1: Multiclass sleep stage classification using artificial intelligence based time-frequency distribution and CNN
Smith K. Kharea; Varun Bajaja; Sachin Taranb; G.R. Sinhac a PDPM-Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Indiab Delhi Technological University (DTU), New Delhi, Indiac Myanmar Institute of Information Technology (MIIT), Mandalay, Myanmar
Abstract
Background: The conventional sleep stage scoring uses interview, questionnaire, and visual inspection by trained neurologists. These methodologies are time taking, inefficient, and error-prone. Thus, need of an automatic classification of sleep stages is felt that can provide accurate and efficient diagnosis of various neuropsychological ...
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