Qiwei Shi

EEG data analysis based on EMD for coma and quasi-brain-death patients

Electroencephalography (EEG) is widely used in evaluating the absence of cerebral cortex function for the determination of brain death. Since EEG recorded signal is always corrupted by some artefacts and various interfering noise, extracting active or nonactive features from noisy EEG signals and evaluating their significance is therefore crucial in the process of brain death diagnosis. This article presents an EEG-based preliminary examination system associated with empirical mode decomposition (EMD) technique to extract informative brain activity features from real-world recorded clinical EEG data. Moreover, the power spectrum technique is applied to evaluate the significant differences between the group of comatose patients and the group of quasi-brain-deaths. Our experimental results show effectiveness and some promising directions of applying the EMD method to the clinical EEG analysis.

Brain-computer interface system using approximate entropy and EMD techniques

Brain-computer interface (BCI) is a technology which would enable us to communicate with external world via brain activities. The electroencephalography (EEG) now is one of the non-invasive approaches and has been widely studied for the brain computer interface. In this paper, we present a motor imaginary based BCI system. The subjects EEG data recorded during left and right wrist motor imagery is used as the input signal of BCI system. It is known that motor imagery attenuates EEG μ and β rhythms over contralateral sensorimotor cortices. Through offline analysis of the collected data, a approximate entropy (ApEn) based complexity measure is first applied to analyze the complexity between two channels located in different hemispheres. Then, empirical mode decomposition (EMD) is used to extract informative brain activity features to discriminate left and right wrist motor imagery tasks. The satisfactory results we obtained suggest that the proposed method has the potential for the classification of mental tasks in brain-computer interface system.

Analyzing EEG of Quasi-brain-death Based on Approximate Entropy Measures

Significant characteristic differences exist between the group of comatose patients and brain deaths. Statistical analysis methods have advantages in analyzing recorded EEG signals. In this paper, we apply a method based on approximate entropy (ApEn) associated with dynamic complexity to analyze a total of 35 patients. The experimental results illustrate effectiveness of the proposed method in measuring the EEG data and well performance in evaluating the differences between comatose patients and quasi-brain-deaths.

Dynamic Extension of Approximate Entropy Measure for Brain-Death EEG

In this paper, we propose a Electroencephalography (EEG) signal processing method for the purpose of supporting the clinical diagnosis of brain death. Approximate entropy (ApEn), as a complexity-based method appears to have potential application to physiological and clinical time-series data. Therefore, we present a ApEn based statistical measure for brain-death EEG analysis. Measure crossing all channels extends along the time-coordinate of EEG signal to observe the variation of the dynamic complexity. However, it is found that high frequency noise such as electronic interference from the surrounding containing in the real-life recorded EEG lead to inconsistent ApEn result. To solve this problem, in our method, a processing approach of EEG signal denoising is proposed by using empirical mode decomposition (EMD). Thus, high frequency interference component can be discarded from the noisy period along the time-coordinate of EEG signals. The experimental results demonstrate the effectiveness of proposed method and the accuracy of this dynamic complexity measure is well improved.

Blind testing of quasi brain deaths based on analysis of EEG energy

This paper presents a power spectral pattern analysis method for quasi-brain-death EEG based on Empirical Mode Decomposition (EMD) under the condition of unknowing the clinical symptoms of patients. EMD method is a time-frequency analysis method for analyzing the nonlinear and non-stationary data. In this paper,we decompose a single-channel recorded EEG data into a number of components with different frequencies, we calculate the power spectral or energy of the decomposed components in a suitable frequency band. Based on the EEG power spectral analysis, the patients are classified into two categories: existence of the brain activities or absence of the brain activities. The experimental results illustrate the effectiveness of our proposed method.

EMD based power spectral pattern analysis for quasi-brain-death EEG

Evaluating the significance differences between the group of comatose patients and the group of brain death is important in the determination of brain death. This paper presents the power spectral pattern analysis for Quasi-Brain-Death EEG based on Empirical Mode Decomposition (EMD). We first decompose a single-channel recorded EEG data into a number of components with different frequencies. We then focus on the components which are related to the brain activities. Since the power of spontaneous activities in the brain is usually higher than that of non-activity components. Therefore, we can evaluate the power spectral patterns between comatose patients and quasi-brain-deaths. Our experimental results illustrate the effectiveness of proposed method.

Blind Experiment Based on Dynamic Approximate Entropy Measure for Quasi-brain-death EEG

Electroencephalography (EEG) based preliminary examination has been proposed in the clinical brain death determination. In the EEG signal analysis process, Approximate Entropy (ApEn) as a complexity based method appears to have potential for the application to physiological and clinical time-series data. In our previous studies, in the condition of knowing about the clinical state of patients from the standard process of brain-death diagnosis, the same results could be obtained by using our developed ApEn measure for quasi-brain-death EEG. In this paper, we present this dynamic complexity measure based blind experiment without knowing about the clinical symptoms of patients beforehand. Features obtained from three typical cases indicate one patient being in coma and the other changing from com to quasi-brain-death. Being identical to further clinical diagnose, results of thirteen cases from nine patients illustrate the effectiveness of our proposed method and implies its significance of a reference for brain death diagnosis in clinical practice.

Blind testing based on ICA for quasi-brain-death EEG

Electroencephalography (EEG) signal analysis and EEG-based preliminary examination has been proposed in the clinical brain death determination. In our previous studies, with knowing the clinical diagnosis of the patient, the result that obtained by using independent component analysis (ICA) for quasi-brain-death EEG is the same with that from the standard clinical procedure. In this paper, we present a blind analysis process based on ICA in the condition of unknowing the clinical symptoms of patients. Significant feature differences between three cases indicate one patient being in coma and the another changing to quasi-brain-death. Being identical to further clinical diagnose, the result illustrates the effectiveness of our time-saving method and implies its value as a reference for brain death diagnosis in clinical practice.

EMPIRICAL MODE DECOMPOSITION METHOD FOR MEG PHANTOM DATA ANALYSIS

Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from real-world measured data and represent them corresponding to the human brain functions. This usually depends on how to reduce a high level noise from the measurement. In this paper, a novel multistage MEG data analysis method based on the empirical mode decomposition (EMD) and independent component analysis (ICA) approaches is proposed for the feature extraction. Moreover, EMD and ICA algorithms are investigated for analyzing the MEG single-trial data which is recorded from the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in high level noise reduction by EMD associated with ICA approach and source localization by equivalent current dipole fitting method.