• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.560-565
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• 2010

In this study, the existence of chaotic characteristics in snoring signals obtained in the form of time series data was checked through quantitative and qualitative analysis methods, and a snoring signal detection system was designed applied with detection algorithms considering diverse parameters of occurring signals in order to enhance the accuracy and reliability of detections and the performance of the system was checked. The system was tested with certain snoring patients and thereby the results as follows could be obtained.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.327-333
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• 2002

In this study, we developed a snoring detection algorithm that detects snores automatically. It consists of preprocessing and snoring detection part. The preprocessing part is composed of a noise removal part using spectrum subtraction, and segmentation part, and computation part of temporal and spectral features. And the snoring detection part decides whether detected blocks are snores with BPNN(Back-Propagation Neural Network). BPNN with one hidden layer and one output layer, is trained with data of 7 subjects and tested with data of 11 subjects of total 18 subjects. The proposed algorithm showed a Sensitivity of 90.41% and a Predictive Positive Value of 84.95%.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.155-160
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• 2021

This paper proposes an automatic method for detecting snore sound, one of the important symptoms of sleep apnea patients. In the proposed method, sound signals generated during sleep are input to detect a sound generation section, and a spectrogram transformed from the detected sound section is applied to a classifier based on a Convolutional Bidirectional Gated Recurrent Unit (CBGRU) with attention mechanism. The applied attention mechanism improved the snoring sound detection performance by extending the CBGRU model to learn discriminative feature representation for the snoring detection. The experimental results show that the proposed snoring detection method improves the accuracy by approximately 3.1 % ~ 5.5 % than existing method.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.105-110
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• 2019

People sleep about one-third of their lives and their sleep time varies according to age. Adult usually sleep 8 hours a day. However, that dose not guarantee good sleep. The cause of this is due to sleep disorders like snoring and sleep apnea. In this paper, the smart pillow for detecting snoring among sleep disorders is investigated. This pillow consists of two microphones located on the left and right side of the pillow. For simple detecting, the snoring signal was converted into the pulse using a peak detection circuit. The decision of the snoring occurrence was by pulse duration. The accuracy of the snoring detection was about 97%. The research results show that the smart pillow can be use to detect the snoring during sleeping.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.143-149
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• 2015

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.212-217
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• 2019

In this paper, a sleep management pillow system for snoring detection and respiration measurement is investigated. The sleep management pillow system consists of four force sensing resistor(FSR) sensors, two microphones(MIC), a pillow, a measurement system. Four FSR sensors attached at the bottom part of the pillow are used for respiration measurement and snoring detection. Two microphones located at the middle left and right of the pillow are utilized for only snoring detection. The respiration and the snoring of ten young people were measured using the sleep management pillow system composed of a data acquisition board, interface circuit, and personal computer. The measurement accuracy of the respiration was about 98% and the measurement accuracy of the snoring was about 97%. The experiment results show that the sleep management pillow system can be used for snoring detection and respiration rate measurement during sleeping.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.11-18
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• 2018

Severe snoring can lead to OSA(Obstructive Sleep Apnea), which can lead to life-threatening cases, and snoring can lead to serious pernicious relationships. In order to solve these snoring problems, several types of smart pillows have recently been released. The core technology is snoring discrimination technology, ie, a technique for determining whether snoring is included in the input sound. In this paper, we propose a snoring detection method to apply to a smart pillow. After extracting the features of the snoring sound from the input signal, we discriminate the snoring using these features and SVM. In order to measure the performance of the proposed method, comparative experiments with the existing methods are performed. The experimental results show about 6% better discrimination performance than the existing method.

• Science of Emotion and Sensibility
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• v.14 no.3
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• pp.459-466
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• 2011

Sleep diseases such as snoring and sleep apnea are physically, mentally harmful and results serious health problems. Snoring, known as breathing noise, is caused by coupled oscillation of the airway when the air passes through the trachea, and sleep apnea is caused by upper airway blockage. In order to solve these problems, many attempts have been made to detect the snoring during sleep and alleviate it. In this study, a new sensing system and analysis algorithm were developed in order to detect snoring sounds correctly under various sleep environments. Two polyvinylidene fluoride (PVDF) vibration sensors were used inside the pillow. The first PVDF sensor detects vibration transmitted through skull caused by snoring. And the second PVDF sensor detects both snoring sounds and ambient noises. The signals of two sensors were acquired through the designed analog circuits, and analyzed for snoring detection. Ten volunteers were participated for the experiment under five different conditions. Data from two PVDF sensors were processed by the established analysis algorithm, and snoring sounds were compared to noises. The results indicated that the energy of snoring is 70% bigger than that of ambient noise, which proves effectiveness of sensing system and analysis algorithm. Further study would be continued for more wide clinical studies with various environment noises. Based on this study, development of anti-snore pillow and sleep monitoring system for comfort sleep could be developed.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.35-36
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• 2011

• Journal of Biomedical Engineering Research
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• v.35 no.4
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• pp.75-80
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• 2014

This paper proposed a method that can automatically classify sleep apnea by using features extracted from pulse rate variability(PRV) signals induced from piezo snoring sensor for patients with obstructive sleep apnea(OSA). We have extracted eight features(NN, SDNN, RMSSD, NN10, NN50, LF, HF and LF/HF ratio) based on time and frequency analyses of PRV. Sleep apnea was classified by a linear discriminant analysis(LDA). A performance was evaluated using snore recordings from 13 patients with OSA (ages: $54.5{\pm}10.5$ years, body mass index: $26.3{\pm}2.5kg/m^2$, apnea-hypopnea index: $19.2{\pm}6.0/h$). The sensitivity and specificity were $78.9{\pm}0.9%$ and $78.9{\pm}0.9%$ for training set and $77.7{\pm}10.9%$ and $79.0{\pm}2.8%$ for test set, respectively. Our study demonstrated the feasibility of implementing a piezo snoring sensor based on a portable device as a simple and cost-effective solution for contributing to the OSA screening.