Daily sleep monitoring is necessary to make potential patients with sleep apnea syndrome aware of their respiration stateduring sleep. Although it is desirable to have an unconstrained system for daily monitoring in a home environment, the amplitude ofrespiration measured by an unconstrained sensor varies depending on the participants’ properties and the recumbent position s. In thisstudy, we propose an algorithm for classifying the respiration state by extracting the respiratory cycle in the signal measured by anunconstrained respiration measurement system as a feature. We confirmed that the respiratory cycle obtained using autocorrelation hasdifferent characteristics between the breathing/respiratory arrest periods. By analyzing the respiratory cycle in the biological signal, itwas found that the method was resilient to amplitude changes due to differences in the participants’ properties and in the recumbentpositions. As a result of cross-validation to evaluate the proposed algorithm, the evaluation indices are all high, and it is confirmed thatthe algorithm is resilient to variations in the participants’ properties and in the recumbent positions.
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