A viable snore detection system: hardware and software implementations
A viable snore detection system: hardware and software implementations
A stand-alone, custom-made biomedical system was introduced for long-term monitoring of sleep anddetection of snoring events. Commercially available electronic components were assembled for recording audio, pulse, andrespiration signals. Its software was implemented for off-line processing of the acquired signals in C++ and MATLABenvironments. The linear and nonlinear features of the signals were extracted and characterized using spectral energydistribution, entropy, and largest Lyapunov exponent (LLE). The performance of the system was evaluated with realphysiological data gathered from 14 chronic snorers. Analysis of the cases indicated that the system identified the snoringevents with an accuracy of 88.22%, sensitivity of 94.91%, and positive predictive value of 90.95%. This high level ofvalidation confirmed the reliability and utility of the system in detecting snoring.
___
- [1] Pevernagie D, Aarts R, De Meyer D. The acoustics of snoring. Sleep Medicine Reviews 2010; 14 (2): 131–134.
- [2] Main C, Liu Z, Welch K, Weiner G, Jones SQ et al. Surgical procedures and non-surgical devices for the management
of non-apnoeic snoring: a systematic review of clinical effects and associated treatment costs. Health Technology
Assessment 2009; 13 (3): 1-6.
- [3] Benjamin JA, Lewis KE. Sleep-disordered breathing and cardiovascular disease. Journal of Postgraduate Medicine
2008; 84: 15-22.
- [4] Arzt M, Young T, Finn L, Skatrud JB, Bradley TD. Association of sleep disordered breathing and the occurrence
of stroke. American Journal of Respiratory and Critical Care Medicine 2005; 172: 1447–1451.
- [5] Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing
and hypertension. New England Journal of Medicine 2000; 342: 1378–1384.
- [6] Gami AS, Howard DE, Olson EJ, Somers VK. Day-night pattern of sudden death in obstructive sleep apnea. New
England Journal of Medicine 2005; 352: 1206–1214.
- [7] Ambrosetti M, Lucioni A, Ageno W, Conti S, Neri M. Is venous thromboembolism more frequent in patients with
obstructive sleep apnea syndrome? Journal of Thrombosis and Haemostasis 2004; 2: 1858–1860.
- [8] Kim T, Kim JW, Lee K. Detection of sleep disordered breathing severity using acoustic biomarker and machine
learning techniques. BioMedical Engineering Online 2018; 17 (1): 1-19.
- [9] Wang C, Peng J. The methods of acoustical analysis of snoring for the diagnosis of OSAHS. Journal of Sleep
Medicine and Disorders 2017; 4 (2): 1-7.
- [10] Yaganoglu M, Kayabekir M, Kose C. SNORAP: A device for the correction of Impaired Sleep health by using tactile
stimulation for individuals with mild and moderate sleep disordered breathing. Sensors 2017; 17 (9): 1-17.
- [11] Przystup P, Bujnowski A, Ruminski J, Wtorek J. A detector of sleep disorders for using at home. Journal of
Telecommunications and Information Technology 2014; 2: 70—78.
- [12] Hara H, Tsutsumi M, Tarumato S, Shiga T, Yamasita H. Validation of a new snoring detection device based on a
hysteresis extraction algorithm. Auris Nasus Larynx 2017; 44 (5): 576-582.
- [13] Qian K, Xu Z, Xu H, Wu Y, Zhao Z. Automatic detection, segmentation and classification of snore related signals
from overnight audio recording. IET Signal Processing 2015; 9 (1): 21–29.
- [14] Calabrese B, Pucci F, Sturniolo M, Veltri P, Gambardella A et al. A system for the analysis of snore signals.
Procedia Computer Science 2011; 4: 1101-1108.
- [15] Jin H, Lee L, Song L, Li Y, Peng J et al. Acoustic analysis of snoring in the diagnosis of obstructive sleep apnea
syndrome: a call for more rigorous studies. Journal of Clinical Sleep Medicine 2015; 11 (7): 765–771.
- [16] Wang C, Peng J. The methods of acoustical analysis of snoring for the diagnosis of OSAHS. Journal of Sleep
Medicine Disorders 2017; 4 (2): 1-7.
- [17] Bhat S, Ferraris A, Gupta D, Mozafarian M, DeBari VA et al. Is there a clinical role for smartphone sleep apps?
Comparison of sleep cycle detection by a smartphone application to polysomnography. Journal of Clinical Sleep
Medicine 2015; 11 (7): 709–715.
- [18] Ankishan H, Aydin H. A new wristwatch based medical device for sleep research/studies: patient arm monitor. In:
International Advanced Researches Engineering Congress; Osmaniye, Turkey; 2017. pp. 1-5.
