Article of the Year 2020
EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer InterfaceRead the full article
Journal of Healthcare Engineering provides a vehicle for the exchange of advanced knowledge, emerging technologies, and innovative ideas related to all aspects of engineering involved in healthcare delivery processes and systems.
Chief Editor, Professor Zollo, has research expertise in neuro-robotics and biomedical technologies for neuroscience, rehabilitation and assistance robotics, and robotic and mechatronic devices for personal assistance and service robotics.
Latest ArticlesMore articles
Menopausal Women’s Health Care Method Based on Computer Nursing Diagnosis Intelligent System
Taking into account the current feature extraction speed and recognition effect of intelligent diagnosis of menopausal women’s health care behavior, this paper proposes to use a cross-layer convolutional neural network to extract behavior features autonomously and use support vector machine multiclass behavior classifier to classify behavior. Compared with the feature images extracted by traditional methods, the behavioral features extracted in this paper are related to the individual menopausal women and have better semantic information, and the feature description ability in the time domain and the space domain has been enhanced. Through Matlab software, using the database established in this paper to compare its feature extraction time, test classification time, and final recognition accuracy with ordinary convolutional neural networks, it is concluded that the cross-layer CNN-SVM model can ensure the speed of feature extraction. It proves that the method in this paper can be applied to the behavioral intelligent diagnosis system for intelligently nursing menopausal women and has good practical value. This paper designs a home care bed intelligent monitoring system, which can automatically detect the posture of the care bed, and not only can change the posture of the bed under the control of personnel, but also can automatically complete the posture conversion according to the setting. At the same time, the system has the function of monitoring the physical condition of the person being cared for and can detect the heart rate, blood oxygen, and other physiological indicators of the bedridden person. In addition, the system can also provide a remote diagnosis function, allowing nursing staff to remotely view the current state of the nursing bed and the physical condition of the person. After testing, the system works stably, improves the automation and safety of the nursing bed control, and enriches the functions of the nursing bed.
Human-in-the-Loop Predictive Analytics Using Statistical Learning
The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human’s input is much essential to accurately understand their health condition to take appropriate decision on time. Therefore, we have proposed a healthcare framework involving the concept of artificial intelligence in the human-in- the-loop cyber-physical system. This model works via a response loop in which the human’s intention is concluded by gathering biological signals and context data, and then, the decision is interpreted to a system action that is recognizable to the human in the physical environment, thereby completing the loop. In this paper, we have designed a model for early prognosis of coma using the electroencephalogram dataset. In the proposed approach, we have achieved the best results using a statistical learning algorithm called autoregressive integrated moving average in comparison to artificial neural networks and long short-term memory models. In order to measure the efficiency of our model, we have used the root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) value to evaluate the linear models as it gives the difference between the measured value and true or correct value. We have achieved the least possible error value for our dataset. To conduct this experiment, we used the dataset available in the phsyionet opensource community.
Gene Characteristics and Prognostic Values of m6A RNA Methylation Regulators in Nonsmall Cell Lung Cancer
Background. N6-methyladenosine (m6A) is the most common internal modiﬁcation present in mRNAs and long noncoding RNAs (lncRNAs), associated with tumorigenesis and cancer progression. However, little is known about the roles of m6A and its regulatory genes in nonsmall cell lung cancer (NSCLC). Here, we systematically explored the roles and prognostic significance of m6A-associated regulatory genes in NSCLC. Methods. The copy number variation (CNV), mutation, mRNA expression data, and corresponding clinical pathology information of 1057 NSCLC patients were downloaded from the cancer genome atlas (TCGA) database. The gain and loss levels of CNVs were determined by utilizing segmentation analysis and GISTIC algorithm. The GSEA was conducted to explore the functions related to different levels of m6A regulatory genes. Logrank test was utilized to assess the prognostic significance of m6A-related gene’s CNV. Results. The genetic alterations of ten m6A-associated regulators were identified in 102 independent NSCLC samples and significantly related to advanced tumor stage. Deletions or shallow deletions corresponded to lower mRNA expression while copy number gains or amplifications were related to increased mRNA expression of m6A regulatory genes. Survival analysis showed the patients with copy number loss of FTO with worse disease-free survival (DFS) or overall survival (OS). Besides, copy number loss of YTHDC2 was also with poor OS for NSCLC patients. Moreover, high FTO expression was significantly associated with oxidative phosphorylation, translation, and metabolism of mRNA. Conclusion. Our findings provide novel insight for better understanding of the roles of m6A regulators and RNA epigenetic modification in the pathogenesis of NSCLC.
Study on the Application and Efficacy of Responsibility Nursing in Dialysis Care
Providing high-quality care to patients undergoing hemodialysis (HD) is a priority for nurses. The present study was conducted to explore the experiences of the quality of nursing care among patients, nurses, and caregivers in Yanghu Branch of Changzhou Second People’s Hospital, China. A total of 120 hemodialysis patients consecutively admitted to Yanghu Branch of Changzhou Second People’s Hospital were enrolled and divided into two groups according to the nursing method they received: control group (routine nursing) and experimental group (responsibility nursing). The two cohorts were observed and compared for alterations of adverse emotions and inflammatory factors, the incidence of complications, pre-and post-nursing sleep quality, life quality, and patients’ satisfaction with nursing. After nursing, the Self-Rating Anxiety/Depression Scale (SAS/DS) scores were lower in the experimental group (EG) than in the control group (CG) (both ). Serum IL-6, hs-CRP, and TNF-α were decreased in both groups after nursing and were even lower in EG (both ). EG had significantly improved sleep quality and life quality than CG, with a higher nursing satisfaction (all ). This validates that the responsibility nursing for dialysis patients can validly mitigate patients’ negative emotions, improve their quality of life, and ensure high-quality dialysis effect, which is feasible for wide popularization and application in clinics.
Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia
The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant (); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance (). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.
Deep Learning-Based Denoised MRI Images for Correlation Analysis between Lumbar Facet Joint and Lumbar Disc Herniation in Spine Surgery
This work aimed to explore the relationship between spine surgery lumbar facet joint (LFJ) and lumbar disc herniation (LDH) via compressed sensing algorithm-based MRI images to analyze the clinical symptoms of patients with residual neurological symptoms after LDH. Under weighted BM3D denoising, Epigraph method was introduced to establish the novel CSMRI reconstruction algorithm (BEMRI). 127 patients with LDH were taken as the research objects. The BEMRI algorithm was compared with others regarding peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Patients’ bilateral LFJ angles were compared. The relationships between LFJ angles, lumbar disc degeneration, and LFJ degeneration were analyzed. It turned out that the PSNR and SSIM of BEMRI algorithm were evidently superior to those of other algorithms. The proportion of patients with grade IV degeneration was at most 31.76%. Lumbar disc grading was positively correlated with change grading of LFJ degeneration (). LFJ asymmetry was positively correlated with LFJ degeneration grade and LDH (). Incidence of residual neurological symptoms in patients aged 61–70 years was as high as 63.77%. The proportion of patients with severe urinary excretion disorders was 71.96%. Therefore, the BEMRI algorithm improved the quality of MRI images. Degeneration of LDH was positively correlated with degeneration of LFJ. Asymmetry of LFJ was notably positively correlated with the degeneration of LFJ and LDH. Patients aged 61–70 years had a high incidence of residual neurological symptoms after surgery, most of which were manifested as urinary excretion disorders.