In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.
In order to improve the efficiency of protein spots detection, a fast detection method based on CUDA was proposed. Firstly, the parallel algorithms of the three most time-consuming parts in the protein spots detection algorithm: image preprocessing, coarse protein point detection and overlapping point segmentation were studied. Then, according to single instruction multiple threads executive model of CUDA to adopted data space strategy of separating two-dimensional (2D) images into blocks, various optimizing measures such as shared memory and 2D texture memory are adopted in this study. The results show that the operative efficiency of this method is obviously improved compared to CPU calculation. As the image size increased, this method makes more improvement in efficiency, such as for the image with the size of 2 048×2 048, the method of CPU needs 5 2641 ms, but the GPU needs only 4 384 ms.
Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.
Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. MethodsPatients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. ResultsA total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.
A hand-held electrocardiogram (ECG) monitor with capacitive coupling is designed in this study that can rapidly detect ECG signals through clothing. This new device improves many deficiencies of the traditional ECG monitor, such as infection due to direct skin contacting, inconvenience, and time-consuming. In specificity, the hand-held ECG monitor consists of two parts, a sensor and an embedded terminal. ECG signals are initially detected by a sensing electrode placed on the chest through clothing, then treated by single ended differential amplification, filtering and master amplification, and later processed through A/D conversion and ECG signal transmission by CC2540 module. The waveform and heart rate are finally displayed on the screen based on digital filtering and data processing for the received ECG signal on the embedded terminal. Results confirm that the newly developed hand-held ECG monitor is capable of detecting real-time ECG signals through clothing with advantages of simple operation, portability and rapid detection.
Heritable aortic disease (HAD) is characterized by thoracic aortic aneurysm/dissection with strong genetic predisposition and high clinical phenotypic heterogeneity. HAD is one of the main causes of sudden death. Early diagnosis of this disease is difficult because of atypical clinical symptoms, leading to the deterioration of disease with the development of aortic aneurysm, aortic dissection or sudden death. Genetic testing plays an important role in the early diagnosis, standardized follow-up, screening of family members, genetic counseling and individualized treatment of HAD. This review focused on the application of genetic testing in the standardized diagnosis and treatment of HAD.
ObjectiveTo suggest the importance of taking notice of oral chemotherapy drugs in cancer patients, and the importance of drug-use evaluation in patients with insufficient kidney functions, by reporting one death case caused by multiple organ failure because of myelosuppression after oral tegafur, gimeracil and oteracil potassium (S-1) capsules for 10 days in a patient with insufficient kidney functions. MethodsThrough the analysis of one patient who died of multiple organ failure due to degree-Ⅳ myelosuppression and the related literature review, we discussed the necessity of individualized administration of clinical chemotherapy. ResultsThe patient had grade-Ⅱ renal insufficiency before chemotherapy and did not undergo dihydropyrimidine dehydrogenase (DPYD) gene test. Myelosuppression occurred 10 days after oral chemotherapy drugs. The white blood cells, neutrophils and platelets decreased progressively, and then developed into degree-Ⅳ suppression. Finally the patient died of multiple organ failure. Conclusions Genetic variation and renal insufficiency may cause differences in drug metabolism. The reduced urinary excretion of guimet pyrimidine (CDHP), the inhibitors of dihydropyrimidine dehydrogenase which is the 5-fluorouracil (5-FU) metabolic enzyme, may lead to elevated plasma concentration of 5-FU, thereby increasing myelosuppression and other adverse reactions. If DPYD gene detection results show low enzyme activity, it can cause lethal toxicity through deceleration of 5-FU metabolism and high concentration of blood. DPYD gene dzetection should be performed if allowed, and individualized treatment plan should be formulated after comprehensive evaluation. The overall situation of the patients should be considered before treatment, and then individualized drugs should be administered.
ObjectiveTo analyze the diagnostic efficacy of colloidal gold immunochromatography assay (GICA) in detection of SARS-CoV-2.MethodsUsing GICA detection kits from three different manufacturers, 33 serum samples were collected from 12 patients with SARS-CoV-2 infection at different time and 45 serum samples from 45 patients without SARS-CoV-2 infection were collected from West China Hospital of Sichuan University from January to February, 2020.ResultsThe sensitivity, specificity, positive predictive value and negative predictive value of the three GICA reagents were 66.7% - 90.9%, 73.3% - 100.0%, 71.4% - 100.0% and 80.4% - 91.7% respectively. The rates of missed diagnosis and misdiagnosis were 9.1% - 33.3% and 0 - 26.7%, respectively. The positive rate decreased with titer increasing. The interference factors mainly included human immunodeficiency virus infection, high rheumatoid factor blood samples, and hemolysis.ConclusionClinical laboratories should pay attention to the differences in the detection ability and potential cross-reaction of different reagents, or use a combination of multiple antibodies.
ObjectiveTo systematically evaluate the efficacy and safety of computer-aided detection (CADe) and conventional colonoscopy in identifying colorectal adenomas and polyps. MethodsThe PubMed, Embase, Cochrane Library, Web of Science, WanFang Data, VIP, and CNKI databases were electronically searched to collect randomized controlled trials (RCTs) comparing the effectiveness and safety of CADe assisted colonoscopy and conventional colonoscopy in detecting colorectal tumors from 2014 to April 2023. Two reviewers independently screened the literature, extracted data, and evaluated the risk of bias of the included literature. Meta-analysis was performed by RevMan 5.3 software. ResultsA total of 9 RCTs were included, with a total of 6 393 patients. Compared with conventional colonoscopy, the CADe system significantly improved the adenoma detection rate (ADR) (RR=1.22, 95%CI 1.10 to 1.35, P<0.01) and polyp detection rate (PDR) (RR=1.19, 95%CI 1.04 to 1.36, P=0.01). It also reduced the missed diagnosis rate (AMR) of adenomas (RR=0.48, 95%CI 0.34 to 0.67, P<0.01) and the missed diagnosis rate (PMR) of polyps (RR=0.39, 95%CI 0.25 to 0.59, P<0.01). The PDR of proximal polyps significantly increased, while the PDR of ≤5 mm polyps slightly increased, but the PDR of >10mm and pedunculated polyps significantly decreased. The AMR of the cecum, transverse colon, descending colon, and sigmoid colon was significantly reduced. There was no statistically significant difference in the withdrawal time between the two groups. Conclusion The CADe system can increase the detection rate of adenomas and polyps, and reduce the missed diagnosis rate. The detection rate of polyps is related to their location, size, and shape, while the missed diagnosis rate of adenomas is related to their location.