Specific Insomnia Aspect

Research on insomnia is increasingly focusing on objective, data-driven diagnostic methods to improve early detection and treatment. Current efforts utilize machine learning, employing algorithms like 1D convolutional neural networks (CNNs) to analyze electroencephalography (EEG) data and response times from questionnaires, achieving high accuracy in distinguishing individuals with insomnia from healthy controls. These advancements aim to provide more efficient and accurate assessments of insomnia severity, potentially leading to personalized interventions and improved patient outcomes. The development of such objective tools is significant for overcoming limitations of subjective self-reporting in insomnia diagnosis.