Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
ObjectiveTo explore the effect of percutaneous kyphoplasty (PKP) on lumbar-pelvic correlation in osteoporotic vertebral compressive fracture (OVCF).MethodsAccording to the inclusion criteria, 63 patients with primary osteoporosis between January 2012 and June 2017 were selected as the control group and 67 patients with single-segment lumbar OVCF receiving PKP and complete clinical data were included as the observation group. There was no significant difference in gender, age, and lumbar spine bone density between the two groups (P>0.05). The visual analogue scale (VAS) score and Oswestry Disability Index (ODI) score were used to assess lumbar pain and function before operation and at 3 days after operation in the observation group; lumbar lordosis (LL), pelvic incidence (PI), pelvic tilt (PT), and sacral slope (SS) were measured in lumbar lateral X-ray films which were taken before PKP and at 1 month after PKP. The same parameters were measured in the lumbar lateral X-ray films which were taken at the time of initial diagnosis in the control group.ResultsAll patients were followed up 3-24 months with an average of 5.8 months in the observation group. The VAS score decreased from 5.6±1.8 before PKP to 2.8±1.3 at 3 days after PKP (t=14.082, P=0.000); ODI decreased from 50.1%±5.0% before PKP to 18.2%±1.8% (t=47.011, P=0.000). Compared with the control group, the LL, PI, and SS decreased and the PT increased in the observation group, and only the difference in LL between the two groups was significant (P<0.05). In the observation group, the LL and SS significantly increased (P<0.05) and PT significantly decreased (P<0.05) at 1 month after operation when compared with preoperative ones, and PI decreased, but the difference was not significant (P>0.05). In the control group, LL was positively correlated with PI and SS (P<0.05); PI was positively correlated with PT and SS (P<0.05). In the observation group, PI was positively correlated with SS (P<0.05) before and after PKP.ConclusionOVCF patients lost the specific lumbar-pelvic correlation. PKP can restore lumbar lordosis, but it still can not restore the normal physiological fitting.
The quality of sleep has a great relationship with health and working efficiency. The result of sleep stage classification is an important indicator to measure the quality of sleep, and it is also an important way to diagnose and treat sleep disorders. In this paper, the method of detrended cross-correlation analysis (DCCA) was used to analyze sleep stage classification, sleep electroencephalograph signals, which were extracted from the MIT-BIH Polysomnographic Database randomly. The results showed that the average DCCA exponent of the awake period is smaller than that of the first stage of non-rapid eye movement (NREM) sleeps. It is well concluded that the method of studying the sleep electroencephalograph with this method is of great significance to improve the quality of sleep, to diagnose and to treat sleep disorders.
ObjectiveTo study the relationship among cholecystectomy/gallbladder disease and bile reflux gastritis.MethodsA retrospective collection of 123 patients with bile reflux gastritis who were diagnosed as outpatients and hospitalized from January 2014 to February 2019 in Shengjing Hospital Affiliated to China Medical University, and 221 patients with non-biliary reflux gastritis at the same period were collected. According to the gallbladder status, the patients were divided into three groups: gallbladder disease, cholecystectomy, and gallbladder disease-free group. The relationship between gallbladder status and bile reflux gastritis was analyzed.ResultsAmong 123 patients with bile reflux gastritis, there were 22 cases (17.89%) with cholecystectomy and 26 cases (21.14%) with gallbladder disease; 221 cases of non-biliary reflux gastritis with cholecystectomy in 7 cases (3.17%) and gallbladder disease in 30 cases (13.57%). Univariate analysis showed that the gallbladder status was different between the bile reflux gastritis group and the non-biliary reflux gastritis group (χ2=21.089, P<0.001). The study showed that the gallbladder status was related to the occurrence of bile reflux gastritis. In contrast, patients with cholecystectomy and gallbladder disease had a higher risk of occurrence than those with no gallbladder disease (OR>1, P<0.012 5). Independent risk factors were considered by logistic multivariate regression analysis, including cholecystectomy, gallbladder disease, and age (P<0.05).ConclusionsThere is a correlation between cholecystectomy/gallbladder disease and bile reflux gastritis. Cholecystectomy and gallbladder disease may be the independent risk factors for bile reflux gastritis.
The study of atrial fibrillation (AF) has been known as a hot topic of clinical concern. Body surface potential mapping (BSPM), a noninvasive electrical mapping technology, has been widely used in the study of AF. This study adopted 10 AF patients’ preoperative and postoperative BSPM data (each patient’s data contained 128 channels), and applied the autocorrelation function method to obtain the activation interval of the BSPM signals. The activation interval results were compared with that of manual counting method and the applicability of the autocorrelation function method was verified. Furthermore, we compared the autocorrelation function method with the commonly used fast Fourier transform (FFT) method. It was found that the autocorrelation function method was more accurate. Finally, to find a simple rule to predict the recurrence of atrial fibrillation, the autocorrelation function method was used to analyze the preoperative BSPM signals of 10 patients with persistent AF. Consequently, we found that if the patient’s proportion of channels with dominant frequency larger than 2.5 Hz in the anterior left region is greater than the other three regions (the anterior right region, the posterior left region, and the posterior right region), he or she might have a higher possibility of AF recurrence. This study verified the rationality of the autocorrelation function method for rhythm analysis and concluded a simple rule of AF recurrence prediction based on this method.
