west china medical publishers
Keyword
  • Title
  • Author
  • Keyword
  • Abstract
Advance search
Advance search

Search

find Keyword "检测" 173 results
  • Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm

    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.

    Release date:2023-08-23 02:45 Export PDF Favorites Scan
  • An improved peak extraction method for heart rate estimation

    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.

    Release date:2019-12-17 10:44 Export PDF Favorites Scan
  • Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network

    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.

    Release date:2024-10-22 02:39 Export PDF Favorites Scan
  • Fast Implementation Method of Protein Spots Detection Based on CUDA

    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.

    Release date: Export PDF Favorites Scan
  • Application of failure mode and effect analysis in the detection of severe acute respiratory syndrome coronavirus 2 nucleic acid

    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.

    Release date:2021-09-24 01:23 Export PDF Favorites Scan
  • Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks

    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.

    Release date:2024-10-22 02:33 Export PDF Favorites Scan
  • Head and Neck Tumor Segmentation Based on Augmented Gradient Level Set Method

    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.

    Release date: Export PDF Favorites Scan
  • Clinical Application of Combined Detection of Serum Carbohydrate Antigen 199, Alanine Aminotransferase, and Gamma-glutamyl Transferase in the Diagnosis of Hyperthyroid Liver Damage

    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.

    Release date: Export PDF Favorites Scan
  • 三磷酸腺苷荧光检测在医疗器械清洗质量控制中的应用

    目的评价三磷酸腺苷(ATP)荧光检测技术在医疗器械清洗中的运用,探讨ATP荧光检测的有效性和优越性。 方法对2013年6月-8月消毒供应中心依照国家标准要求肉眼观察合格的700件手工清洗和机械清洗器械,按照器械的表面结构和复杂程度分为简单表面器械、复杂表面器械、管腔器械,使用ATP荧光检测技术进行ATP残留量测定,对测定数据进行记录和分析。 结果手工清洗组,简单规则表面器械的ATP平均值为81 RLU,复杂表面器械的ATP平均值26 RLU,管腔器械中的ATP平均值为17 RLU;在机械清洗组,简单规则表面器械的ATP平均值为15 RLU,复杂表面器械中的ATP平均值25 RLU,管腔器械中的ATP平均值为17 RLU,均在参考的合格值范围以内。 结论执行标准的工作流程,无论是手工清洗还是机械清洗,均能保证清洗质量;机械清洗的严谨性更好,可以避免主观性和依从性问题。ATP荧光检测可以作为日常监测工具并具有科学性。

    Release date: Export PDF Favorites Scan
  • 循环肿瘤细胞在乳腺癌患者中的临床意义

    【摘要】乳腺癌在肿瘤发展早期就可远处播散,乳腺癌术后复发和转移是影响预后的主要因素, 在外周血中发现肿瘤细胞即是重要证据, 因而提出了循环肿瘤细胞(circulating tumor cell, CTC)的概念。检测乳腺癌CTC 有助于准确的判断预后,并为及时评判和监测治疗效果提供依据,有利于患者的个体化治疗,具有重要的临床意义。

    Release date:2016-09-08 09:51 Export PDF Favorites Scan
18 pages Previous 1 2 3 ... 18 Next

Format

Content