<![CDATA[Distinguishing between epileptic seizures and bipolar disorder in clinical setting presents a significant challenge due to overlapping symptoms and the complex mechanism underlying both conditions. This study offers a novel perspective by integrating the latest research and clinical practices to explore this intricate diagnostic landscape. Unlike previous studies that primarily focused on isolated aspects, this study synthesizes recent advancements in neuroimaging, wearable technology, and machine learning to enhance diagnostic accuracy. Data sources searched were Google Scholar, PubMed, and ScienceDirect using the keywords of ‘epileptic seizures’, ‘bipolar disorder’, ‘diagnosis’, ‘neuroimaging’, ‘wearable technology’, and ‘machine learning’. Following the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) methodology, the findings highlight how the variability of mood episodes and their resemblance to seizure activity often complicate differential diagnosis. Moreover, they underscore the potentials of emerging technologies, such as real-time monitoring via wearable devices and AI-driven diagnostic tools, in refining current clinical approaches. This study emphasizes the necessity of clinic awareness regarding subtle but crucial distinctions between bipolar disorder and epileptic seizures. By leveraging continuous monitoring and data-driven insights, an innovative framework that combines clinical expertise with advanced technology is proposed, paving the way for more precise and effective diagnostic methods.]]>
