Jiarui Lin

The effect of grey levels on mutual information based medical image registration

Medical image registration is an active research field in medical image processing in the last two decades. One of the surveys of medical image registration is voxel-based method, which is generally considered as very accurate, almost fully automatic. The most efficient voxel-based method is maximization of mutual information (MI). In This work, we will discuss the effects of rescaling intensity values of an image on MI method, and then compare the registration results and computation time of the same image pairs with different grey levels. We implemented series rigid registration of CT and MR image, in each registration, the intensity values of CT and MR images are linearly reseated to the same range. We adopt 5 different levels in our experiments, i.e. 256, 128. 64, 32 and 16 grey levels to exploit the affection of grey levels on MI based registration method. We also count the time cost on the calculation of single MI value and the whole registration procedure. Finally we reach this conclusion that the results become worse with the reduction of grey levels of the image, and rescaling the intensity values of the original images into [0,63] is an excellent tradeoff in brain registration by mutual information.

Designing a dual page virtual keyboard for mental speller

Exploiting the novel human-machine interaction paradigm which is called brain-machine interface, we are dedicated to constructing a so called imitating-natural-reading visual evoked potential based mental speller for those with neuromuscular disorders and motor disabilities. These techniques also have potential applications in occasions such as navigating, diving and battlefield, etc. Users input a character to computer by gazing at the respective key of virtual keyboard on the screen. In order to realize the goal, we designed a virtual keyboard with double pages based on the past work. Analyzing the speed of communication of this paradigm reflect that it can boost up the bit rate significantly.

N2 components as features for brain computer interface

A mental speller using brain computer interface (BCI) may allow a user to communicate by gazing at a virtual keyboard on the screen to select a desired character to compose a word, and thus sentences. Different from other paradigms, a so called imitating-natural-reading (INR) modality was exploited to construct a novel mental speller-INR SPELLER. In order to boost the bit rate, a 300ms window was used to estimate the accurate time of target stimuli onset from EEG signals. To meet this task, N2 components of visual evoked potentials (VEP) were investigated. Experimental results indicated that the object-specified component can be estimated in single trial at an accuracy of 90.5% with support vector machine (SVM) classifier.

QRS classification using adaptive Hermite decomposition and radial basis function network

The accurate QRS classification is essential for enhancing the performance of automated ECG interpretation system. The correct selection, of features is very important to minimize the dimension of the feature space and maximize the distance between different classes of QRS. In this study, the QRS complexes are decomposed on a set of Hermite functions and the coefficients attained are served as the features. The Hermite functions are orthonormal and have strong morphological representation ability for QRS complexes; thus each feature extracted has independent information and the signal can be represented with a low number of coefficient. An Adaptive Hermite Decomposition (AHD) method is employed to perform the decomposition. Then, a Radial Basis Function Network (RBFN) is used to classify normal and PVC patterns in the features space. Because the patterns are relatively concentrated, the choice of RBFN is reasonable. The results show that the RBFN can provide a satisfactory classification with a few training epochs. Based on MIT/BIH database annotations, 600 pairs of normal and abnormal QRS complexes (PVC beats) were extracted from 12 files to train and test the classifier developed using AHD and RBFN.

Research on gastric multiparameter sensors technology

This paper introduce« gastric multiparameter sensors basic theory, and men gives several results of sensors property detection. The results show that these sensors can give a good base of signal detection for the research.

Effects of Nano Red Elemental Selenium on Sodium Currents in Rat Dorsal Root Ganglion Neurons

Nano red elemental selenium (Nano-Se), was demonstrated to be useful in medical and scientific researches. Here, we investigated the effects of Nano-Se on sodium currents on rat dorsal root ganglion neurons (DRG), using the whole-cell patch clamp method. Nano-Se reversibly decrease the INa(TTX-S) in a concentration-dependent, time-dependent and open-channel block manners without affecting INa(TTX-R). It shifted the steady-state activation and inactivation curves for INa to more negative potentials. In the research of recovery from inactivation, the recovery time constant is longer in the present of Nano-Se. Nano-Se had a weaker inhibitory effect on INa, compared with marked decrease caused by selenite which indicated that Nano-Se is less neurotoxic than selenite in short-term/large dose treatments and had similar bio availability to sodium selenite. The results of interaction between the effects of Nano-Se and selenite on sodium currents indicated a negative allosteric interaction between the selenite binding site and the Nano-Se binding site or that they have the same competitive binding site

Single-Trial Estimation of Imitating-Natural-Reading Evoked Potentials in Single-Channel

Using imitating-natural-reading induced potentials as communication carriers, we are constructing a brain-computer interface based mental speller which enable users to interaction with computers. The potentials were induced in this way: In a trial, strings consisted of target and non-target symbols were moving smoothly from right to left through a little visual window at the center of computer screen. Subject was instructed to stare at the visual window to count the target, and thus potentials were evoked. In practical applications, fewer electroencephalograph recording channels are preferred. We explored the single-trial estimating of event-related potentials recorded in single-channel using support vector machines in three subjects. With carefully feature selections, we obtained satisfying results of correct classification rate, which is 92.1%, 94.1% and 91.5%, respectively. The results demonstrated the advantages of the inducing paradigm used in our experiments

A method of spectra-analysis for abnormal voice signals

A microprocessor-based voice-spectra analysis system is presented. The voice signals of some patients with laryngeal disease have been analyzed. The results showed that the voices of patients with different diseases have distinct acoustic characteristics. This method can recognize the acoustic characteristics of abnormal voices and help doctors and medical experts to research the physiology and pathology of the voice, diagnose laryngeal diseases, and monitor laryngeal state before and after operations. The waveforms of the voice signals and the analysis results are given.<<ETX>>

Research of gastro-oesophageal disease aided diagnose expert system

Describes a method for the development of a gastro-oesophageal disease diagnostic expert system. Results obtained from gastric multiparameter monitoring are the important way of knowledge acquisition in this system. Medical knowledge is also included. For different kinds of knowledge, different kinds of knowledge expression and methods of acquisition are used. Three kinds of inferring methods are then used to obtain the conclusion. The test results show that the expert system will help to diagnose digestive diseases, and it also provides a new method for clinical and basic medical research.

Correlation character of ionic current fluctuations in cell membrane ion channels

The gating of ion channels has widely been modeled by assuming the transition between open and closed states is a memoryless process. Nevertheless, the statistical analysis of an ionic current signal recorded from voltage dependence K+ single channel is presented. Calculating the sample autocorrelation function of the ionic current based on the digitized signals, rather than the sequence of open and closed states durations time. The result is shown existence memory. For difference voltage, the ion channel current fluctuation has difference correlation attributions. The result suggests the correlation character of the ionic current fluctuations. Also the possible biological implication of the findings have been discussed