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1

Snoring Sound Recognition Using Multi-Channel Spectrograms

Ye Ziqiang, Peng Jianxin, Zhang Xiaowen, Song Lijuan

Archives of Acoustics

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2024

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Vol. 49, nr 2

169--178

EN

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common and high-risk sleep-related breathing disorder. Snoring detection is a simple and non-invasive method. In many studies, the feature maps are obtained by applying a short-time Fourier transform (STFT) and feeding the model with single-channel input tensors. However, this approach may limit the potential of convolutional networks to learn diverse representations of snore signals. This paper proposes a snoring sound detection algorithm using a multi-channel spectrogram and convolutional neural network (CNN). The sleep recordings from 30 subjects at the hospital were collected, and four different feature maps were extracted from them as model input, including spectrogram, Mel-spectrogram, continuous wavelet transform (CWT), and multi-channel spectrogram composed of the three single-channel maps. Three methods of data set partitioning are used to evaluate the performance of feature maps. The proposed feature maps were compared through the training set and test set of independent subjects by using a CNN model. The results show that the accuracy of the multi-channel spectrogram reaches 94.18%, surpassing that of the Mel-spectrogram that exhibits the best performance among the single-channel spectrograms. This study optimizes the system in the feature extraction stage to adapt to the superior feature learning capability of the deep learning model, providing a more effective feature map for snoring detection.

2

Hemodynamic impact of snoring on carotid artery after stent implantation: the role of oscillation

Zhang Xianghao, Fan Zhenmin, Liu Xiao, Liu Mingyuan, Ye Xia, Deng Xiaoyan

Acta of Bioengineering and Biomechanics

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2023

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Vol. 25, no. 1

35--45

EN

Snoring is common in overweight and elderly patients treated by endovascular stenting. Studies have proved a correlation between snoring and carotid stenosis, thus, snoring after carotid artery stenting (CAS) might promote or worsen clinical performance. This study tested this hypothesis by constructing a patient-specific carotid bifurcation model and numerically analyzing hemodynamic changes of the carotid artery under different snoring conditions. These conditions included small and large amplitude, low and high frequency, and different age groups. The results found that high amplitude snoring suppressed the disturbed flow at the stented segment while the downstream region of ICA became more chaotic, accounting for in-stent intimal restenosis and thrombosis. Furthermore, local blood flow patterns of elder groups with snoring symptoms were more likely to be changed due to low-speed flow, increasing the possibility of vascular remodeling and thrombosis. Besides, increased snoring frequency hardly influenced the local disturbed flow. Therefore, older adults should receive medical treatment actively after stenting for high-amplitude snoring as soon as possible to avoid potential adverse events.

3

Possibilities of diagnosing breathing disorders during sleep in home conditions

Górnicka Dorota, Kuryjański Ryszard, Michalik Michał

Diagnostyka

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2021

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Vol. 22, No. 4

115--121

EN

Disorders of breathing during sleep not only adversely affect the condition of the body during the daytime, but, above all, can be dangerous to health and life. Clinical methods of diagnosing these disorders are highly developed and, as a result, allow to effectively eliminate the problem, but still the problem is early diagnosis at home, which will be the basis for reporting to the doctor for extended examinations. This paper presents a proposed algorithm for inferring sleep-disordered breathing supported by conclusions from work on investigating the associations of discriminants with selected fragments of acoustic signals. The effectiveness of the developed algorithm was verified on a test sample of acoustic signals from selected patients treated by the MML clinic. The results of the study are the basis for the development of a numerical application for preclinical diagnosis of sleep apnea and sleep-disordered breathing. The verification of the algorithm carried out on real examples confirms the correctness of the assumptions made, demonstrates its effectiveness and suitability for use in a mobile application.

PL

Zaburzenia oddychania podczas snu nie tylko niekorzystnie wpływają na kondycję organizmu w porze dziennej, ale przede wszystkim mogą być niebezpieczne dla zdrowia i życia. Kliniczne metody diagnozowania tych zaburzeń są wysoko rozwinięte i w efekcie pozwalają na skuteczne wyeliminowanie problemu, jednak nadal problemem jest wczesna diagnostyka w warunkach domowych, która będzie podstawą do zgłoszenia się do lekarza w celu przeprowadzenia rozszerzonych badań. W artykule przedstawiono propozycję algorytmu wnioskowania o zaburzeniach oddychania w trakcie snu popartego wnioskami z prac nad badaniem związków wyróżników z wybranymi fragmentami sygnałów akustycznych. Skuteczność opracowanego algorytmu zweryfikowano na próbce testowej sygnałów akustycznych pochodzących od wybranych pacjentów leczonych przez klinikę MML. Rezultaty przeprowadzonych badań są podstawą opracowania aplikacji numerycznej służącej przedklinicznemu diagnozowaniu bezdechów śródsennych i zaburzeń oddychania podczas snu. Przeprowadzona na rzeczywistych przykładach weryfikacja algorytmu potwierdza poprawność przyjętych założeń, wykazuje jego skuteczność i przydatność do zastosowania w aplikacji mobilnej.

4

Snoring as a sign of abnormality

Czopek K.

Bio-Algorithms and Med-Systems

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2011

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Vol. 7, no. 1

37--42

EN

Medical support is needed for subjects who snore to get a good night’s sleep. This prospective study is aimed to show how sleep disorders influence a good night sleep. One of the common sleep disorder that affects people of all ages is snoring. Snoring sound could be a sign of cardiovascular disease. Correct interpretation remains a very significant problem in dealing with respiratory sounds, such as snoring and/or breathing. Acoustic characteristics of snoring sounds, which are approximately periodic waves with noise, can be analyzed by using a multidimensional voice program MDVP. Acoustic analysis techniques give information on the mechanism, loudness, intensity and sites of obstruction of upper airways. Further conclusions can be made by comparing simultaneously gathered acoustic and electrocardiographic signals.