The invention discloses a
deep learning OSA severity classification model based on vowels. The method comprises the following steps: 1, collecting
vowel speech signals of a subject, wherein the
vowel speech signals comprise five Chinese vowels: / a / , / o / , / e / , / i / and / u / ; step 2, obtaining an AHI index obtained after the subject is subjected to whole-night PSG, and grading the severity of the subject; and step 3, extracting four kinds of paranguage features from the voice
signal, wherein the four kinds of paranguage features comprise eGemaps, emobase, ComParE16 and MRCG. Fusing the four sub-language features of the five vowels into a
vowel feature graph, and providing a
feature set with the best detection efficiency for subsequent training; and step 4, combining the obtained vowel feature map with baseline features (including age, BMI, height, weight,
neck circumference and abdominal circumference), inputting the obtained vowel feature map into a constructed
convolutional neural network (CNN) for training and testing, and obtaining classification results under different AHI severity degrees.