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.

Table 2 The performance of snoring sounds detection based on different works

Ref.	Image type	Model	Main results
Hong et al[56]	Log-Mel spectrogram	Vision Transformer-based deep learning model	Sen: 89.8%, Spe: 91.3%, Acc: 95.9%
Romero et al[58]	Bottleneck features	Deep autoencoder, auditory model	F1: 94.75%
Liu et al[59]	Time-domain waveform, spectrogram, Mel-spectrogram	MobileNetV2 CNN	Acc: 95.00%
Ye et al[57]	Spectrogram, Mel-spectrogram, CWT	CNN, multi-channel spectrogram	Acc: 94.18%
Lim et al[44]	Time-domain waveform, spectrogram, Mel-spectrogram	RNN	Acc: 98.9%
Jiang et al[60]	Time-domain waveform, spectrum, spectrogram, Mel-spectrogram, CQT-spectrogram	CNNs-DNNs, CNNs-LSTMs-DNNs	Acc: 95.00%
Li et al[61]	Spectrogram	1D CNN, 2D CNN (visibility graph)	Acc: 89.3%, Sen: 89.7%, Spe: 88.5%
Xie et al[62]	Spectrogram	CNN, RNN	Acc: 95.3%, Sen: 92.2%, Spe: 97.7%
González-martínez et al[63]	Harmonic spectrogram	CNN	AUC: 0.89

Acc: Accuracy; Sen: Sensitivity; Spe: Specificity; CNN: Convolutional neural network; DNN: Deep neural network; RNN: Recurrent neural network; LSTM: Long short-term memory; CWT: Continuous wavelet transform.

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.