{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T16:26:59Z","timestamp":1776788819998,"version":"3.51.2"},"reference-count":46,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sleep apnea syndrome (SAS) affects about 3\u20137% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS is polysomnography (PSG), an intrusive procedure that depends on subjective assessment by expert clinicians. To address the limitations of PSG, we propose a decision support system, which uses a tracheal microphone for data collection and a deep learning (DL) approach\u2014namely SiCRNN\u2014to detect apnea events during overnight sleep recordings. Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). The final detection of apnea events is performed using an unsupervised clustering algorithm, specifically k-means. Multiple experimental runs were carried out to determine the optimal network configuration and the most suitable type and frequency range for the input data. Tests with data from eight patients showed that our method can achieve a Recall score of up to 95% for apnea events. We also compared the proposed approach to a fully convolutional baseline, recently introduced in the literature, highlighting the effectiveness of the Siamese training paradigm in improving the identification of SAS.<\/jats:p>","DOI":"10.3390\/s24237782","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T08:33:51Z","timestamp":1733387631000},"page":"7782","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7671-0318","authenticated-orcid":false,"given":"Davide","family":"Lillini","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5754-4864","authenticated-orcid":false,"given":"Carlo","family":"Aironi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2388-1501","authenticated-orcid":false,"given":"Lucia","family":"Migliorelli","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3812-0900","authenticated-orcid":false,"given":"Leonardo","family":"Gabrielli","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9374-0128","authenticated-orcid":false,"given":"Stefano","family":"Squartini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Platon, A.L., Stelea, C.G., Boi\u0219teanu, O., Patrascanu, E., Zetu, I.N., Ro\u0219u, S.N., Trifan, V., and Palade, D.O. 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