Automatic snoring sounds detection from sleep sounds based on deep learning

Yanmei Jiang1, Jianxin Peng2, Xiaowen Zhang3
1School of Physics and Optoelectronics, South China University of Technology, Guangzhou, China
2School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510640, China
3State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery, Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University, Guangzhou 510120, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

Strollo PJ Jr, Rogers RM (1996) Obstructive sleep apnea. N Engl J Med 334(2):99–104

Lloberes P, DuránCantolla J, Martínez-García MÁ, Marín JM, Ferrer A, Corral J et al (2011) Diagnosis and treatment of sleep apnea-hypopnea syndrome. Arch Bronconeumol 47(3):143–156

Qian K, Janott C, Pandit V, Zhang ZX, Heiser C, Hohenhorst W et al (2016) Classification of the excitation location of snore sounds in the upper airway by acoustic multifeature analysis. IEEE Trans Biomed Eng 64(8):1731–1741

Zhao L, Huang XZ (2002) Overview of sleep snoring research. Chin Gen Pract 5(5):412–414

Abeyratne UR, Patabandi CKK, Puvanendran K (2001) Pitch-jitter analysis of snoring sounds for the diagnosis of sleep apnea. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 2072–2075

Le Bon O, Staner L, Hoffmann G, Dramaix M, San Sebastian I, Murphy JR et al (2001) The first-night effect may last more than one night. J Psychiatr Res 35(3):165–172

Beattie ZT, Hayes TL, Guilleminault C, Hagen CC (2013) Accurate scoring of the apnea–hypopnea index using a simple non-contact breathing sensor. J Sleep Res 22(3):356–362

Emoto T, Abeyratne UR, Kawano K, Okada T, Jinnouchi O, Kawata L (2018) Detection of sleep breathing sound based on artificial neural network analysis. Biomed Signal Process Control 41:81–89

Pevernagie D, Aarts RM, Meyer DE (2010) The acoustics of snoring. Sleep Med Rev 14:131–144

Ip MSM, Lam B, Ng MMT, Lam WK, Tsang KWT, Lam KSL (2002) Obstructive sleep apnea is independently associated with insulin resistance. Am J Respir Crit Care Med 165(5):670–676

Aldrich MS (1999) Sleep medicine. Springer, New York, USA

Perez-Padilla JR, Slawinski E, Difrancesco LM, Feige RR, Remmers JE, Whitelaw WA (1993) Characteristics of the snoring noise in patients with and without occlusive sleep apnea. Am Rev of Respir Dis 147(3):635–644

Fiz JA, Abad J, Jané R, Riera M, Mananas MA, Caminal P et al (1996) Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnea. Eur Respir J 9(11):2365–2370

Sola-Soler J, Jane R, Fiz JA, Morera J (2003) Spectral envelope analysis in snoring signals from simple snorers and patients with obstructive sleep apnea. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 2527–2530

Ng AK, Koh TS, Baey E, Lee TH, Abeyratne UR, Puvanendran K (2008) Could format frequencies of snore signals be an alternative means for the diagnosis of obstructive sleep apnea. Sleep Med 9(8):894–898

Duckitt WD, Tuomi SK, Niesler TR (2006) Automatic detection, segmentation and assessment of snoring from ambient acoustic data. Physiol Meas 27(10):1047–1056

Cavusoglu M, Kamasak M, Erogul O, Ciloglu T, Serinagaoglu Y, Akcam T (2007) An efficient method for snore/nonsnore classification of sleep sounds. Physiol Meas 28(8):841–853

Dafna E, Tarasiuk A, Zigel Y (2013) Automatic detection of whole night snoring events using non-contact microphone. PLoS ONE 8:e84139

Nguyen TL, Yonggwan W (2015) Sleep snoring detection using multi-layer neural networks. Biomed Mater Eng 26:1749–1755

Mikami T, Kojima Y, Yonezawa K, Yamamoto M, Furukawa M (2013) Spectral classification of oral and nasal snoring sounds using a support vector machine. J Adv Comput Intell Intell Inform 17(4):611–621

Goswami U, Black A, Krohn B, Meyers W, Iber C (2019) Smartphone-based delivery of oropharyngeal exercises for treatment of snoring: a randomized controlled trial. Sleep Breath 23(1):243–250

Wang C, Peng JX, Song LJ, Zhang XW (2016) Automatic snoring sounds detection from sleep sounds via multi-features analysis. Australas Phys Eng Sci Med 40(1):1–9

Samuelsson LB, Rangarajan AA, Shimada K, Krafty RT, Buysse DJ, Strollo PJ, Kravitz HM, Zheng HY, Hall MH (2017) Support vector machines for automated snoring detection: proof-of-concept. Sleep Breath 21(1):119–133

Khan T (2019) A deep learning model for snoring detection and vibration notification using a smart wearable gadget. Electronics 8(9):987

Abeyratne UR, Wakwella AS, Hukins C (2005) Pitch jump probability measures for the analysis of snoring sounds in apnea. Physiol Meas 26(26):779–798

Wu PP, Zhao G, Zhou M (2008) Improved spectral subtraction based on multi-window spectrum estimation. Mod Electron Technol 12:150–152

Yi H, Loizou PC (2004) Speech enhancement based on wavelet thresholding the multiaper spectrum. IEEE Trans Speech Audio Proc 12(1):59–67

Scalart P, Filho JV (1996) Speech enhancement based on a priori signal to noise estimation. In: 1996 IEEE international conference on acoustics, speech, and signal processing conference proceedings. IEEE, pp 629–632

New TL, Tran HD, Ng WZT, Ma B (2017) An integrated solution for snoring sound classification using Bhattacharyya distance based GMM supervectors with SVM, feature selection with random forest and spectrogram with CNN. Proc Interspeech 2017:3467–3471

Amiriparian S, Gerczuk M, Ottl S, Cummins N, Freitag M, Pugachevskiy S et al (2017) Snore sound classification using image-based deep spectrum features. Proc Interspeech 2017:3512–3516

Rabiner LR, Gold B, Yuen CK (1978) Theory and application of digital signal processing. IEEE Trans Syst Man Cyber 23(2):146–146

Brown JC, Puckette MS (1992) An efficient algorithm for the calculation of a constant Q transform. J Acoust Soc Am 92(5):2698–2701

Schorkhuber C, Klapuri A. Constant Q transform toolbox for music processing. In: 7th Sound and Music Computing Conference, Barcelona, Spain, pp 210–217

Brown JC (1991) Calculation of a constant Q spectral transform. J Acoust Soc Am 89(1):425–434

Todisco M, Delgado H, Evans N (2016) A new feature for automatic speaker verification anti-spoofing: constant Q cepstral coefficients. In: Proc. ISCA Odyssey, pp 283–290

Qian K, Xu ZY, Xu HJ, Wu YQ, Zhao Z (2015) Automatic detection, segmentation and classification of snore related signals from overnight audio recording. IET Signal Proc 9(1):21–29

Abdel-Hamid O, Mohamed A R, Jiang H, Penn G, Yu D (2012) Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 4277–4280

Abdel-Hamid O, Mohamed AR, Jiang H, Deng L, Penn G, Yu D (2014) Convolutional neural networks for speech recognition. IEEE/ACM Trans Audio Speech Lang Process 22(10):1533–1545

Sainath TN, Vinyals O, Senior A, Sak H (2015) Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 4580–4584

Liu SY, Deng WH (2015) Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian conference on pattern recognition (ACPR), pp 730–734

Peng C, Zhang XY, Yu G, Luo GM, Sun J (2017) Large Kernel matters—improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4353–4361