In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation, which leads to the low and unstable recognition efficiency. Aiming at this problem, a method that uses the fusion features of ECG and sEMG signal to estimate the fatigue during lower limb rehabilitation was proposed, and an improved particle swarm optimization-support vector machine classifier (improved PSO-SVM) was proposed and used to identify the fusion feature vector. Finally, the accurate recognition of the three states of relax, transition and fatigue was achieved, and the recognition rates were 98.5%, 93.5%, and 95.5%, respectively. Comparative experiments showed that the average recognition rate of this method was 4.50% higher than that of sEMG features alone, and 13.66% higher than that of the combined features of ECG and sEMG without feature fusion. It is proved that the feature fusion of ECG and sEMG signals in the process of lower limb rehabilitation training can be used for recognizing fatigue more accurately.
ObjectiveTo compare and analyze the effect of myocardial protection between HTK and del Nido cardioplegia solutions in neonates with surgeries for transposition of the great arteries. MethodsThe clinical data of 208 neonates with complete transposition of the great arteries in our institution from 2014 to 2020 were retrospectively analyzed. According to the cardioplegia solutions utilized in the operations, the patients were divided into two groups: a HTK group and a del Nido group. Propensity score matching was conducted to eliminate the biases. The cardiopulmonary bypass time, aortic cross-clamping time, total amount of cardioplegia solutions, transfusion frequency of cardioplegia, ICU stay time, mechanical support time, inotropic score, hospital stay, left ventricular ejection fraction, N-terminal proBNP and troponin I were compared and analyzed between the two groups after matching. ResultsAfter 1:1 propensity score matching, a total of 54 patients were analyzed with 27 patients in each group. In the HTK group, there were 22 males and 5 females with a median age of 7.0 (2.0, 11.0) d. In the del Nido group, there were 23 males and 4 females with a median age of 8.0 (3.0, 11.0) d. A total of 3 children died after the surgery: 2 (7.4%) patients in the HTK group and 1 (3.7%) patient in the del Nido group. There was no significant difference in hospital mortality between the two groups (P=1.000). The total amount of cardioplegia solutions in the HTK group was significantly higher than that of del Nido group (P<0.001). Transfusion frequency of cardioplegia in del Nido group was significantly higher than that of the HTK group (P=0.043). There was no significant difference in the postoperative ICU time, mechanical support time, length of hospital stay, inotropic score, left ventricular ejection fraction, N-terminal B-type natriuretic peptide precursor or troponin I between the two groups (P>0.05). ConclusionFor neonates with surgeries for complete transposition of the great arteries, HTK cardioplegia solutions can provide effective and safe myocardial protection, which is similar to del Nido cardioplegia solutions.
Cardiovascular diseases is the leading cause of threat to human life and health worldwide. Early risk assessment, timely diagnosis, and prognosis evaluation are critical to the treatment of cardiovascular diseases. Currently, the evaluation of diagnosis and prognosis of cardiovascular diseases mainly relies on imaging examinations such as coronary CT and coronary angiography, which are expensive, time-consuming, partly invasive, and require high professional competence of the operator, making it difficult to promote in the community or in areas where medical resources are scarce. The fundus microcirculation is a part of the human microcirculation and has similar embryological origins and physiopathological features to cardiovascular circulation. Several studies have revealed fundus imaging biomarkers associated with cardiovascular diseases, and developed and validated intelligent diagnosis and treatment models for cardiovascular diseases based on fundus imaging data. Fundus imaging is expected to be an important adjunct to cardiovascular disease diagnosis and treatment given its noninvasive and convenient nature. The purpose of this review is to summarize the current research status, challenges, and future prospects of the application of artificial intelligence based on multimodal fundus imaging data in cardiovascular disease diagnosis and treatment.