Sleep Organisation in Depression and Schizophrenia: Index of Endogenous Periodicity of Sleep as a State Marker [Retracted]
DOI:
https://doi.org/10.3889/oamjms.2013.014Keywords:
Sleep, Polysomnography, Schizophrenia, Depression, Biological Markers.Abstract
Background: Sleep disorders are frequent symptoms described in psychiatric patients with major depression or schizophrenia. These patients also exhibit changes in the sleep architecture measured by polysomnography (PSG) during sleep. The aim of the present study was to identify potential biomarkers that would facilitate the diagnosis based on polysomnography (PSG) measurements.
Subjects and Methods: 30 patients with schizophrenia, 30 patients with major depression and 30 healthy control subjects were investigated in the present study. The mean age in the group with schizophrenia was 36.73 (SD 6.43), in the group of patients with depression 40.77 (SD 7.66), in the healthy controls group 34.40 (SD 5.70). The gender distribution was as follows: 18 male, 12 female in the group with schizophrenia; in the group of patients with depression 11 male, 19 female; in the control group 16 male and 14 female. All subjects underwent polysomnography (PSG) for a minimum time of 8 hours according to the criteria of Rechtschaffen & Kales (1968). The following polysomnographic (PSG) parameters were analyzed: sleep latency (SL), total sleep time (TST), waking time after sleep onset (WTASO), number of awakenings (NAW), slow wave sleep (SWS), rapid eye movement sleep (REM), rapid eye movement sleep latency (REML), first REM period (REM 1), and first NREM period (NREM 1). We tested the potential of multiple sleep variables to predict diagnosis in different groups by using linear discriminate analysis (LDA).
Results: There were significant differences in polysomnography (PSG) variables between healthy control subjects and psychiatric patients (total sleep time, sleep latency, number of awakenings, time of awakening after sleep onset, REM 1 latency, REM 1 and index of endogenous periodicity). Importantly, LDA was able to predict the correct diagnosis in 88% of all cases.
Conclusions: The presented analysis showed commonalities and differences in polysomnography (PSG) changes in patients with major depressive disorder and in patients with schizophrenia. Our results underline the potential of polysomnography (PSG) measurements to facilitate diagnostic processes.Downloads
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