Objective To explore the relationship between the diabetic retinopathy (DR) and the changes of erythrocyte deformability(ED),erythrocyte membrane phospholipid and spectrin. Methods One hundred and eight patients with non-insulin dependent diabetes mellitus were divided into DR group(55 cases)and nonDR(NDR)group(53 cases).The changes of erythrocyte filtration index(EFI),erythrocyte membrane phospholipid and spectrin dimers(SP-D)and spectrin tetramers (SP-T)were measured in patients of DR and NDR groups and compared with the results of 53 cases of normal control group. Results The EFI,SP-D, SP-D/SP-T,sphingomyelin (SM) /phophatidylcholine(PC)were higher,and SPT,SM,PC,phophatidylserine(PS)and phatidylethanolamine(PE)were lower in patients with DR than those in control and NDR patients (F=8.467～18.925,q=6.845～12.627,Plt;0.001).The changes of all indicators in proliferative DR(PDR) patients were more obvious than those in background DR(BDR) patients(t=5,825-15.443,Plt;0.001).The EFI in DR patients was positively correlated to SM/PC,SP-D and SP-D/SP-T(Plt;0.01)，negatively correlated to SM,PC,PE,PS and SP-T(Plt;0.01). Conclusions The decrease of ED caused by the abnormalities of erythrocyte membrane phospholipid and spectrin might participate in the occurance and development of DR,and correlated to the degree of pathologic changes. (Chin J Ocul Fundus Dis, 1999, 15: 160-162)
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.
Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues—the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.