Copyright
        ©The Author(s) 2025.
    
    
        World J Radiol. Sep 28, 2025; 17(9): 109116
Published online Sep 28, 2025. doi: 10.4329/wjr.v17.i9.109116
Published online Sep 28, 2025. doi: 10.4329/wjr.v17.i9.109116
            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 | 
            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 | 
            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% | 
- Citation: Ding L, Peng JX, Song YJ. Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review. World J Radiol 2025; 17(9): 109116
- URL: https://www.wjgnet.com/1949-8470/full/v17/i9/109116.htm
- DOI: https://dx.doi.org/10.4329/wjr.v17.i9.109116

 
         
                         
                 
                 
                 
                 
                 
                         
                         
                        