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.
Epilepsy is a complex and widespread neurological disorder that has become a global public health issue. In recent years, significant progress has been made in the use of wearable devices for seizure monitoring, prediction, and treatment. This paper reviewed the applications of invasive and non-invasive wearable devices in seizure monitoring, such as subcutaneous EEG, ear-EEG, and multimodal sensors, highlighting their advantages in improving the accuracy of seizure recording. It also discussed the latest advances in the prediction and treatment of seizure using wearable devices.
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 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.
Objective To observe the effect of BMSCs on the cardiac function in diabetes mellitus (DM) rats through injecting BMSCs into the ventricular wall of the diabetic rats and investigate its mechanism. Methods BMSCs isolated from male SD rats (3-4 months old) were cultured in vitro, and the cells at passage 5 underwent DAPI label ing. Thirty clean grade SD inbred strain male rats weighing about 250 g were randomized into the normal control group (group A), the DM group (group B), and the cell transplantation group (group C). The rats in groups B and C received high fat forage for 4 weeks and the intraperitoneal injection of 30 mg/kg streptozotocin to made the experimental model of type II DM. PBS and DAPI-labeledpassage 5 BMSCs (1 × 105/μL, 160 μL) were injected into the ventricular wall of the rats in groups B and C, respectively. After feeding those rats with high fat forage for another 8 weeks, the apoptosis of myocardial cells was detected by TUNEL, the cardiac function was evaluated with multi-channel physiology recorder, the myocardium APPL1 protein expression was detected by Western blot and immunohistochemistry test, and the NO content was detected by nitrate reductase method. Group C underwent all those tests 16 weeks after taking basic forage. Results In group A, the apoptosis rate was 6.14% ± 0.02%, the AAPL1 level was 2.79 ± 0.32, left ventricular -dP/dt (LV-dP/dt) was (613.27 ± 125.36) mm Hg/s (1 mm Hg=0.133 kPa), the left ventricular end-diastol ic pressure (LVEDP) was (10.06 ± 3.24) mm Hg, and the NO content was (91.54 ± 6.15) nmol/mL. In group B, the apoptosis rate was 45.71% ± 0.04%, the AAPL1 level 1.08 ± 0.24 decreased significantly when compared with group A, the LVdP/ dt was (437.58 ± 117.58) mm Hg/s, the LVEDP was (17.89 ± 2.35) mm Hg, and the NO content was (38.91±8.67) nmol/mL. In group C, the apoptosis rate was 27.43% ± 0.03%, the APPL1 expression level was 2.03 ± 0.22, the LV -dP/dt was (559.38 ± 97.37) mm Hg/ s, the LVEDP was (12.55 ± 2.87) mm Hg, and the NO content was (138.79 ± 7.23) nmol/ mL. For the above mentioned parameters, there was significant difference between group A and group B (P lt; 0.05), and between group B and group C (P lt; 0.05). Conclusion BMSCs transplantation can improve the cardiac function of diabetic rats. Its possible mechanismmay be related to the activation of APPL1 signaling pathway and the increase of NO content.
In order to improve the accuracy and efficiency of automatic seizure detection, the paper proposes a method based on improved genetic algorithm optimization back propagation (IGA-BP) neural network for epilepsy diagnosis, and uses the method to achieve detection of clinical epilepsy rapidly and effectively. Firstly, the method extracted the linear and nonlinear features of the epileptic electroencephalogram (EEG) signals and used a Gaussian mixture model (GMM) to perform cluster analysis on EEG features. Next, expectation maximization (EM) algorithm was used to estimate GMM parameters to calculate the optimal parameters for the selection operator of genetic algorithm (GA). The initial weights and thresholds of the BP neural network were obtained through using the improved genetic algorithm. Finally, the optimized BP neural network is used for the classification of the epileptic EEG signals to detect the epileptic seizure automatically. Compared with the traditional genetic algorithm optimization back propagation (GA-BP), the IGA-BP neural network can improve the population convergence rate and reduce the classification error. In the process of automatic detection of epilepsy, the method improves the detection accuracy in the automatic detection of epilepsy disorders and reduced inspection time. It has important application value in the clinical diagnosis and treatment of epilepsy.
Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective.