Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.
Abstract This experiment was to study the feasibility from direct observation of muscle contraction of the lower extremity fromelectrical stimulation threshold of nerve fascicle in identifying the Iα intrafusal afferent fibers during selective posterior rhizotomy (SPR) and to investigate the clinical relationship between the muscle spasm and the electrical stimulation of nerve fascicles. The electrical stimulation threshold of all nerve fascicles in 36 cases during SPR were analysed statistically. The results showed that there was a significant difference between the electrical stimulation threshold of the severed nerve fascicles and intact nerve fascicles no matter the nerve root or each posterior nerve rootlet was examined. It was simple and reliable for surgeons to identify correctly the Iα intrafusal afferent fibers intraoperatively from direct observation of the electrical stimulation threshold of nerve fascicle.
The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the ‘clean’ EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of L1-norm penalty term to avoid over fitting problem. Then, it is solved by the fast iterative shrinkage-thresholding algorithm (FISTA), which updates the variables through a shrinkage threshold operation in each iteration to converge to the global optimal solution. The experimental results show that compared with other methods, this method can improve the accuracy of noise reduction and reconstruction of brain functional network to more than 98%, effectively suppress the noise, and help to better explore the function of human brain in noisy environment.
Calculation of cardiac hemodynamic parameters is based on accurate detection of feature points in impedance cardiogram. According to these parameters, doctors can determine heart conditions, so it is very important to accurately detect the feature point of impedance differential signals. This article presents a process in which we used wavelet threshold method to de-noise signals, and then detected the feature points after six layers wavelet decomposition by using bior3.7. The experimental data were collected from healthy persons in our laboratory and twenty two clinical patients in Chongqing Daping Hospital by using KF_ICG instrument. The results indicated that this method could precisely detect feature points whether it was from healthy people or clinical patients. This helps to achieve the application of noninvasive detection cardiac hemodynamic parameters in clinical treatments by using impedance method.
At present, fatigue state monitoring of upper limb movement generally relies solely on surface electromyographic signal (sEMG) to identify and classify fatigue, resulting in unstable results and certain limitations. This paper introduces the sEMG signal recognition and motion capture technology into the fatigue state monitoring process and proposes a fatigue analysis method combining an improved EMG fatigue threshold algorithm and biomechanical analysis. In this study, the right upper limb load elbow flexion test was used to simultaneously collect the biceps brachii sEMG signal and upper limb motion capture data, and at the same time the Borg Fatigue Subjective and Self-awareness Scale were used to record the fatigue feelings of the subjects. Then, the fatigue analysis method combining the EMG fatigue threshold algorithm and the biomechanical analysis was combined with four single types: mean power frequency (MPF), spectral moments ratio (SMR), fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC). The test results of the evaluation index fatigue evaluation method were compared. The test results show that the method in this paper has a recognition rate of 98.6% for the overall fatigue state and 97%, 100%, and 99% for the three states of ease, transition and fatigue, which are more advantageous than other methods. The research results of this paper prove that the method in this paper can effectively prevent secondary injury caused by overtraining during upper limb exercises, and is of great significance for fatigue monitoring.
Objective Using cortex convulsions threshold detector and electrical stimulation in rats cortex convulsions threshold model, compare the efficacy and aging of domestic lamotrigine (LTG) and imported LTG.
Methods Electrical stimulation convulsions threshold model in rats after stability, 40 rats were randomly divided into A、B、C、D groups,AandBgroup were divided into three different dose groups: domestic LTG low dose (12.5 mg/kg/d), middle dose (25 mg/kg·d), high dose group (37.5 mg/kg·d); imported LTG low doses (12.5 mg/kg·d), middle dose (25 mg/kg·d), high dose group (37.5 mg/kg·d); Carbamazepine middle dose group (72 mg/kg·d); the control group (normal saline 2 ml/time). Recording electrical stimulation in rats cortex convulsions threshold model after administration, compare the differences before and after the administration.
Results Three different dose groups of domestic LTG and imported LTG all hadahigher level of electrical stimulation cortex convulsions threshold, and showedadose-response relationship. Onset time of LTG after administration was 1 to 2 hours, peak time was 3 to 4 hours, maintaining time was 8 to 10 hours.
Conclusion LTG can improve cortex convulsions threshold in the electrical stimulated rats, there was no significant difference with carbamazepine, and showedadose-response relationship; Repeat dosing for 4 days, both domestic and imported LIG can maintain effective anticonvulsive effect, the efficacy and the aging of two groups of LTG have no significant difference (P＞0.05).
Electric and electronic products are required to pass through the certification on electrical safety performance before entering into the market in order to reduce electrical shock and electrical fire so as to protect the safety of people and property. The leakage current is the most important factor in testing the electrical safety performance and the test theory is based on the perception current effect and threshold. The traditional method testing the current threshold for perception only depends on the sensing of the human body and is affected by psychological factors. Some authors filter the effect of subjective sensation by using physiological and psychological statistical algorithm in recent years and the reliability and consistency of the experiment data are improved. We established an experiment system of testing the human body's current threshold for perception based on EEG feature analysis, and obtained 967 groups of data. We used wavelet packet analysis to detect α wave from EEG, and used FFT to do spectral analysis on α wave before and after the current flew through the human body. The study has shown that about 97.72% α wave energy changes significantly when electrical stimulation occurs. It is well proved that when the EEG feature identification is applied to test the human body current threshold for perception, and meanwhile α wave energy change and human body sensing are used together to confirm if the current flowing through the human body reaches the perception threshold, the measurement of the human body current threshold for perception could be carried out objectively and accurately.
Central serous chorioretinopathy (CSC) is a common macular disease, mainly manifested as a plasma detachment of the macula. Photodynamic therapy (PDT) is an effective treatment for CSC, but with the shortage of the photosensitizer Verteporfin, the effective treatment of CSC has become a common concern for ophthalmologists. In this paper, based on the latest research results on the relationship between the changes in the thickness of the outer nuclear layer and the natural course of the disease and PDT therapy, we propose that patients with CSC should receive effective treatment as early as possible to prevent irreversible damage to visual function due to the thinning of the outer nuclear layer. In addition to PDT, it is recommended that laser photocoagulation or subthreshold micropulse laser treatment of the leaking spot should be considered first, depending on the presence of the leaking spot and its location in relation to the macula center. Anti-vascular endothelial growth factor therapy can be considered if there is a combination of choroidal neovascularization and/or polypoidal choroidal vasculopathy. Other treatments that have not been demonstrated to be effective in evidence-based medicine are not recommended.
