Due to the characteristics and environmental factors, electrocardiogram (ECG) signals are usually interfered by noises in the course of signal acquisition, so it is crucial for ECG intelligent analysis to eliminate noises in ECG signals. On the basis of wavelet transform, threshold parameters were improved and a more appropriate threshold expression was proposed. The discrete wavelet coefficients were processed using the improved threshold parameters, the accurate wavelet coefficients without noises were gained through inverse discrete wavelet transform, and then more original signal coefficients could be preserved. MIT-BIH arrythmia database was used to validate the method. Simulation results showed that the improved method could achieve better denoising effect than the traditional ones.
ObjectiveThis paper aimed to summarize the new progress in surgical indications regarding as maximum diameter from evidence-based medical evidence and morphological rupture-risk assessment of abdominal aortic aneurysms (AAA) and its clinical application value.MethodThe rupture-risk and its mechanism of AAA in specific population and morphological characteristics were reviewed.ResultsAsymptomatic patients in specific subgroups may also benefit from AAA repair by lowering the intervention threshold. Besides the maximum diameter of aneurysm, other morphological factors, such as the true geometric shape, the wall thickness, and mural thrombus also had important predictive value for AAA rupture risk.ConclusionRupture-risk assessment based on the actual individual situation of AAA patients can further facilitate the clinical diagnosis and treatment.
To quantitatively evaluate the upper-limb spasticity of stroke patients in recovery stage, the relationship between surface electromyography (sEMG) characteristic indexes from biceps brachii and triceps brachii and the spasticity were explored, which provides the electrophysiological basis for clinical rehabilitation. Ten patients with spasticity after stroke were selected to be estimated by modified Ashworth (MAS) assessment and a passive elbow sinusoidal motion experiment was carried out. At the same time, the sEMG of biceps and triceps were recorded. The results shows that the reflex electromyographic threshold could reflect the physiological mechanism of spasticity and had significant correlation with MAS scale which showed that sEMG could be prosperous for the clinical quantitative evaluation of spasticity of stroke patients.
Objective To introduce the multivariate random effects model (MREM) in the meta-analysis of diagnostic tests with multiple thresholds. Methods This paper expanded and extended the bivariate random effects model (BREM) to develop the MREM, and implemented it in the SAS Proc NLMIXED procedure. Results The MREM could obtain the study specific ROC curve for each study through empirical Bayes estimation, and the summary ROC curve located in between all study specific ROC curves evenly, while the BREM couldn’t obtain the study specific ROC curve. In addition, in the aspect of parameters estimation, the MREM didn’t depend on the choice of the diagnosis threshold and the type of SROC. The MREM could get only one SROC curve and its AUC was between the AUC of the 5 types of SROC from BREM, so it could avoid overestimation or underestimation. Conclusion The MREM can fully exploit the data, obtain stable and reliable results, and have a good application value in meta-analysis of diagnostic tests with multiple thresholds.
Surface electromyogram (sEMG) may have low signal to noise ratios. An adaptive wavelet thresholding technique was developed in this study to remove noise contamination from sEMG signals. Compared with conventional wavelet thresholding methods, the adaptive approach can adjust thresholds based on different signal to noise ratios of the processed signal, thus effectively removing noise contamination and reducing distortion of the EMG signal. The advantage of the developed adaptive thresholding method was demonstrated using simulated and experimental sEMG recordings.
This paper discusses the relationship between stimulating pulse width and the threshold of electrically evoked compound action potential (ECAP). Firstly, the rheobase and chronaxy from strength-duration curve of nerve fiber was computed using the shepherd's experiment results. Secondly, based on the relationship between ECAP and the action potential of nerve fiber, a mathematical expression to describe the relationship between stimulating pulse width and ECAP threshold was proposed. Thirdly, the parameters were obtained and the feasibility was proved to the expression with the results of experiment using guinea pigs. Research result showed that with ECAP compared to the action potential of nerve fiber, their threshold function relationship with stimulating pulse width was similar, and rheobase from the former was an order smaller in the magnitude than the latter, but the chronaxy was close to each other. These findings may provide meaningful guidance to clinical ECAP measurement and studying speech processing strategies of cochlear implant.
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.
Sub-threshold depression refers to a psychological sub-health state that fails to meet the diagnostic criteria for depression. Appropriate intervention can improve the state and reduce the risks of disease development. In this paper, we focus on music neurofeedback stimulation improving emotional state of sub-threshold depression college students.Twenty-four college students with sub-threshold depression participated in the experiment, 16 of whom were members of the experimental group. Decompression music based on spectrum classification was applied to 16 experimental group participants for 10 min/d music neural feedback stimulation with a period of 14 days, and no stimulation was applied to 8 control group participants. Three feature parameters of electroencephalogram (EEG) relative power, sample entropy and complexity were extracted for analysis. The results showed that the relative power of α、β and θ rhythm increased, while δ rhythm decreased after the stimulation of musical nerofeedback in the experimental group. The sample entropy and complexity were significantly increased after the stimulation, and the differences of these parameters pre and post stimulation were statistically significant (P < 0.05), while the differences of all feature parameters in the control group were not statistically significant. In the experimental group, the scores of self-rating depression scale(SDS) decreased after the stimulation of musical nerofeedback, indicating that the depression was improved. The result of this study showed that music neurofeedback stimulation can improve sub-threshold depression and may provides an effective new way for college students to self-regulation of emotion.
Aiming at the defects that the traditional pulse transit time (PTT) detection methods are sensitive to changes in photoplethysmography (PPG) signal and require heavy computation, we proposed a new algorithm to detect PTT based on waveform time domain feature and dynamic difference threshold. We calculated the PTT by using dynamic difference threshold method to detect the R-waves of electrocardiogram (ECG), shortening the main peak detection range in PPG signal according to the characteristics of the waveform time domain, and using R wave to detect the main peak of PPG signal. We used the American MIMIC database and laboratory test data to validate the algorithm. The experimental results showed that the proposed method could accurately extract the feature points and detect PTT, and the PTT detection accuracies of the measurements and the database samples were 99.1% and 97.5%, respectively. So the proposed method could be better than the traditional methods.
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.