Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 3 3-4 2019 06 12 90 94 EN Nahid Nikoee Department of Management, School of Humanities, Electronic Branch, Islamic Azad University, Tehran, Iran Mohammad Seyed Hoseini Department of Industrial Engineering, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Arezu Najafi Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran. Amin Amali Otorhinolaryngology Research Center, Tehran University of Medical Sciences, Tehran, Iran; Occupational Sleep Research Center, Tehran University of Medical Sciences, Tehran, Iran AND Department of Otorhinolaryngology, Imam Khomeini Educational Complex Hospital, Valiasr Hospital, Tehran University of Medical Sciences, Tehran, Iran Behrouz Amirzargar Otorhinolaryngology Research Center AND Occupational Sleep Research Center AND Department of Otorhinolaryngology, Imam Khomeini Educational Complex Hospital, Valiasr Hospital, Tehran University of Medical Sciences, Tehran, Iran Reihaneh Heidari Otorhinolaryngology Research Center, Tehran University of Medical Sciences, Tehran, Iran; Occupational Sleep Research Center, Tehran University of Medical Sciences, Tehran, Iran AND Department of Otorhinolaryngology, Imam Khomeini Educational Complex Hospital, Valiasr Hospital, Tehran University of Medical Sciences, Tehran, Iran 2018 03 13 2019 02 23 Background and Objective: Polysomnography (PSG) is the gold standard for diagnosis of sleep disorders. Several software programs are available to analyze sleep tests according to available guidelines and decrease the time and cost of PSG analysis. This study aimed to compare the parameters of automated analyzer software with analysis of trained technician (manual analysis). Materials and Methods: Twenty patients who underwent full-night PSG were randomly selected. A sleep technologist who was blind to the study, scored sleep stages and respiratory events according to recommended criteria of American Academy of Sleep Medicine (AASM) 2013, then an auto analysis was done using N-7000 amplifier. Results of auto analysis and manual analysis were compared. Descriptive statistics and paired t-test were used for data analysis. Results: Total sleep time (TST) and sleep efficiency (SE) calculated by auto analysis was significantly more than manual analysis (511.82 ± 35.34 vs. 396.85 ± 75.97 for TST and 95.47 ± 3.74 vs. 74.14 ± 35.34 for SE, respectively). Furthermore, there was no concordance for sum of apneas and hypopneas during TST. However, calculated number of hypopneas in non-rapid eye movement (NREM) stage in auto analysis and manual analysis was quite similar. The least precision was observed in scoring of stages 3 and REM for auto analysis scoring and the most similarity for scoring of stage N2. Conclusion: Detecting hypopneas in NREM stage by auto analysis maybe the reliable parameter that could help the technicians during analysis of sleep test. There is a need for more advanced automated algorithms. Furthermore, manual analysis is superior to automated one in PSG analysis according to the current results. https://jss.tums.ac.ir/index.php/jss/article/view/104