M. Shahbakhti

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Parkinson's disease (PD) was described by James Parkinson first time and it is now recognized as the second common neurological disorder after Alzheimer. Since most of the people with PD suffer form speech disorder, it is believed that speech analysis can be considered as the easiest way for PD detection. In this research, we try to use extracted features(More)
The electroencephalographic signals (EEG) are rather weak and contaminated with different artifacts that have biological and external sources. Among these artifacts, blinks and eye movements are the most common of them. In this paper, we introduce a new method, Empirical Mode Decomposition (EMD), for removal of blink contamination from EEG signal. The(More)
Recording the electrical current of the cortex is called electroencephalography (EEG). EEG signals can be affected by high and low frequency noises which are caused due to muscular activity (EMG), Power line interference, eye blinks and etc. In this paper, we introduce an adaptive wavelet method for elimination of high frequency noises from EEG. The desired(More)
EEG signals collected from cortex are often contaminated with the eye electrical activity, which increase the difficulty in analyzing the EEG and to obtaining clinical information. This paper describes a novel method, adaptive filtering without using an extra channel, for elimination of blink from EEG. The artifact reference is extracted from(More)
Neonatal death can be prevented by early prediction of pre-term labor. During the last decade, uterine electrohysterography (EHG) signal has been considered as a noninvasive method for this aim. There is a wide range of researches which investigated EHG signals for diagnosis of pre-term labor. In this article, features have been extracted by Discrete(More)
EEG signals collected from cortex are often contaminated with the eye electrical activity, which increase the difficulty in analyzing the EEG and to obtaining clinical information. In this paper, two different methods based on Empirical Mode Decomposition, for elimination of blink in EEG is compared. The performance index of this experiment is the(More)
Prediction of preterm labor is of great importance to reduce neonatal death. Analysis of electrohysterogram (EHG) could be considered as a proper tool for this aim. In this paper, the statistical and non-linear features have been extracted from EHG signals and then Support Vector machine (SVM) has been applied for classification between term and preterm(More)
Parkinson's disease (PD) is a neurodegenerative brain disorder that occurs when approximately 60% to 80% of the dopamine-producing cells are damaged. PD is the second common neurodegenerative disorder after Alzheimer. PD could be diagnosed by various signals such as EEG, gait and speech. Approximately, 90 percent of people with PD suffer from speech(More)
Electroencephalograph (EEG) is the measurement of the electrical activity of the brain, obtained by recording from electrodes placed on the cortex. It is often affected with various artifacts that have biological and external sources. Eye electrical activity might be considered as the most common artifact, which increase the difficulty in analyzing the EEG(More)
  • M. Shahbakhti
  • 2013 ISSNIP Biosignals and Biorobotics Conference…
  • 2013
EEG signals collected from cortex are often contaminated with ocular artifacts that might yield to wrong analysis, particularly in BCI systems. This paper describes a novel method, adaptive filtering without using an extra channel, for elimination of blink from EEG. The artifact reference is extracted from blink-contaminated EEG by EMD approach. The(More)