Analysis of rhythm features of EEG for driving fatigue / 生物医学工程学杂志

With extracting separately delta, theta, alpha and beta rhythms of electroencephalogram (EEG), we studied the characters of EEG for fatigued drivers by analyzing relative power spectrum, power spectral entropy and brain electrical activity mapping. The experimental results showed that with the average relative power spectrum in delta and theta rhythms of EEG increasing, the average relative power spectrum in alpha and beta rhythms decreased, while the average relative power spectrum in delta, theta and alpha rhythms increased in deep fatigue. The average power spectral entropy of EEG decreases with the increasing fatigue level. The average relative power spectrum and the average power spectral entropy of EEG could be expected to serve as the index for detecting fatigue level of drivers.

Application of HHT to driving fatigue in EEG analysis / 生物医学工程学杂志

Based on the fact that the signals of electroencephalogram (EEG) possess non-linear and non-stationary properties, Hilbert-Huang Transform (HHT) was proposed for the EEG analysis of driving fatigue. Firstly, C4-lead EEG was selected, and the data of normal driving state and fatigue driving state was analyzed by HHT to explore the differences. Then O2-lead EEG was chosen for contrastive analysis of differences between the different leads. It was found through the analysis that the EEG signals had different Hilbert marginal spectrums for different states, and there were also some differences at the same state for the two leads. It can be certain that HHT can well distinguish different states of drivers as a novel approach for driving fatigue detection, and the selected lead may affect detectable results to some extent.

A new method of tremor diagnosis based on singular value decomposition of EMD / 生物医学工程学杂志

Aiming at three kinds of tremor, including essential tremor (ET), Parkinsonian disease (PD) tremor and physiological tremor (PT), which are subjected to frequent clinical misdiagnosis, a new method based on singular value decomposition (SVD) of empirical mode decomposition (EMD) and support vector machine (SVM) for the recognition of tremor is proposed in this paper. First, the hand acceleration signals of three different types of 40 tremor voluntary subjects were collected on the basis of informed consent, and the EMD method was used to decompose the signals into a number of stationary intrinsic mode functions (IMFs). Then the preceding four IMFs which could describe signals were selected, and the initial feature vector matrixes were formed. After the application of SVD technique to the initial feature vector matrixes, the singular values were obtained and used as the feature vectors of tremor types to be put in the support vector machine classifier as well as in the identification of tremor types. The results of experiment have shown that the proposed diagnosis method based on SVD of EMD and SVM can extract tremor features effectively and identify tremor types accurately. It also provides a new assistant approach for clinical diagnosis of tremor.

The current situation and development of tremor signals analysis / 生物医学工程学杂志

Tremor, a rhythmic and involuntary oscillatory movement of one or several body parts, is the movement resulting from the abnormal synchronization of motor neural units. Detecting and analyzing the ACC, EMG and EEG signals of tremor patients by signal processing methods are very important for clinical diagnosis, rating evaluation and detection of incipient illness. This paper introduces the applications of time domain,frequency domain, artificial neural network, high order accumulation, approximate entropy, fuzzy, chaos, discriminant analysis in the researches of tremor signals, and finally points out the application foreground of researches on tremor signals.