Identification of arterial oxygen intermittency in oximetry data

Abstract

AbstractIn patients with kidney failure treated by hemodialysis, intradialytic arterial oxygen saturation (SaO2) time series present intermittent high-frequency high-amplitude oximetry patterns (IHHOP), which correlate with observed sleep-associated breathing disturbances. A new method for identifying such intermittent patterns is proposed. The method is based on the analysis of recurrence in the time series through the quantification of an optimal recurrence threshold ($${{\varvec{\epsilon}}}_{\mathbf{o}\mathbf{p}\mathbf{t}}$$ ϵ o p t ). New time series for the value of $${{\varvec{\epsilon}}}_{\mathbf{o}\mathbf{p}\mathbf{t}}$$ ϵ o p t were constructed using a rolling window scheme, which allowed for real-time identification of the occurrence of IHHOPs. The results for the optimal recurrence threshold were confronted with standard metrics used in studies of obstructive sleep apnea, namely the oxygen desaturation index (ODI) and oxygen desaturation density (ODD). A high correlation between $${{\varvec{\epsilon}}}_{\mathbf{o}\mathbf{p}\mathbf{t}}$$ ϵ o p t and the ODD was observed. Using the value of the ODI as a surrogate to the apnea–hypopnea index (AHI), it was shown that the value of $${{\varvec{\epsilon}}}_{\mathbf{o}\mathbf{p}\mathbf{t}}$$ ϵ o p t distinguishes occurrences of sleep apnea with great accuracy. When subjected to binary classifiers, this newly proposed metric has great power for predicting the occurrences of sleep apnea-related events, as can be seen by the larger than 0.90 AUC observed in the ROC curve. Therefore, the optimal threshold $${{\varvec{\epsilon}}}_{\mathbf{o}\mathbf{p}\mathbf{t}}$$ ϵ o p t from recurrence analysis can be used as a metric to quantify the occurrence of abnormal behaviors in the arterial oxygen saturation time series.