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0 events/hour and an intraclass correlation coefficient of 0.985. The neural network approach for automatic scoring of respiratory events achieved high accuracy and good agreement with manual scoring. The presented neural network could be used for improving the efficiency of sleep apnea diagnostics or for analysis of large research datasets that are unfeasible to score manually. In addition, since the neural network scores individual respiratory events, the automatic scoring can be easily reviewed manually if desired.The rapidly increas
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