<![CDATA[This study aims to conduct a Systematic Literature Review (SLR) on the utilization of Electroencephalogram (EEG) signals in Internet of Things (IoT)-based systems for the early monitoring and diagnosis of heart disease. Literature was collected from IEEE Xplore, Scopus, Semantic Scholar, MDPI, and ResearchGate, covering publications from 2024 to 2026. Article selection was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, while the quality of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) method. The review findings indicate that the integration of EEG with other biosignals, particularly Electrocardiogram (ECG), in wearable IoT-based systems can improve the accuracy of real-time cardiovascular monitoring. The application of artificial intelligence and machine learning further enhances the capability for early heart disease detection. In addition, multimodal biosignal approaches provide more comprehensive insights into patients’ neurological and cardiovascular conditions. However, challenges remain regarding EEG signal quality, device integration complexity, energy consumption, and data security and privacy. The novelty of this study lies in its comprehensive synthesis of EEG, IoT, and artificial intelligence integration for cardiovascular monitoring, supported by a quality assessment of the literature using the GRADE approach. These findings demonstrate the significant potential of EEG in supporting the development of more accurate and efficient smart healthcare systems.]]>
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