Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Madan; Chauhan, Sujata; Rajput, Anil Kumar; Verma, Indu; Tiwari, Alok Kumar Title A name given to the resource EASM: An efficient AttnSleep model for sleep Apnea detection from EEG signals Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimedia Tools and Applications;Volume;84;Issue;4;pp.1985-2003 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11042-024-19118-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11042-024-19118-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85190642064?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85190642064?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh M., Christ (Deemed to be University), Delhi-NCR Campus, India; Chauhan S., Dronacharya Govt. College, Gurugram, India; Rajput A.K., ABV-Indian Institute of Information Technology &amp; Management, Gwalior, India; Verma I., Christ (Deemed to be University), Delhi-NCR Campus, India; Tiwari A.K., ABV-Indian Institute of Information Technology &amp; Management, Gwalior, India Description An account of the resource This paper addresses the crucial task of automatic sleep stage classification to assist sleep experts in diagnosing sleep disorders such as sleep apnea and insomnia. The proposed solution presents a novel attention-based deep learning model called, Efficient Attention-sleep Model (EASM), designed specifically for sleep apnea detection using EEG signals. EASM incorporates a streamlined architecture that includes a modified Muti-Resolution Convolutional Neural Network (MRCNN), Adaptive Feature Recalibration (AFR), and a simplified Temporal Context Encoder (TCE) module to reduce complexity. To mitigate overfitting, ridge regression is utilized, which incorporates a penalty term to enhance model generalization. Furthermore, the proposed EASM utilizes a class-balanced focal loss function to address data imbalance issues. The effectiveness of EASM is evaluated on two publicly available datasets, SLEEP EDF-20 and SLEEP EDF-78. Comparative analysis of EASM against state-of-the-art models demonstrates its superior performance in terms of accuracy, training time, and model complexity. Notably, the proposed model achieves a 50% reduction in training time and a 55.7% decrease in complexity compared to the Attnsleep model. The EASM achieves a classification accuracy of 85.8% with minimum loss when compared to the Attnsleep model. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Subject The topic of the resource CNN; Focal loss; Multi-head attention; Temporal context encoder Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 13807501; CODEN: MTAPF Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online