Reference : Midsagittal Jaw Movement Analysis for the Scoring of Sleep Apneas and Hypopneas
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/62984
Midsagittal Jaw Movement Analysis for the Scoring of Sleep Apneas and Hypopneas
English
Senny, Frédéric [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Electronique, microsystèmes, mesures et instrumentation >]
Destiné, Jacques mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Electronique, microsystèmes, mesures et instrumentation >]
Poirrier, Robert [Université de Liège - ULg > Services généraux (Faculté de médecine) > Relations académiques et scientifiques (Médecine)]
Jan-2008
IEEE Transactions on Biomedical Engineering
55
1
87-95
Yes (verified by ORBi)
International
0018-9294
[en] Midsagittal Jaw Movements ; Sleep apneas ; classification
[en] Given the importance of the detection and classification of sleep apneas and hypopneas (SAHs) in the diagnosis and the characterization of the SAH syndrome, there is a need for a reliable noninvasive technique measuring respiratory effort. This paper proposes a new method for the scoring of SAHs based on the recording of the midsagittal jaw motion (MJM, mouth opening) and on a dedicated automatic analysis of this signal. Continuous wavelet transform is used to quantize respiratory effort from the jaw motion, to detect salient mandibular movements related to SAHs and to delineate events which are likely to contain the respiratory events. The classification of the delimited events is performed using multilayer perceptrons which were trained and tested on sleep data from 34 recordings. Compared with SAHs scored manually by an expert, the sensitivity and specificity of the detection were 86.1% and 87.4%, respectively. Moreover, the overall classification agreement in the recognition of obstructive, central, and mixed respiratory events between the manual and automatic scorings was 73.1%. The MJM signal is hence a reliable marker of respiratory effort and allows an accurate detection and classification of SAHs.
http://hdl.handle.net/2268/62984

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