Mousa Shamsi

Work place: Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran



Research Interests: Engineering, Computational Engineering


Mousa Shamsi was born in Tabriz, Iran, in 1972. He received his B.Sc. degree in Electrical Engineering (major: electronic) from Tabriz University, in 1995. In 1996, he joined the University of Tehran, Tehran, Iran. He received his M.Sc. degree in Electrical Engineering (major: Biomedical Engineering) from this university in 1999. From 1999 to 2002, he taught as a lecturer at the Sahand University of Technology, Tabriz, Iran. From 2002 to 2008, he was a PhD student at the University of Tehran in Bioelectrical Engineering. In 2006, he was granted with the Iranian government scholarship as a visiting researcher at the Ryukyus University, Okinawa, Japan. From December 2006 to May 2008, he was a visiting researcher at this University. Since 2008, he has been a faculty member at Sahand university of technology in Tabriz, Iran, From summer 2013, he is an Associate professor at the Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Author Articles
Modeling Epileptic EEG Time Series by State Space Model and Kalman Filtering Algorithm

By Atefeh Goshvarpour Ateke Goshvarpour Mousa Shamsi

DOI:, Pub. Date: 8 Feb. 2014

The human brain is one of the most complex physiological systems. Therefore, electroencephalogram (EEG) signal modeling is important to achieve a better understanding of the physical mechanisms generating these signals. The aim of this study is to investigate the application of Kalman filter and the state space model for estimation of electroencephalogram signals in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) were analyzed. The estimation performance of the proposed method on EEG signals is evaluated using the root mean square (RMS) measurement. The result of the present study shows that this model is appropriate for the analysis of EEG recordings. In fact, this model is capable of predicting changes in EEG time series with phenomena such as epileptic spikes and seizures.

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Spectral and Time Based Assessment of Meditative Heart Rate Signals

By Ateke Goshvarpour Mousa Shamsi Atefeh Goshvarpour

DOI:, Pub. Date: 8 Apr. 2013

The objective of this article was to study the effects of Chi meditation on heart rate variability (HRV). For this purpose, the statistical and spectral measures of HRV from the RR intervals were analyzed. In addition, it is concerned with finding adequate Auto-Regressive Moving Average (ARMA) model orders for spectral analysis of the time series formed from RR intervals. Therefore, Akaike's Final Prediction Error (FPE) was taken as the base for choosing the model order. The results showed that overall the model order chosen most frequently for FPE was p = 8 for before meditation and p = 5 for during meditation. The results suggested that variety of orders in HRV models upon different psychological states could be due to some differences in intrinsic properties of the system.

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Other Articles