Biomedical subjects

Zhizhong Wang

Publications and source records attributed to Zhizhong Wang.

14 recordsLinked to original sources

PubMed2006

Classification of surface EMG signals using harmonic wavelet packet transform.

In this paper, an efficient method based on the discrete harmonic wavelet packet transform (DHWPT) is presented to classify surface electromyographic (SEMG) signals. After the relative energy of SEMG signals in each frequency band had been extracted by the DHWPT, a genetic algorithm was utilized to select appropriate features in order to reduce the feature dimensionality. Then, the selected features were used as the input vectors to a neural network classifier to discriminate four types of prosthesis movements. Compared with other classification methods, the proposed method provided high classification accuracy in experimental research. In addition, this method could also save a lot of computational time because the DHWPT has a fast algorithm based on the fast Fourier transform for numerical implementation.

Animals↗

PubMed2006

Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.

In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

Electromyography↗

PubMed2006

Multifractal analysis of ventricular fibrillation and ventricular tachycardia.

A study of ventricular fibrillation and ventricular tachycardia was undertaken using multifractal analysis. By applying the method of direct determination of the f(alpha) singularity spectrum, the value of the area of the VF and VT singularity spectrum was calculated. The comparison between the results showed that the value of the area of the VF singularity spectrum tended to be larger than that of the value of the area of the VT singularity spectrum. This makes the multifractal singularity spectrum a powerful criterion for discriminating between VF and VT.

Algorithms↗

PubMed2006

Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials.

We have studied methods for noise reduction of myoelectric signals and for extraction of motor unit action potentials from these signals. Effective MUAP peak detection is the first important step in EMG decomposition. We first combined independent component analysis and wavelet filtering to remove power line interference, and then applied a wavelet filtering method and threshold estimation calculated using wavelet transform to suppress background noise and Gaussian white noise. The technique was applied to single-channel, short-period real myoelectric signals from normal subjects and to artificially generated EMG recordings. In contrast to existing methods based on amplitude single-threshold filtering of the original myoelectric signal or a conventional digitally filtered signal, our technique is fast and robust. Moreover, the proposed algorithm is substantially automatic. The performance has been evaluated with a set of synthetic and experimentally recorded myoelectric signals. The basic tool for testing was power spectrum density (PSD) estimation by the Welch method, which allowed us to analyze the PSD of nonstationary signals.

Action Potentials↗

PubMed2006

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition.

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short perioda9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.

Action Potentials↗

PubMed2006

Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis.

The mean frequency (MNF) of surface electromyography (EMG) signal is an important index of local muscle fatigue. The purpose of this study is to improve the mean frequency (MNF) estimation. Three methods to estimate the MNF of non-stationary EMG are compared. A novel approach based on Hilbert-Huang transform (HHT), which comprises the empirical mode decomposition (EMD) and Hilbert transform, is proposed to estimate the mean frequency of non-stationary signal. The performance of this method is compared with the two existing methods, i.e. autoregressive (AR) spectrum estimation and wavelet transform method. It is observed that our method shows low variability in terms of robustness to the length of the analysis window. The time-varying characteristic of the proposed approach also enables us to accommodate other non-stationary biomedical data analysis.

Adult↗

PubMed2006

[Classification of surface EMG signal based on wavelet transform with nonlinear scale].

Surface EMG (sEMG) signal is a complex nonlinear, non-stationary signal. In this paper, wavelet transform with nonlinear scale (NWT) is introduced. Due to the gradual shortening of its time-resolution, NWT is good at extracting the precise time-frequency information from sEMG signal. First, every sEMG signal (30 sets are for forearm supination and 30 sets are for forearm pronation) is transformed into intensity distribution (time-frequency distribution) by NWT. And then the feature vector is determined from the characteristic roots which are obtained from the intensity distribution by principle component analysis. At last, the two patterns of sEMG signals are identified by BP neural network. The results show that the accurate classification rate is higher gained by NWT than by two conventional time-frequency distributions. At the same time, the calculating complexity of neural network is decreased greatly.

Electromyography↗

PubMed2005

Application of vinyl tris(trimethylsilyl)germanes in Pd-catalyzed couplings.

The oxidative treatment of vinyltris(trimethylsilyl)germanes with hydrogen peroxide (NaOH/H(2)O/THF) or tert-butyl peroxide (KH/THF) generates reactive germanol or germanoxane species that undergo Pd-catalyzed cross-couplings with aryl and alkenyl halides and aryl triflates in the presence of Pd(PPh(3))(4). Vinylgermanes having either a conjugated or isolated double bond serve as versatile transmetalation reagents. The E-germanes undergo coupling with retention of stereochemistry under aqueous and anhydrous conditions, while coupling of Z-germanes occurs with less stereoselectivity to produce a mixture of E/Z products.

