In recent years, wearable devices grew up gradually and developed increasingly. Aiming at the problems of skin sensibility and the change of electrode impedance of Ag/AgCl electrode in the process of long-term electrocardiogram (ECG) signal monitoring and acquisition, this paper discussed in detail a new sensor technology–fabric electrode, which is used for ECG signal acquisition. First, the concept and advantages of fabric electrode were introduced, and then the common substrate materials and conductive materials for fabric electrode were discussed and evaluated. Next, we analyzed the advantages and disadvantages from the aspect of textile structure, putting forward the evaluation system of fabric electrode. Finally, the deficiencies of fabric electrode were analyzed, and the development prospects and directions were prospected.
Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.
ObjectiveTo investigate the effect of early enteral nutrition (EEN) support in the perioperative period of children with perforated appendicitis based on the enhanced recovery after surgery (ERAS). MethodsThe children with perforated appendicitis were collected as an observation group, who underwent EEN support treatment based on the ERAS mode from January 2021 to December 2022 in the Xuzhou Children’s Hospital. At the same time, the children with perforated appendicitis received conventional nutrition support from January 2019 to December 2020 were matched as a control group according to the principle of balanced and comparable baseline data such as the gender, age, disease course, pathological type, and body mass index with the observation group. The time of first exhaust or defecation and the hospital stay after surgery were compared. Meanwhile, the nutritional indexes [prealbumin (PA), albumin (ALB), hemoglobin (Hb)], immune indexes [immunoglobulin (Ig) A, IgM, IgG], serum inflammatory factors [C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α)] before surgery, on day 1 and 7 after surgery were compared. And the adverse effects were observed. ResultsThere were 40 children with perforated appendicitis in the observation group and the control group, respectively. There were no statistical differences in the baseline data such as the gender, age, course of disease, pathological type, and body mass index between the two groups (P>0.05). The time of first exhaust or defecation and the hospital stay after surgery in the observation group were shorter than in the control group (t=3.234, P=0.002; t=5.582, P<0.001). The levels of PA, ALB, Hb, IgA, IgM, and IgG in the observation group were higher than in the control group on day 7 after surgery (P<0.05). The levels of CRP, IL-6, and TNF-α in the observation group were lower than in the control group on day 7 after surgery (P<0.05）. The incidence of adverse reactions in the observation group was lower than that in the control group [5.0% (2/40) vs. 22.5% (9/40), χ2=5.165, P=0.023]. ConclusionsFrom on the results of this study, EEN support based on ERAS during perioperative period of children with perforated appendicitis contributes to recover gastrointestinal function, correct nutritional status, improve immune function, and reduce inflammation, and which has a higher safety for children with perforated appendicitis.
Human motion recognition (HAR) is the technological base of intelligent medical treatment, sports training, video monitoring and many other fields, and it has been widely concerned by all walks of life. This paper summarized the progress and significance of HAR research, which includes two processes: action capture and action classification based on deep learning. Firstly, the paper introduced in detail three mainstream methods of action capture: video-based, depth camera-based and inertial sensor-based. The commonly used action data sets were also listed. Secondly, the realization of HAR based on deep learning was described in two aspects, including automatic feature extraction and multi-modal feature fusion. The realization of training monitoring and simulative training with HAR in orthopedic rehabilitation training was also introduced. Finally, it discussed precise motion capture and multi-modal feature fusion of HAR, as well as the key points and difficulties of HAR application in orthopedic rehabilitation training. This article summarized the above contents to quickly guide researchers to understand the current status of HAR research and its application in orthopedic rehabilitation training.
Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model.
ObjectiveTo investigate the effect of virtual scene simulation training combined with midium frequency impulse electrotherapy on upper limb function and daily living ability of hemiplegia patients.MethodsFrom March to October 2019, 50 hemiplegic patients were recruited and randomly assigned to the trial group and the control group, with 25 patients in each group. The control group was given routine rehabilitation training, while the trial group was given virtual scene simulation training and medium frequency impulse electrotherapy on the basis of routine rehabilitation training. The Fugl-Meyer Assessment-Upper Extremities (FMA-UE), Simple Test for Evaluating Hand Function (STEF), and Modified Barthel Index (MBI) were used to assess patients’ upper limb function and daily living ability before treatment and after 8 weeks of treatment.ResultsBefore treatment, the FMA-UE, STEF, and MBI scores of the trial group vs. the control group were 22.88±5.18 vs. 23.44±6.26, 40.12±4.82 vs. 41.44±4.54, and 51.40±7.29 vs. 48.60±7.00, respectively, and none of the between-group differences was statistically significant (P>0.05); after 8 weeks of treatment, the FMA-UE, STEF, and MBI scores of the two groups were 39.48±6.35 vs. 33.52±6.53, 59.08±7.54 vs. 52.52±5.83, and 71.00±8.78 vs. 62.40±9.37, respectively, and all of the between-group differences were statistically significant (P<0.05). After 8 weeks of treatment, the FMA-UE, STEF and MBI scores of the two groups of patients were significantly improved compared with those before treatment (P<0.05), and the improvement of each score of the trial group was significantlybetter than that of the control group (P<0.05). No stroke recurrence, electric burn, or other adverse reactions occurred in the two groups after treatment. ConclusionVirtual scene simulation training combined with midium frequency impulse electrotherapy can effectively improve the upper limb function of patients with hemiplegia and improve their quality of life.