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Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low\u2010cost and easy\u2010to\u2010use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)\u2010based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four\u2010level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy\u2010to\u2010use and effective screening tool for OSA accordingly.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200275","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:08:01Z","timestamp":1674086881000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Signal Segmentation\u2010Free Model for Electrocardiogram\u2010Based Obstructive Sleep Apnea Severity Classification"],"prefix":"10.1002","volume":"5","author":[{"given":"Jeng-Wen","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery Cardinal Tien Hospital and School of Medicine College of Medicine Fu Jen Catholic University  362, Zhongzheng Rd., Xindian Dist. 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