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.
Objective Using the evidence-based management to manage the flexible endoscope based on the data collected by information means, to reduce the rate of serious faults and control maintenance costs. Methods From January 2017 to December 2018, we collected and analyzed the flexible endoscope data of the use, leak detection, washing and disinfection, and maintenance between 2015 and 2018 from the Gastroenterology Department of our hospital. Three main causes of flexible endoscope faults were found: delayed leak detection, irregular operation, and physical/chemical wastage. Management schemes (i.e., leak detection supervision, fault tracing, and reliability maintenance) were enacted according to these reasons. These schemes were improved continuously in the implementation. Finally, we calculated the changes of the fault rate of each grade and the maintenance cost. Results By two years management practice, compared with those from 2015 to 2016, the annual rates of grade A and grade C faults of flexible endoscope from 2017 to 2018 decreased by 10.3% and 16.7% respectively, and the annual average maintenance cost fell by 53.2%. Conclusions The maintenance costs of flexible endoscope could be effectively controlled by enacting and implementing a series of targeted management schemes based on the data from the root causes of faults applying the evidence-based management. Evidence-based management based on data has a broad application prospect in the management of medical equipment faults.
It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
ObjectiveTo investigate the effectiveness of probe-based near infrared autofluorescence (AF) technology in the identification and functional protection of parathyroid gland (PG) during endoscopic total thyroidectomy. MethodsWe retrospectively collected the clinical data of 160 patients who underwent total thyroidectomy with bilateral central compartment lymph node dissection due to papillary thyroid carcinoma in Chongqing General Hospital from 1 July 2023 to 31 January 2024. Among them, 80 patients who used probe-based near infrared AF technology to identify the PGs were categorized as the AF group, 80 patients who used naked eye (NE) to identify the PGs were categorized as the NE group. The number of PGs identified, inadvertently removed, preserved in situ and autotransplanted, the incidence of postoperative hypoparathyroidism, and operative time were compared between the two groups. ResultsThe incidence of transient hypoparathyroidism was significantly lower in the AF group than that of the NE group [21.25% (17/80) vs. 43.75% (35/80), χ2=9.231, P=0.002], with no cases of permanent hypoparathyroidism in either group. The AF group had significantly more PGs identified and preserved in situ than the NE group (P<0.05) , but had significantly fewer PGs inadvertently removed and autotransplanted than the NE group (P<0.05). The AF group identified the first PG earlier than the NE group (4 min vs. 5 min, P<0.001). But there was no statistically difference in the operative time between the two groups (90 min vs. 94 min, P=0.052). ConclusionThe probe-based near infrared AF technology can help surgeons better identify and protect PGs during surgery, reducing the incidence of postoperative transient hypoparathyroidism.
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.