Abstract: There was limited information on the analysis of sleep disorder especially apnea. Therefore, study on sleep apnea is essential to understand the sleep behavior during the occurrence of apnea. Markov chain analysis particularly suitable to analyze discrete data that involves time. Data on apnea is discrete as it was measured by recording if the apnea events happens during each epoch of sleep. In this study, the Markov chain model is fitted to the apnea and no apnea data to determine the optimum order of the occurrence of apnea using the Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC) for each subjects of 14 subjects that has apnea problem. The results indicated that the optimum order varies according to sleep stages and period of sleep. Generally, the higher order of Markov chain models is suggested during light and deep sleep stages. However, it is found that regardless of sleep stages the optimum order of Markov chain model is varies during the first, second, third and fourth quarter period of sleep. The analysis for describing the most appropriate order showed that the third order model is suitable for most subjects. In conclusion, the analysis indicates that the third order of the Markov chain model is the most appropriate order regardless of sleep stages and sleep period.