Catalysis↗

PubMed2005

Classification of surface EMG signal using relative wavelet packet energy.

Features can be classified into interferential features and discriminable features according to their contribution to pattern recognition. In this paper, a novel and simple method based on wavelet packet transform is proposed to extract the features from surface EMG signal. In this method, the features are relative wavelet packet energy (RWPE), which is evaluated from several selected frequency bands of surface EMG signal. Compared with a conventional method, which is of the best performance in previous applications, the method can compress the interferential features and enhance the discriminable features more effectively. In consequence, the RWPE features calculated by the method represent different patterns of surface EMG signal more accurately and the accuracy of surface EMG signal pattern classification is improved greatly.

Electromyography↗

PubMed2005

[Effect of expression of p53 in squamous cell carcinoma of larynx and mucosa adjacent to tumor on the biological behavior].

OBJECTIVE: To investigate the expression of p53 in squamous cell carcinoma of larynx (LSCC) and mucosa adjacent to LSCC and study the correlation between it and the biological behavior with retrospective analysis. METHOD: p53 protein overexpression was detected in 76 squamous cell carcinoma and the tissue beside the LSCC with SP method of Immuno-histochemical staining [IHC]. RESULT: p53 overexpression was observed in 47.4% of LSCC, 40.0% of carcinoma in situ, 22.2% of atypical hyperplasia (AtH), 0% of normal mucosa. p53 overexpression in parenchyma of LSCC as well as in the corresponding carcinoma in situ is unaminous completely. There was a significant difference between p53 positive staining in AtH and in carcinoma of larynx as well as carcinoma in situ, but p53 overexpression in AtH adjacent to the laryngeal carcinoma is apparently correlated with that in the corresponding parenchyma of carcinoma of larynx (P < 0.01). No correlation was found between p53 over expression and sex, age, TNM stage, location and T stage, but there was significant correlation between p53 over expression and differentiation grading (G1-->G2 + G3). It was noted that of the rate p53 overexpression was 68.8% in lymphatic metastasis group, which was higher than the percentage (41.7%) found in lymphatic nonmetastasis group (P > 0.05). CONCLUSION: p53 overexpression are present in LSCC of different stages and AtH with significant correlation. p53 may play an important role in an early stage of malignant transformation of a subset of LSCC. Multiple-step carcinogenesis and malignancy as well as prognosis may be in association with p53-related tumorigenesis.

Adult↗

PubMed2004

Radical-mediated silyl- and germyldesulfonylation of vinyl and (alpha-fluoro)vinyl sulfones: application of tris(trimethylsilyl)silanes and tris(trimethylsilyl)germanes in Pd-catalyzed couplings.

[reaction: see text] Radical-mediated silyl- and germyldesulfonylations of various vinyl and (alpha-fluoro)vinyl sulfones with tris(trimethylsilyl)silane and germanium hydrides provide access to vinyl and (alpha-fluoro)vinyl silanes and germanes. Upon oxidative treatment with hydrogen peroxide in basic aqueous solution, the vinyl tris(trimethylsilyl)silanes and -germanes undergo Pd-catalyzed cross-couplings with aryl halides.

Catalysis↗

PubMed2004

Mechanism of linear and nonlinear optical effects of chalcopyrite AgGaX2 (X=S, Se, and Te) crystals.

The electronic band structures for AgGaX(2) (X=S, Se, Te) chalcopyrites have been calculated using a pseudopotential total energy method. First-principles calculations of the linear and nonlinear optical properties are presented for these crystals, with the electronic band structures obtained from pseudopotential method as input. The theoretical refractive indices and nonlinear optical coefficients are in good agreement with available experimental values. The origin of the nonlinear optical effects is explained through real-space atom-cutting analysis. The contribution of the GaX(2) group (X=S, Se, Te) for second harmonic generation (SHG) effect is dominant while that of the cation Ag is negligible. In addition, the percentage contribution to the SHG coefficients from the different bonds increase with increase of the bond order.

Journal Article↗

PubMed2004

[Using AR model to analyze injured nerve with needle EMG signal].

The two main factors to affect the style of the recruitment are temporal recruitment and spatial recruitment. This study sought a new way to analyze the recruitment with the modern spectrum method. The abnormal spatial recruitment and temporal recruitment of varied injury degrees of intramuscular neuron were compared through the AR model. At last, AR coefficients were extracted and passed through BP artificial neuron network to classify different NEMG signals and good result was gained.

Action Potentials↗

PubMed2004

[Surface electromyography signal classification using gray system theory].

A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.

Electromyography↗