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.
Based on the capacitance coupling principle, we studied a capacitive way of non-contact electrocardiogram (ECG) monitoring, making it possible to obtain ECG on the condition that a patient is habilimented. Conductive fabric with a good electrical conductivity was used as electrodes. The electrodes fixed on a bed sheet is presented in this paper. A capacitance comes into being as long as the body gets close to the surface of electrode, sandwiching the cotton cushion, which acts as dielectric. The surface potential generated by heart is coupled to electrodes through the capacitance. After being processed, the signal is suitable for monitoring. The test results show that 93.5% of R wave could be detected for 9 volunteers and ECG with good signal quality could be acquired for 2 burnt patients. Non-contact ECG is harmless to skin, and it has advantages for those patients to whom stickup electrodes are not suitable. On the other hand, it is convenient to use and good for permanent monitoring.
Blood glucose monitoring has become the weakest point in the overall management of diabetes in China. Long-term monitoring of blood glucose levels in diabetic patients has become an important means of controlling the development of diabetes and its complications, so that technological innovations in blood glucose testing methods have far-reaching implications for accurate blood glucose testing. This article discusses the basic principles of minimally invasive and non-invasive blood glucose testing assays, including urine glucose assays, tear assays, methods of extravasation of tissue fluid, and optical detection methods, etc., focuses on the advantages of minimally invasive and non-invasive blood glucose testing methods and the latest relevant results, and summarizes the current problems of various testing methods and prospects for future development trends.
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.
Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.
The present circuit was designed to apply to human tissue impedance tuning and matching device in ultrashort wave treatment equipment. In order to judge if the optimum status of circuit parameter between energy emitter circuit and accepter circuit is in well syntony, we designed a high frequency envelope detect circuit to coordinate with automatic adjust device of accepter circuit, which would achieve the function of human tissue impedance matching and tuning. Using the sampling coil to receive the signal of amplitude-modulated wave, we compared the voltage signal of envelope detect circuit with electric current of energy emitter circuit. The result of experimental study was that the signal, which was transformed by the envelope detect circuit, was stable and could be recognized by low speed Analog to Digital Converter (ADC) and was proportional to the electric current signal of energy emitter circuit. It could be concluded that the voltage, transformed by envelope detect circuit can mirror the real circuit state of syntony and realize the function of human tissue impedance collecting.
The requirement for unconstrained monitoring of heartbeat during sleep is increasing, but the current detection devices can not meet the requirements of convenience and accuracy. This study designed an unconstrained ballistocardiogram (BCG) detection system using acceleration sensor and developed a heart rate extraction algorithm. BCG is a directional signal which is stronger and less affected by respiratory movements along spine direction than in other directions. In order to measure the BCG signal along spine direction during sleep, a 3-axis acceleration sensor was fixed on the bed to collect the vibration signals caused by heartbeat. An approximate frequency range was firstly assumed by frequency analysis to the BCG signals and segmental filtering was conducted to the original vibration signals within the frequency range. Secondly, to identify the true BCG waveform, the accurate frequency band was obtained by comparison with the theoretical waveform. The J waves were detected by BCG energy waveform and an adaptive threshold method was proposed to extract heart rates by using the information of both amplitude and period. The accuracy and robustness of the BCG detection system proposed and the algorithm developed in this study were confirmed by comparison with electrocardiogram (ECG). The test results of 30 subjects showed a high average accuracy of 99.21% to demonstrate the feasibility of the unconstrained BCG detection method based on vibration acceleration.
ObjectiveTo investigate diagnosis, gene detection, and treatment principle of medullary thyroid carcinoma.Method The relevant literatures and guidelines about diagnosis and treatment of medullary thyroid carcinoma were summarized and analyzed retrospectively. Resultsmedullary thyroid carcinoma was given priority to surgical treatment. hereditary medullary cancer could be prophylactic thyroidectomy by the RET gene test results. advanced progressive medullary thyroid carcinoma, could be treated by palliative surgery, external radiotherapy, or systemic treatment with the tyrosine kinase inhibitor. ConclusionsPrognosis of medullary thyroid carcinoma is worse, and occurrence of early metastasis is easy. so the first operation should be thoroughgoing. and the operation timing of prophylactic total thyroidectomy for hereditary medullary cancer could be determined by the results of RET gene detection to achieving early cure.