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Abstract

The present teachings include analyzing oxygen saturation levels sensed during a hemodialysis treatment for a patient to determine whether the patient has a medical condition based on hypoxemia, apnea, or the like experienced during the treatment. To this end, the present teachings may include the use of a machine-learning algorithm trained to identify a presence of a high-frequency intermittent pattern that would be formed in a plot of the oxygen saturation levels, e.g., to determine a severity of respiratory instability experienced. The present teaching may also or instead include a time-series analysis including at least one of: (i) calculating recurrence-based quantification, such as, but not limited to, recurrence rate, determinism, and laminarity; (ii) calculating the optimal recurrence threshold based on maximum variations of the system's determinism and degree of predictability; and (iii) calculating complexity-based measures such as permutation entropy. Such analyses may be used to detect, inter-alia, sleep apnea syndrome.

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