Author/Authors :
Sabeti, Malihe Department of Computer Engineering - Islamic Azad University North Tehran Branch, Tehran, Iran , Moradi, Ehsan Department of Neurosurgery - Shahid Beheshti University of Medical Sciences, Tehran, Iran , Taghavi, Mahsa Medical Faculty - Islamic Azad University Kazeroon Branch, Kazeroon, Iran , Mohammadi, Mokhtar Department of Information Technology - College of Engineering and Computer Science - Lebanese French University, Kurdistan Region, Iraq , Boostani, Reza CSE & IT Department - Faculty of Electrical and Computer Engineering - Shiraz University, Shiraz, Iran
Abstract :
Background: Psychiatrists diagnose schizophrenia based on clinical symptoms such as disordered thinking, delusions, hallucinations, and severe distortion of daily functions. However, some of these symptoms are common with other mental illnesses such as bipolar mood disorder. Therefore, quantitative assessment of schizophrenia by analyzing a physiological-based data such as the electroencephalogram (EEG) signal is of interest. In this study, we analyze the spectrum and time-frequency distribution (TFD) of EEG signals to understand how schizophrenia affects these signals.
Methods: In this regard, EEG signals of 20 patients with schizophrenia and 20 age-matched participants (control group) were investigated. Several features including spectral flux, spectral flatness, spectral entropy, time-frequency (TF)-flux, TF-flatness, and TF-entropy were extracted from the EEG signals.
Results: Spectral flux (1.5388 ± 0.0038 and 1.5497 ± 0.0058 for the control and case groups, respectively, P = 0.0000), spectral entropy (0.8526 ± 0.0386 and 0.9018 ± 0.0428 for the control and case groups, respectively, P = 0.0004), spectral roll-off (0.3896 ± 0.0434 and 0.4245 ± 0.0410 for the control and case groups, respectively, P = 0.0129), spectral flatness (0.1401 ± 0.0063 and 0.1467 ± 0.0077 for the control and case groups, respectively, P = 0.0055), TF-flux (1.2675 ± 0.1806 and 1.5284 ± 0.2057 for the control and case groups, respectively, P = 0.0001) and TF-flatness (0.9980 ± 0.0000 and 0.9981 ± 0.0000 for the control and case groups, respectively, P = 0.0000) values in patients with schizophrenia were significantly greater than the control group in most EEG channels. This prominent irregularity may be caused by decreasing the synchronization of neurons in the frontal lobe.
Conclusion: Spectral and time frequency distribution analysis of EEG signals can be used as quantitative indexes for neurodynamic investigation in schizophrenia.
