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

Table 1 Summary of the database information

Dataset name	Subject sample	Age range	Sampling rate	Recording environment
Snoring-detection[22]	1000 audio clips (500 snoring + 500 non- snoring)	Not specified	16 kHz	Various background environments
ICSD[23]	Infant crying and snoring recordings	0–2 years (infants)	Various	Indoor environments
PSG-audio corpus[24]	212 subjects	23–85 years	48 kHz	Sleep laboratories
SSBPR dataset[25]	20 patients for body position recognition	26–57 years	32 kHz	Hospital environment

PSG: Polysomnography.