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.
The prevalence of cardiovascular disease in our country is increasing, and it has been a big problem affecting the social and economic development. It has been demonstrated that early intervention of cardiovascular risk factors can effectively reduce cardiovascular disease-caused mortality. Therefore, extensive implementation of cardiovascular testing and risk factor screening in the general population is the key to the prevention and treatment of cardiovascular disease. However, the categories of devices available for quick cardiovascular testing are limited, and in particular, many existing devices suffer from various technical problems, such as complex operation, unclear working principle, or large inter-individual variability in measurement accuracy, which lead to an overall low popularity and reliability of cardiovascular testing. In this study, we introduce the non-invasive measurement mechanisms and relevant technical progresses for several typical cardiovascular indices (e.g., peripheral/central arterial blood pressure, and arterial stiffness), with emphasis on describing the applications of biomechanical modeling and simulation in mechanism verification, analysis of influential factors, and technical improvement/innovation.
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.
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.
Bacterial biofilm is the key problem of chronic wound infection and difficult healing. How to prevent and control bacterial biofilm and improve the prognosis of chronic wound has become a research hotspot in the field of wound care. This paper will summarize from the following aspects: four major stages in the process of chronic wound bacteria biofilm formation (surface adhesion, formation of small colonies, biofilm maturation, and dispersion and separation); characteristics of host immune response in the presence of biofilms; morphological, microbiological, and molecular detection methods for biofilms; and progress in in vitro trials, animal trials, clinical trials, and new therapeutic methods of biofilm. The purpose of this review is to provide evidence for the treatment of biofilms for chronic wounds.
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%.
ObjectiveMitochondrial encephalomyopathy is a series of diseases that drag in central nervous system and generalized muscles. The pathogenesis of the disease is lack of ATP for the dysfunction of mitochondria. The misdiagnosis rate of the disease is high and the purpose of this study is to improve the recognition and diagnosis of mitochondrial encephalomyopathy and thus, clinicians could take rational treatment in time and improve patients' prognosis. MethodsThe clinical data of 11 patients with mitochondrial encephalomyopathy were analyzed including the physical data, clinical presentations, laboratory data, neuroimaging findings, muscle biopsy, genetic testing, treatment and prognosis. Reviewing literature and summarizing the clinical characteristics of mitochondrial encephalomyopathy. ResultsAmong the 11 patients with mitochondrial encephalomyopathy, the mean age was 17 years old. 1 case had family history. 7 cases were misdiagnosed in the first clinic visit. The onset of the 11 cases, 9 were paroxysmal and 2 were hidden. In the course, 10 cases had an epileptic seizure. Among the 9 cases who took the determination of serum lactate, 8 was in high level.9 cases had MRI examination and all found abnormality, 10 patients had EEG examination, and 9 cases found abnormality, 6 cases had muscle biopsy and all found the ragged red fiber(RRF). 6 cases had molecular genetic testing, and all found mutations in mitochondrial DNA. Among the 10 cases who had epileptic seizure, 3 cases can be controlled with single kind of antiepileptic drug. The other 7 cases had a recurrence of epilepsy with single kind of antiepileptic drugs, but can be cotrolled after drug adjusting or drug combination. ConclusionMitochondrial encephalomyopathy is often accompanied by seizure, which is usually found in children, and also often accompanied by systemic muscle symptoms. The clinical manifestations of the disease is not typical, but is complex and varied symptoms, so the clinical misdiagnosis rate is high. Mitochondrial encephalomyopathy mainly involves the main intracranial artery distribution area (parietal lobe, temporal lobe, occipital lobe, etc.) in central nervous system, and can involve more than one part. Patients with mitochondrial myopathy brain are usually detected the elevation of serum lactate levels, but if the lactic acid level is normal, it does not rule out the possibility of the disease, the confirmation of the disease is mainly by muscle biopsy or genetic tests. There is no specific treatment for mitochondrial encephalomyopathy till now, and it still give priority to symptomatic treatment. And the prognosis is poorer.
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.