To assess the rel iabil ity of diabetic cutaneous ulcer surface area (DCUSA) measurement usingdigital planimetry method (A) and transparency tracing method (B). Methods Images of diabetic cutaneous ulcers from35 inpatients with diabetic skin ulcers from September 2005 to April 2007 were taken by a digital camera once a week or twice a week over a period of 12 weeks, resulting in 305 photographs; the ulcers were traced on a grid with acetate wound tracings, simultaneously. A total of 305 pairs of DCUSA which were calculated respectively throughout digital camera combined with Image J medical imaging software and transparency tracing with grid sheet by two independent observers sequentially were obtained. The intraclass correlation coefficients (ICCs, one-way random effect model) was used as an indicator of chancecorrected agreement to estimate the relative rel iabil ity for the interobserver data. Multiple l inear regression analysis was also used to measure the relationship of these two methods. Results DCUSA obtained from method A and obtained from method B was (4.84 ± 7.73) cm2 and (5.03 ± 7.89) cm2, respectively; no significant difference was found (P gt; 0.05). ICCs was high (ICCs=0.949 for method B and 0.965 for method A), indicating that the relative rel iabil ity for the interobserver was excellent. The method A were highly correlated with measurements obtained from method B (r = 0.957, P lt; 0.05). Conclusion The digital planimetry method described in this study represents a simple, practical, without any wound damage and contamination, and inexpensive technique to accurately evaluate the areas of diabetic cutaneous ulcers. The photographic technique combined with Image J medical imaging software should be considered for wound measurement.
Objective To reflect the correlation between social support and mental health of the aged through the Pearson correlation coefficient. Methods Databases including PubMed, SpringerLink, EMbase, The Cochrane Library, VIP, WanFang Data and CNKI were searched from inception to October, 2011 to collect literature on the correlation between social support and mental health of the aged. The studies were screened according to the inclusion and exclusion criteria. After extracting data and assessing the quality of the included studies, meta-analysis was conducted using RevMan 5.0 software. Results Of the 2 396 identified studies, 4 studies were included. The results showed that 4 studies were not high in the overall quality. The total score of social support of the elderly and its three dimensions were related to mental health. Among 9 factors associated with mental health, somatization, depression and anxiety were weakly correlated to the objective support while the others were extremely weakly correlated. Anxiety and phobic anxiety were weakly correlated to the subjective support while the others were extremely weakly correlated. Phobic anxiety was weakly correlated to the utilizing degree while the others were extremely weakly correlated. Somatization, anxiety and phobic anxiety were weakly correlated to the total score of social support while the others were extremely weakly correlated. Conclusion Social support probably improves mental health of the aged to some extent.
In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCI) systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset Ⅳa from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.
Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.
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.
Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects’ optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.