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.
Objective To summarize the current research progress on the concept, clinical presentation, diagnosis, biomechanical changes, and pathological mechanisms of the medial meniscus posterior root tear (MMPRT), and its clinical correlations with tibial rotation. MethodsThe research literature on MMPRT and its relationship with tibial rotation at home and abroad in recent years was extensively consulted and summarized. Results MMPRT is a specific and common type of medial meniscus injury of the knee joint. The occurrence of posterior medial pumping pain events following low-energy trauma in patients provides important clues for the diagnosis of this injury, with MRI being the preferred imaging modality. The biomechanical effects generated by MMPRT are similar to those caused by total removal of the medial meniscus. And this injury is usually associated with tibial rotation. MMPRT induces pathological external rotation of the tibia, which can be restored by timely medial meniscus posterior root repair. Furthermore, changes in tibial rotation are related to the healing status after medial meniscus posterior root repair. ConclusionMMPRT is closely related to tibial rotation. Understanding the biomechanics, pathological mechanisms, and clinical correlations between the two is of great significance for improving the diagnosis and treatment strategies.
Objective To analyze the causal relationship between gut microbiota and tic disorder based on Mendelian randomization (MR). Methods A total of 196 known microbiota (9 phyla, 16 classes, 20 orders, 32 families, and 119 genera) in the human intestinal microbiota dataset downloaded from the MiBioGen database were selected as the exposure factors, and the dataset of tic disorder (finn-b-KRA_PSY_TIC) containing 172 patients and 218620 controls was downloaded from the genome-wide association study database as the outcome variable. Inverse variance weighted was used as the main analysis method, and the causal relationship between gut microbiota and tic disorder was evaluated using odds ratio (OR) and its 95% confidence interval (CI). Horizontal pleiotropy was tested by MR-Egger intercept and MR-PRESSO global test, heterogeneity was assessed by Cochran’s Q test, and sensitivity analysis was performed by leave-one-out method. Results Inverse variance weighted results showed that the Family Rhodospirillaceae [OR=0.398, 95%CI (0.191, 0.831), P=0.014], Order Rhodospirillales [OR=0.349, 95%CI (0.164, 0.743), P=0.006], and Parasutterella [OR=0.392, 95%CI (0.171, 0.898), P=0.027] had negative causal relationships with tic disorder. The Genus Lachnospira [OR=8.784, 95%CI (1.160, 66.496), P=0.035] and Candidatus Soleaferrea [OR=2.572, 95%CI (1.161, 5.695), P=0.020] had positive causal relationships with tic disorder. In addition, MR-Egger intercept and MR-PRESSO global test showed no horizontal pleiotropy, Cochran’s Q test showed no heterogeneity, and leave-one-out sensitivity analysis showed the results were stable. Conclusions A causal relationship exists between gut microbiota and tic disorder. The Family Rhodospirillaceae, Order Rhodospirillales, and Parasutterella are associated with a decreased risk of tic disorder, while the Genus Lachnospira and Candidatus Soleaverea can increase the risk of tic disorder.
Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.
ObjectiveThe time relationship between seizure semiology and epileptic discharges during focal epileptic seizures is a crucial predictor for the localization of epileptogenic zone. Low voltage fast activities (LVFA), especially gamma band oscillations, are confirmed to play a central role in ictogenesis and semiology production. In the present study, we focus on the “electro-clinical correlation” between LVFA in agranulo-dysgranular insulo-cingulate cortices and the sign of “Chapeau de gendarme (CDG)” via detailed analysis of ictal video-stereoencephalography (video-SEEG) of focal epileptic seizures. MethodsWe retrospectively analyzed the ictal video-SEEG of the 7 cases in which CDG signs were presented in habitual seizures and intracerebral electrodes were co-implanted in agranulo-dysgranular insular and cingulate cortices. We calculate the latency of LVFA in each of cortical regions of interest, agranulo-dygranular insular cortex, agranulo-dysgranular cingulate cortex, and the latency of CDG signs via visual and spectral analysis of the ictal SEEG. Moreover, Pearson correlation analysis and linear regression were used to test the time relationship between gamma band oscillations in agranulo-dysgranular insulo-cingulate cortices and generation of CDG signs. ResultsThe co-activation of LVFA occurred in agranulo-dysgranular insulo-cingulate cortices always preceded the appearance of CDG sign in all of the 69 seizures. The LVFA were confirmed as gamma band oscillations via spectral analysis of SEEG. A linear relationship between the latencies of CDG signs and the latencies of co-activation of agranulo-dysgranular insulo-cingulate cortices in gamma band was furth confirmed by Pearson correlation analysis and linear regression. ConclusionsThere is a causal relationship between the involvement of agranulo-dysgranular insulo-cingulate cortices and the generation of CDG sign, and thus the CDG sign could be view as semiological marker of activation of emotional insulo-cingulate cortex in focal epilepsy.
