J Med Syst - Artificial apnea classification with quantitative sleep EEG synchronization.

Tópicos

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Resumo

In the present study, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls. For this purpose, sleep EEG series recorded from patients and healthy volunteers are classified by using several Feed Forward Neural Network (FFNN) architectures with respect to synchronic activities between C3 and C4 recordings. Among the sleep stages, stage2 is considered in tests. The NN approaches are trained with several numbers of neurons and hidden layers. The results show that the degree of central EEG synchronization during night sleep is closely related to sleep disorders like CSA and OSA. The MI and CF give us cooperatively meaningful information to support clinical findings. Those three groups determined with an expert physician can be classified by addressing two hidden layers with very low absolute error where the average area of CF curves ranged form 0 to 10?Hz and the average MI values are assigned as two features. In a future work, these two features can be combined to create an integrated single feature for error free apnea classification.

Resumo Limpo

present studi linear nonlinear eeg synchron method call coher function cf mutual inform mi perform obtain high qualiti signal featur discrimin central sleep apnea csa obstruct sleep apnea osa control purpos sleep eeg seri record patient healthi volunt classifi use sever feed forward neural network ffnn architectur respect synchron activ c c record among sleep stage stage consid test nn approach train sever number neuron hidden layer result show degre central eeg synchron night sleep close relat sleep disord like csa osa mi cf give us cooper meaning inform support clinic find three group determin expert physician can classifi address two hidden layer low absolut error averag area cf curv rang form hz averag mi valu assign two featur futur work two featur can combin creat integr singl featur error free apnea classif

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