Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM

• Published in: Biomedical signal processing and control Vol. 80; p. 104351
• Main Authors: Ding, Li, Peng, Jianxin, Song, Lijuan, Zhang, Xiaowen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023
• Subjects: LSTM; Obstructive sleep apnea hypopnea syndrome; Snoring sounds; Transfer learning; VGG19; Obstructive sleep apnea hypopnea syndrome; LSTM; VGG19; Snoring sounds; Transfer learning
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2022.104351

Snoring is a typical syndrome of obstructive sleep apnea hypopnea syndrome (OSAHS). The acoustic analysis of snoring sound has been proved potential to develop a non-invasive approach for assisting diagnose OSAHS. In this work, a pre-trained VGG19 and the long short-term memory (LSTM) fused model was proposed to classify snoring sounds of simple snorers and OSAHS patients and detect apnea-hypopnea snoring from the whole night recorded sounds of patients. Mel-spectrograms of snoring sounds were fed into the VGG19 + LSTM model to learn relatively distinguishable features. Compared with other fused models, the proposed VGG19 + LSTM model yielded the highest accuracy of 99.31 % in classifying simple snorers’ snoring and OSAHS patients’ snoring. For distinguishing normal snoring and apnea-hypopnea snoring of patients, the VGG19 + LSTM achieved 85.21 % and 66.29 % accuracies based on hold-out and leave-one-subject-out validation methods respectively. The estimated AHI highly correlated with PSG AHI with a Pearson correlation coefficient of 0.966 (p < 0.001). Results of the proposed model demonstrate that acoustic analysis of snoring sounds has great potential for screening sleep and diagnosing OSAHS.