Temat: snoring sound - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Detector of Sleep Disorders for Using at Home
Autorzy:: Przystup, P.
Bujnowski, A.
Rumiński, J.
Wtorek, J.
Powiązania:: https://bibliotekanauki.pl/articles/309022.pdf
Data publikacji:: 2014
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: ECG
larynx vibration
sleep apnea
snoring sound
thoracic impedance
Opis:: Obstructive sleep apnea usually requires all-night examination in a specialized clinic, under the supervision of a medical staff. Because of those requirements it is an expensive and a non-widely utilized test. Moving the examination procedure to patients’ home with automatic analysis algorithms involved will decrease the costs and make it available for larger group of patients. The developed device allows all-night recordings of the following biosignals: three channels ECG, thoracic impedance (respiration), snoring sounds and larynx vibrations. Additional information, like patient’s body position changes and electrodes’ attachment quality are estimated as well. The reproducible and high quality signals are obtained using the developed and unobtrusive device.
Źródło:: Journal of Telecommunications and Information Technology; 2014, 2; 70-78
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring
Autorzy:: Wang, Can
Peng, Jianxin
Zhang, Xiaowen
Powiązania:: https://bibliotekanauki.pl/articles/176601.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: acoustical analysis
feature extraction
support vector machine
snoring sound
Opis:: Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.
Źródło:: Archives of Acoustics; 2020, 45, 1; 141-151
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

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