J Clin Monit Comput - Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals.

Tópicos

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Resumo

JECTIVE: Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5?s period.METHODS: Eight level wavelet packet analysis was performed on each 5?s clip using Daubechies (DB3) mother wavelet and for comparison discrete wavelet analysis was performed using Symlet (SYM3) wavelets. The choice of wavelet basis function was based on a grid search using Daubechies, Symlet and biorthogonal wavelets with decomposition levels varying between 2 and 5. Support vector machine is used for two-class classification. Out of 29 overnight polysomnographic studies, 23 of them were used in the training phase and 6 patients were used for independent testing.RESULTS: The proposed algorithm is shown to perform better in classifying CSA and OSA with wavelet packet features (accuracy-91%, sensitivity-88.14% and specificity-91.11%) than with the traditional wavelet decomposition based features (accuracy-83.79%, sensitivity-89.18% and specificity-83.59%). The independent test resulted in overall classification accuracy, sensitivity and specificity of 91.08, 91.02 and 91.09% respectively using wavelet packet analysis.CONCLUSIONS: The classification result indicates the possibility of non-invasively classifying CSA and OSA events based on shorter segments of ECG signals.

Resumo Limpo

jectiv obstruct sleep apnea osa caus paus airflow reduc breath effort contrast central sleep apnea csa event accompani breath effort aim studi differenti csa osa event use wavelet packet analysi support vector machin ecg signal s periodmethod eight level wavelet packet analysi perform s clip use daubechi db mother wavelet comparison discret wavelet analysi perform use symlet sym wavelet choic wavelet basi function base grid search use daubechi symlet biorthogon wavelet decomposit level vari support vector machin use twoclass classif overnight polysomnograph studi use train phase patient use independ testingresult propos algorithm shown perform better classifi csa osa wavelet packet featur accuraci sensit specif tradit wavelet decomposit base featur accuraci sensit specif independ test result overal classif accuraci sensit specif respect use wavelet packet analysisconclus classif result indic possibl noninvas classifi csa osa event base shorter segment ecg signal

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