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

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.