As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.
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.
Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.
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.
ObjectiveTo use failure mode and effect analysis (FMEA) to check and improve the risk of severe acute respiratory syndrome coronavirus 2 nucleic acid detection, and explore the application effect of FMEA in the emergency inspection items.MethodsFMEA was used to sort out the whole process of severe acute respiratory syndrome coronavirus 2 nucleic acid detection from January 30 to February 21, 2020. By establishing the theme, setting up a team, analyzing the failure mode and potential influencing factors. Then calculate the risk priority number (RPN), formulate preventive measures and implement continuous improvement according to the analysis results.ResultsA total of 2 138 cases were included. After improvement, the number of potential failure modes has been reduced by 2 (17 vs.19); the value of total RPN decreased (3 527.49 vs. 1 858.28). There was significant difference in average RPN before and after improvement [(185.66±74.34) vs. (97.80±37.97); t=6.128, P<0.001].ConclusionsIn the early stage of emergency inspection items, using FMEA can systematically check the risk factors in the process, develop improvement measures. It also can effectively reduce the risk of severe acute respiratory syndrome coronavirus 2 nucleic acid detection in hospital.
Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor’s diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.
To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume. In large volume tumor segmentation, the proposed level set method can reduce manual intervention and enhance the segmentation accuracy. Tumor volume calculation results are close to the gold standard. From the experiment results, the augmented gradient based level set method has achieved accurate head and neck tumor segmentation. It can provide useful information to computer aided diagnosis.
ObjectiveTo investigate the significance of carbohydrate antigen 199 (CA199), alanine aminotransferase (ALT), gamma-glutamyl transferase (γ-GT) levels in the diagnosis of liver damage caused by hyperthyroidism. MethodA total of 106 patients confirmed to have hyperthyroid liver damage between February 2012 and February 2014 were selected to form the hyperthyroidism liver injury group (group A). Ninety-five hyperthyroidism patients without liver damage were regarded as the hyperthyroidism without liver injury group (group B). In the same period, 72 healthy subjects were designated to form the control group (group C). Automatic chemiluminescence detector was used to determine free triiodothyronine, free thyroid hormone and CA199, and automatic biochemical analyzer was adopted to measure the levels of γ-GT and ALT. Then we performed the statistical analysis. ResultsThe levels of serum CA199, γ-GT and ALT in group A were significantly higher than those in group B and group C, and the differences were statistically significant (P<0.05). CA199 and γ-GT levels in group B were significantly higher than those in group C (P<0.05). The area under the receiver operating characteristic curve for CA199, γ-GT, ALT was respectively 0.840, 0.895, and 0.818, the maximum Youden indexes were 0.593, 0.703, and 0.578, with the corresponding critical values 37.25 U/mL, 60.81 U/L, and 43.14 U/L, respectively. The parallel dectection of the three indexes improved Youden index to 0.763. ConclusionsCA199, γ-GT and ALT as diagnosis indexes of hyperthyroidism liver damage have good diagnostic value, and combined detection of the three indexes is more favorable for early diagnosis and prediction.