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Meas."],"published-print":{"date-parts":[[2026,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Manual sleep staging is labor-intensive, whereas many automated systems remain computationally demanding and are validated on a single dataset. This study aimed to develop a lightweight multimodal framework for automated sleep staging and exploratory sleep-disorder screening from polysomnographic (PSG) signals.\n                    <jats:italic>Approach.<\/jats:italic>\n                    SleepMambaNet combines efficient channel attention to integrate complementary information across PSG channels with bidirectional Mamba modules to model long-range temporal dependencies between sleep epochs. The framework was evaluated on the public ISRUC-S1 and ISRUC-S3 datasets and a multicenter clinical cohort of 185 PSG recordings.\n                    <jats:italic>Main results.<\/jats:italic>\n                    In subject-independent experiments, SleepMambaNet achieved accuracy, macro-\n                    <jats:italic>F<\/jats:italic>\n                    1, and Cohen\u2019s kappa of 0.852, 0.824, and 0.803 on ISRUC-S3; 0.830, 0.801, and 0.773 on ISRUC-S1; and 0.854, 0.819, and 0.806 on the combined public and clinical data, respectively. The external cross-dataset evaluation yielded accuracy, macro-\n                    <jats:italic>F<\/jats:italic>\n                    1, and kappa of 0.828, 0.812, and 0.779. For exploratory sleep-disorder screening, the model achieved accuracy of 0.892,\n                    <jats:italic>F<\/jats:italic>\n                    1 score of 0.887, sensitivity of 0.956, specificity of 0.842, and precision-recall area under the curve of 0.831.\n                    <jats:italic>Significance.<\/jats:italic>\n                    These results show that cross-channel physiological information and inter-epoch temporal context can be modeled effectively with only 0.47 million trainable parameters across heterogeneous public and clinical PSG data. The combination of computational efficiency and cross-dataset performance provides a practical basis for prospective evaluation of automated PSG decision support, while the binary screening output should not be interpreted as a disease-specific or standalone clinical diagnosis.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/aeab40","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T07:11:08Z","timestamp":1790838668000},"page":"105002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SleepMambaNet: a lightweight multimodal framework for sleep staging and sleep disorder screening with multicenter validation"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0903-1119","authenticated-orcid":true,"given":"Chao","family":"Zhang","sequence":"first","affiliation":[{"name":"Centre for Artificial Intelligence Driven Drug Discovery"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Queliang","family":"Wang","sequence":"additional","affiliation":[{"name":"The Third People\u2019s Hospital of Zhuhai"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Yaping","family":"Liu","sequence":"additional","affiliation":[{"name":"The Affiliated Brain Hospital, Guangzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Xiaoliang","family":"Li","sequence":"additional","affiliation":[{"name":"The Third People\u2019s Hospital of Zhuhai"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Fan","family":"Jiang","sequence":"additional","affiliation":[{"name":"The Affiliated Brain Hospital, Guangzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Shuyan","family":"Li","sequence":"additional","affiliation":[{"name":"Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Weirong","family":"Cui","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Driven Drug Discovery"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4632-4364","authenticated-orcid":false,"given":"Jingjing","family":"Guo","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Driven Drug Discovery"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"266","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"pmeaaeab40bib1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3389\/fninf.2019.00045","type":"journal-article","article-title":"Detection of EEG K-complexes using fractal dimension of time frequency images technique coupled with undirected graph features","volume":"13","author":"Al-Salman","year":"2019","journal-title":"Front. 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