{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:02:50Z","timestamp":1781881370329,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"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>Obstructive sleep apnea\u2013hypopnea syndrome (OSAHS) is a prevalent sleep disorder strongly linked to increased cardiovascular and metabolic risk. While prior studies have explored snore-based analysis for OSAHS, they have largely focused on either detection or classification in isolation. Here, we present a two-stage framework that integrates precise snoring event detection with deep learning-based classification. In the first stage, we develop an Adaptive Multi-Feature Fusion Endpoint Detection algorithm (AMFF-ED), which leverages short-time energy, spectral entropy, zero-crossing rate, and spectral centroid to accurately isolate snore segments following spectral subtraction noise reduction. Through adaptive statistical thresholding, joint decision-making, and post-processing, our method achieves a segmentation accuracy of 96.4%. Building upon this, we construct a balanced dataset comprising 6830 normal and 6814 OSAHS-related snore samples, which are transformed into Mel spectrograms and input into ERBG-Net\u2014a hybrid deep neural network combining ECA-enhanced ResNet18 with bidirectional GRUs. This architecture captures both spectral patterns and temporal dynamics of snoring sounds. The experimental results demonstrate a classification accuracy of 95.84% and an F1 score of 94.82% on the test set, highlighting the model\u2019s robust performance and its potential as a foundation for automated, at-home OSAHS screening.<\/jats:p>","DOI":"10.3390\/s25175483","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T15:15:57Z","timestamp":1756912557000},"page":"5483","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Non-Contact Screening of OSAHS Using Multi-Feature Snore Segmentation and Deep Learning"],"prefix":"10.3390","volume":"25","author":[{"given":"Xi","family":"Xu","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China"},{"name":"School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghua","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9509-0755","authenticated-orcid":false,"given":"Xinpan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1111\/jsr.12334","article-title":"The prevalence and characteristics of obstructive sleep apnea in hospitalized patients with type 2 diabetes in China","volume":"25","author":"Zhang","year":"2016","journal-title":"J. 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