Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review

Table 3 The performance of snoring sounds classification of obstructive sleep apnea-hypopnea syndrome patients

Ref.	Image type	Model	Classification	Classification results
Song et al[55]	Mel-spectrogram	XGBoost, CNN, ResNet	OSAHS snoring vs simple snore	Acc: 83.44%, Sen: 85.27%
Ding et al[46]	Mel-spectrogram	VGG19 + LSTM	Simple snoring vs OSAHS snoring	Acc: 85.21%
Cheng et al[65]	MFCC, Fbanks, LPC	LSTM	Apnea vs normal snoring,	Acc: 95.3%
Li et al[66]	Spectrogram, Mel-spectrogram	CNN	OSAHS detection	Acc: 92.5%, Sen: 93.9%, Spc: 91.2%
Serrano et al[67]	Mel-spectrogram	VGGish + bi-LSTM	Apnea vs non-apnea	Acc: 95%

Acc: Accuracy; Sen: Sensitivity; Spe: Specificity; Spe: Specificity; CNN: Convolutional neural network; LSTM: Long short-term memory; OSAHS: Obstructive sleep apnea-hypopnea syndrome; MFCC: Mel-frequency cepstral coefficients; LPC: Linear predictive coding.