In the evaluation of tear film stability based on corneal topography, a pretreatment algorithm for tear film video was proposed for eye movement, eyelash reflection and background interference. First, Sobel operator was used to detect the blur image. Next, the target image with highlighted ring pattern was obtained by the morphological open operation performed on the grayscale image. Then the ring pattern frequency of the target image was extracted through the Hough circle detection and fast Fourier transform, and a band-pass filter was applied to the target image according to the ring pattern frequency. Finally, binarization and morphological closed operation were used for the localization of the ring pattern. Ten tear film videos were randomly selected from the database and processed frame by frame through the above algorithm. The experimental results showed that the proposed algorithm was effective in removing the invalid images in the video sequence and positioning the ring pattern, which laid a foundation for the subsequent evaluation of tear film stability.
Conventional maximum intensity projection (MIP) images tend to ignore some morphological features in the detection of intracranial aneurysms, resulting in missed detection and misdetection. To solve this problem, a new method for intracranial aneurysm detection based on omni-directional MIP image is proposed in this paper. Firstly, the three-dimensional magnetic resonance angiography (MRA) images were projected with the maximum density in all directions to obtain the MIP images. Then, the region of intracranial aneurysm was prepositioned by matching filter. Finally, the Squeeze and Excitation (SE) module was used to improve the CaraNet model. Excitation and the improved model were used to detect the predetermined location in the omni-directional MIP image to determine whether there was intracranial aneurysm. In this paper, 245 cases of images were collected to test the proposed method. The results showed that the accuracy and specificity of the proposed method could reach 93.75% and 93.86%, respectively, significantly improved the detection performance of intracranial aneurysms in MIP images.
Objective
To explore the application of two methods of direct fecal detection ofClostridium difficilein patients with recurrent inflammatory bowel disease (IBD), including nucleic acid amplification test (NAAT) and enzyme immunoassay (EIA), in order to provide support for hospitals to prevent and control clostridium difficile infection (CDI).
Methods
Fresh feces of 48 patients with recurrent IBD treated between November 2014 and April 2015 were collected within 48 hours after admission. Anaerobic culture and identification, NAAT and EIA were used to test the same samples. Statistical analysis was performed using Kappa test.
Results
Among the 48 fecal samples,Clostridium difficilewas negative in 37 and positive in 11 including 2 (4.2%) with toxigenicClostridium difficile characterized as toxin type A+B+. Compared with anaerobic culture and identification, NAAT had a perfect correlation (Kappa=1.00,P<0.05), and EIA had an almost perfect correlation (Kappa=0.89,P<0.05). But EIA toxin test had missed detection of toxigenic samples.
Conclusions
For patients with recurrent IBD combined with CDI, both NAAT and EIA test may be applied to detctClostridium difficile in feces directly, while NAAT may show a better performance. Samples from highly suspected patients with negative toxin result tested by EIA should be confirmed by other methods such as NAAT.
Objective To explore the methods of early diagnosis of arteriosclerosis obliterans of lower extremity (ASOLE). Methods The related literatures on ASOLE detection means adopted clinically were reviewed, and their advantages and disadvantages were compared.Results Asymptomatic ASOLE could be discovered by determination of ankle brachial index (ABI) and toe brachial index (TBI), which was a good index for arterial function assessment of lower extremity. Pulse wave velocity (PWV) was more vulnerable and less sensitive than ABI, and therefore more suitable for screening of a large sample. ASI was an index to assess arterial structure and function, and it had a good correlation with PWV. Flow-mediated dilation (FMD) was a measurement evaluating the function of endothelial cell; Pulse wave measurement was simple, sensitive, and its result was reliable. Color Doppler ultrasonography could localizate the lesion and determine the degree of stenosis at the same time. Multiple-slice CT angiography (MSCTA) was more accurate than color Doppler ultrasonography, but its inherent shortcomings, such as nephrotoxicity of contrast agent, was still need to be resolved. 3D-contrast enhancement magnetic resonance angiography (CEMRA) had little nephrotoxicity, but a combination of other imaging methods was necessary. Microcirculation detections required high consistency of the measurement environment, but they were simple, sensitive and noninvasive, and therefore could be used for screening of ASO. Conclusion Publicity and education of highrisk groups, and reasonable selection of all kinds of detection means, are helpful to improve the early diagnosis of ASOLE.
The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.
It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.
The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.
ObjectiveTo investigate a more convenient and safe sampling method for viral nucleic acid detection of coronavirus disease 2019.MethodsAn oropharyngeal swab and nasopharyngeal swab were simultaneously taken from 100 patients with coronavirus disease 2019 in a hospital in Wuhan. Then the efficacies of two sampling methods were compared on the positive rates of viral nucleic acid detection.ResultsThe positive rate for SARS-CoV-2 was 54% in oropharyngeal swabs, while 89% positive in nasopharyngeal swabs. There was a significant difference in the detection rate between oropharyngeal swab and nasopharyngeal swab (χ2=3.850 4, P=0.049 7).ConclusionsThe positive rate for nucleic acid testing from nasopharyngeal swabs are significantly better than that from oropharyngeal swabs. Therefore, sampling by nasopharyngeal swabs, rather than oropharyngeal swabs, should be chosen as the preferred virological screening method for patients with coronavirus disease 2019.
Ambulatory electrocardiogram (ECG) monitoring can effectively reduce the risk and death rate of patients with cardiovascular diseases (CVDs). The Body Sensor Network (BSN) based ECG monitoring is a new and efficient method to protect the CVDs patients. To meet the challenges of miniaturization, low power and high signal quality of the node, we proposed a novel 50 mm×50 mm×10 mm, 30 g wireless ECG node, which includes the single-chip analog front-end AD8232, ultra-low power microprocessor MSP430F1611 and Bluetooth module HM-11. The ECG signal quality is guaranteed by the on-line digital filtering. The difference threshold algorithm results in accuracy of R-wave detection and heart rate. Experiments were carried out to test the node and the results showed that the proposed node reached the design target, and it has great potential in application of wireless ECG monitoring.
