| Summary: | Medical retrieval systems have become significantly important in clinical settings. However, commercial retrieval systems that heavily rely on term-based indexing face challenges when handling continuous medical data, such as electroencephalography data, primarily due to the high cost associated with utilizing neurologist analyses. With the increasing affordability of data recording systems, it becomes increasingly crucial to address these challenges. Traditional procedures for annotating, classifying, and interpreting medical data are costly, time consuming, and demand specialized knowledge. While cross-modal retrieval systems have been proposed to address these challenges, most concentrate on images and text, sidelining time-series medical data like electroencephalography data. As the interpretation of electroencephalography signals, which document brain activity, requires a neurologist’s expertise, this process is often the most expensive component. Therefore, a retrieval system capable of using text to identify relevant signals, eliminating the need for expert analysis, is desirable. Our research proposes a solution to facilitate the creation of indexing systems employing electroencephalography signals for report generation in situations where reports are pending a neurologist review. We introduce a method incorporating a convolutional-neural-network-based encoder from DeepSleepNet, which extracts features from electroencephalography signals, coupled with a transformer which learns the signal’s auto-correlation and the relationship between the signal and the corresponding report. Experimental evaluation using real-world data revealed our approach surpasses baseline methods. These findings suggest potential advancements in medical data retrieval and a decrease in reliance on expert knowledge for electroencephalography signal analysis. As such, our research represents a significant stride towards making electroencephalography data more comprehensible and utilizable in clinical environments.
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