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The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome, not readily available and represents a well-known roadblock to managing this huge societal burden. Assessment of neuromuscular function involved in the upper airway using electromyography (EMG) has shown potential to characterize and diagnose sleep apnea, while the development of transmembranous electromyography (tmEMG), a painless surface probe, has made this opportunity practical and highly feasible. However, experience and ability to interpret electrical signals from the upper airway are scarce, and much of the pertinent information within the signal is likely difficult to detect visually. To overcome this issue, we explored the use of transformers, a deep learning (DL) model architecture with attention mechanisms, to model tmEMG data and distinguish between electromyographic signals from a cohort of control, neurogenic, and sleep apnea patients. Our approach involved three strategies to train a generalizable model on a relatively small dataset including, (1) transfer learning using an audio spectral transformer (AST), (2) the use of 6,000 simulated EMG recordings, converted to spectrograms and using standard backpropagation for fine-tuning, and (3) application of regularization to prevent overfitting and enhance generalizability. This DL approach was tested using 177 transoral EMG recordings from a prior study\u2019s database that included six healthy controls, five moderate to severe OSA patients, and five amyotrophic lateral sclerosis (ALS) patients with evidence of bulbar involvement (neurogenic injury). Sensitivity and specificity for classifying neurogenic cases from controls were 98% and 73%, respectively, while classifying OSA from controls were 88% and 64%, respectively. Notably, by averaging the predicted probabilities of each segment for individual patients, the model correctly classified up to 82% of control and OSA patients. These results not only suggest a potential to diagnose OSA patients accurately, but also to identify OSA endotypes that involve neuromuscular pathology, which has major implications for clinical management, patient outcomes, and research.<\/jats:p>","DOI":"10.1186\/s12868-024-00913-9","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T09:29:32Z","timestamp":1735637372000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea"],"prefix":"10.1186","volume":"25","author":[{"given":"Ross","family":"Mandeville","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hooman","family":"Sedghamiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Perry","family":"Mansfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey","family":"Sheean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Studer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derrick","family":"Cordice","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghodsieh","family":"Ghanbari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atul","family":"Malhotra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shamim","family":"Nemati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jejo","family":"Koola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"913_CR1","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-1-60761-735-8_5","volume-title":"Essentials of Sleep Medicine: an Approach for Clinical Pulmonology","author":"JP Bakker","year":"2012","unstructured":"Bakker JP, Malhotra A, Patel SR. 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The protocol for this study is identified as Pro00075951. All research activities were conducted in accordance with the ethical standards of the Advarra IRB and the applicable national and international guidelines for human subjects research. Informed consent was obtained from all participants included in the study in accordance with the protocol approved by the Advarra IRB (IRB#00000971, Protocol #Pro00075951).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Informed consent to publish de-identified data was obtained from all participants included in the study, in accordance with the protocol approved by the Advarra IRB (IRB#00000971, Protocol #Pro00075951).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"CS, PM, HS, RM, GS, AM, SN: Receiving fees from Powell Mansfield, inc. CS, PM, RM, GS: Holding shares in Powell Mansfield, inc. Powell Mansfield, inc.: Holding patents relating to the content of the manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"80"}}