalography (EEG) is the most popular and frequently used tool among researchers due to its low cost and superior temporal resolution. Objective: We conducted a scoping review of EEG and MCI between 2012 and 2022 to track the progression of research in this field. Methods: In contrast to previous scoping reviews, the data charting was aided by co-occurrence analysis using VOSviewer, while data reporting adopted a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework to increase the quality of the results. Results: Event-related potentials (ERPs) and EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the research themes addressed by 2310 peer-reviewed articles on EEG and MCI. Conclusion: Our review identified the main research themes in EEG and MCI with high-accuracy detection of seizure and MCI performed using ERP/EEG, QEEG and EEG-based machine learning frameworks.

Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning

Accurate diagnosis is required before performing proper treatments for coronary heart disease. Machine learning based approaches have been proposed by many researchers to improve the accuracy of coronary heart disease diagnosis. Ensemble learning and cascade generalization are among the methods which can be used to improve the generalization ability of learning algorithm. The objective of this study is to develop heart disease diagnosis method based on ensemble learning and cascade generalization. Cascade generalization method with loose coupling strategy is proposed in this study. C4. 5 and RIPPER algorithm were used as meta-level algorithm and Naive Bayes was used as baselevel algorithm. Bagging and Random Subspace were evaluated for constructing the ensemble. The hybrid cascade ensemble methods are compared with the learning algorithms in non-ensemble mode and non-cascade mode. The methods are also compared with Rotation Forest. Based on the evaluation result, the hybrid cascade ensemble method demonstrated the best result for the given heart disease diagnosis case. Accuracy and diversity evaluation was performed to analyze the impact of the cascade strategy. Based on the result, the accuracy of the classifiers in the ensemble is increased but the diversity is decreased.

Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System

This research is about the development a fuzzy decision support system for the diagnosis of coronary artery disease based on evidence. The coronary artery disease data sets taken from University California Irvine (UCI) are used. The knowledge base of fuzzy decision support system is taken by using rules extraction method based on Rough Set Theory. The rules then are selected and fuzzified based on information from discretization of numerical attributes. Fuzzy rules weight is proposed using the information from support of extracted rules. UCI heart disease data sets collected from U.S., Switzerland and Hungary, data from Ipoh Specialist Hospital Malaysia are used to verify the proposed system. The results show that the system is able to give the percentage of coronary artery blocking better than cardiologists and angiography. The results of the proposed system were verified and validated by three expert cardiologists and are considered to be more efficient and useful.