- [19] Fischer T, Schneider J, Stork W. Classification of breath and snore sound s using audio data recorded with
smartphones in the home. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP);
Shanghai, China; 2016. pp. 226-230.
- [20] Dafna E, Tarasiuk A, Zigel Y. Automatic detection of whole night snoring events using non-contact microphone.
PLoS One 2013; 8 (12): e84139.
- [21] Azarbarzin A, Moussavi ZM. Automatic and unsupervised snore sound extraction from respiratory sound signals.
IEEE Transactions on Biomedical Engineering 2011; 58: 1156–1162.
- [22] Azarbarzin A, Moussavi Z. Snoring sounds variability as a signature of obstructive sleep apnea. Medical Engineering
Physics 2013; 35 (4): 479-485.
- [23] Maali Y, Al-Jumaily A. Hierarchical parallel PSO-SVM based subject-independent sleep apnea classification. In:
International Conference on Neural Information Processing (ICONIP); Doha, Qatar; 2012. pp. 500-507.
- [24] Avcı C, Akbaş, A. Sleep apnea classification based on respiration signals by using ensemble methods. Bio-Medical
Materials and Engineering 2005; 26: 1703–1710.
- [25] Fontenla-Romero O, Guijarro-Berdiñas B, Alonso-Betanzos A, Moret-Bonillo V. A new method for sleep apnea
classification using wavelets and feedforward neural networks. Artificial Intelligence in Medicine 2005; 34: 65-76.
- [26] Khan T. A deep learning model for snoring detection and vibration notification using a smart wearable gadget.
Electronics 2019; 8 (9): 2-19.
- [27] Haidar R, Koprinska I, Jeffries B. Sleep apnea event detection from nasal airflow using convolutional neural networks.
Lecture Notes in Computer Science 2017; 10638: 819-827.
- [28] Khan MN, Nock R, Gooneratne NS. Mobile devices and insomnia: understanding risks and benefits. Current Sleep
Medicine Reports 2015; 1 (4): 226-231.
- [29] Tal A, Shinar Z, Shaki D, Codish S, Goldbart A. Validation of contact-free sleep monitoring device with comparison
to polysomnography. Journal of Clinical Sleep Medicine 2017; 13 (3): 517-522.
- [30] Çavuşoğlu M, Poets CF, Urschitz MS. Acoustics of snoring and automatic snore sound detection in children.
Physiological Measurement 2017; 38 (11): 1919-1938.
- [31] Shin H, Cho J. Unconstrained snoring detection using a smartphone during ordinary sleep. BioMedical Engineering
Online 2014; 13: 1-14.
- [32] Ankishan H, Yilmaz D. Comparison of SVM and ANFIS for snore related sounds classification by using the largest
Lyapunov exponent and entropy. Computational and Mathematical Methods in Medicine 2013; 2013: 1-13.
- [33] Williams GP. Chaos Theory Tamed. Washington, DC, USA: Joseph Henry Press, 1997.
- [34] Rosenstein MT, Collins JJ, De Luca CJ. A practical method for calculating largest Lyapunov exponents from small
data sets. Physica D 1993; 65 (1-2): 117–134.
- [35] Takens F. Detecting strange attractors in turbulence. Lecture Notes in Mathematics 1981; 898: 366–381.
- [36] Cortes C, Vapnik V. Support-vector networks. Machine Learning 1995; 20 (3): 273-297.
- [37] Hoffstein V, Mateika S, Nash S. Comparing perceptions and measurements of snoring. Sleep 1996; 19: 783–789.
- [38] Samuelsson LB, Rangarajan AA, Shimada K, Krafty RT, Buysse JD et al. Support vector machines for automated
snoring detection: proof-of-concept. Sleep Breath 2017; 21 (1): 119–133.
- [39] Wang C, Peng J, Song L, Zhang X. Automatic snoring sounds detection from sleep sounds via multi-features
analysis. Australasian Physical and Engineering Sciences in Medicine 2017; 40: 127–135.
- [40] Karunajeewa AS, Abeyratne UR, Hukins C. Silence breathing-snore classification from snore-related sounds. Physiological Measurement 2008; 29 (2): 227–243.
- [41] Çavuşoğlu M, Kamasak M, Eroğul O, Çiloğlu T, Serinağaoglu Y, Akçam T. An efficient method for snore/nonsnore
classification of sleep sounds. Physiological Measurement 2008; 28 (8): 841–853.
- [42] Duckitt WD, Tuomi SK, Niesler TR. Automatic detection, segmentation and assessment of snoring from ambient
acoustic data. Physiological Measurement 2006; 27: 1047–1056.
- [43] Ko PR, Kientz JA, Choe EK, Kay M, Landis CA et al. Consumer sleep technologies: a review of the landscape.
Journal of Clinical Sleep Medicine 2015; 11 (1): 1455-1461.