Biventricular segmentation using U-Net incorporating improved Transformer and convolutional channel attention module / 中国医学物理学杂志

A U-Net incorporating improved Transformer and convolutional channel attention module is designed for biventricular segmentation in MRI image.By replacing the high-level convolution of U-Net with the improved Transformer,the global feature information can be effectively extracted to cope with the challenge of poor segmentation performance due to the complex morphological variation of the right ventricle.The improved Transformer incorporates a fixed window attention for position localization in the self-attention module,and aggregates the output feature map for reducing the feature map size;and the network learning capability is improved by increasing network depth through the adjustment of multilayer perceptron.To solve the problem of unsatisfactory segmentation performance caused by blurred tissue edges,a feature aggregation module is used for the fusion of multi-level underlying features,and a convolutional channel attention module is adopted to rescale the underlying features to achieve adaptive learning of feature weights.In addition,a plug-and-play feature enhancement module is integrated to improve the segmentation performance which is affected by feature loss due to channel decay in the codec structure,which guarantees the spatial information while increasing the proportion of useful channel information.The test on the ACDC dataset shows that the proposed method has higher biventricular segmentation accuracy,especially for the right ventricle segmentation.Compared with other methods,the proposed method improves the DSC coefficient by at least 2.83%,proving its effectiveness in biventricular segmentation.

Motor imagery EEG classification and recognition based on differential entropy and convolutional neural network / 中国医学物理学杂志

To address the problem of low accuracy in multi-classification recognition of motor imagery electroencephalogram(EEG)signals,a recognition method is proposed based on differential entropy and convolutional neural network for 4-class classification of motor imagery.EEG signals are extracted into 4 frequency bands(Alpha,Beta,Theta,and Gamma)through the filter,followed by the computation of differential entropy for each frequency band.According to the spatial characteristics of brain electrodes,the data structure is reconstructed into three-dimensional EEG signal feature cube which is input into convolutional neural network for 4-class classification.The method achieves an accuracy of 95.88%on the BCI Competition IV-2a public dataset.Additionally,a 4-class classification motor imagery dataset is established in the laboratory for the same processing,and an accuracy of 94.50%is obtained.The test results demonstrate that the proposed method exhibits superior recognition performance.

Study on Objective Recognition and Color Classification of Sublingual Veins / 中国中医药信息杂志

Objective To explore the method of objective identification of color information in sublingual veins diagnosis of TCM.Methods Combined with computer vision,compact fully convolution networks(CFCNs)and 19 deep learning classification models were used for study,and a double pulse rectangle algorithm was designed as a means of segmentation and recognition of sublingual veins and color information extraction.Results The accuracy of segmentation of tongue bottom obtained by the method of removing reflection + data expanding + data post-processing was 0.955 9,F1 value was 0.947 3,and mIoU value was 0.900 0.The accuracy of segmentation of sublingual veins obtained by the method of removing reflection + tongue input + data expanding + corrosion expansion was 0.778 4,F1 value was 0.738 3 and mIoU value was 0.585 1,which were obviously superior to the current classic or improved U-net model.On the color classification of sublingual veins,the best classification model was DenseNet161-bc-early_stopping with an accuracy rate of 0.803 7.Conclusion The deep learning method has a certain effect on identifying the color information of sublingual veins in TCM,which provides a new method for the research of quantitative color detection technology of sublingual veins diagnosis in TCM.

Research on Pulse Signal Recognition Based on Weighted Soft Voting Fusion Model / 世界科学技术-中医药现代化

Pulse recognition is an important part of the objectification and intelligence of TCM.This non-invasive and fast diagnostic method has great clinical value,however,data imbalance and cumbersome feature extraction are still challenging problems.The feature vectors were extracted from the one-dimensional pulse signal obtained after the Butterworth bandpass filter using the tsfresh library.And 9 columns of medical auxiliary features selected by exploratory data analysis were added.The feature filtering is performed jointly to derive 21 columns of feature vectors,which are used as input to the weighted soft voting fusion model.The data imbalance problem is solved by Borderline SMOTE algorithm.Construct a weighted soft-voting fusion model based on four types of machine learning:XGBoost,RF,LGBM,and GBDT.Eventually,the models will output specific pulse categories and demonstrate the performance by evaluating the metrics accuracy,precision,recall and F1 score.The experimental results show that the screened 21 feature vectors for a total of six types of pulse signal test sets achieve an accuracy of 90.04%in the five-fold cross-validation and take only 65.9466 seconds.It can provide a more accurate and intelligent auxiliary reference for pulse signal recognition,with lower operational complexity and higher accuracy compared to commonly used pulse recognition methods.The shorter training time also makes it more clinically useful in the recognition of multiple pulse signals.

Medical diagnosis of lung nodules by fusing CT image feature extraction and improved SVM algorithm / 医疗卫生装备

Objective To propose a lung nodule diagnosis method based on CT image feature extraction and improved support vector machine(SVM)algorithm to enhance the accuracy and efficiency of automatic identification of lung nodules.Methods A cascade feature extraction method combining deep learning-based feature extraction and traditional manual extraction was used for CT image feature extraction,and the extracted features were input into an improved SVM algorithm to complete automated identification of lung nodules,using a multiple kernel learning support vector machine(MKL-SVM)algorithm and a particle swarm optimization(PSO)algorithm that integrated simulated annealing(SA)algorithm for parameter optimizing.The performance of cascade features was tested by comparing traditional feature extraction,deep learning-based feature extraction and cascade feature extraction.Comparison tests were performed using single kernel functions(RBF kernel,Sigmoid kernel and polynomial kernel functions)to validate the performance of the MKL-SVM algorithm.Tests were carried out using SVM functions with Sigmoid kernel to compare the fitness curves of the PSO algorithm and the PSO-SA algorithm for optimization to validate the effectiveness of the PSO-SA algorithm.Comparison analyses were conducted with the existing computer aided diagnosis(CAD)models of lung under the same dataset to verify the diagnostic efficacy of the proposed model of cascade features combined with improved MKL-SVM(cascade features with improved MKL-SVM,CF with MKL-SVM).Results The performance test results showed that cascade feature extraction had the F value with a mean value of 0.934 1,a maximum value of 0.957 3,a minimum value of 0.919 5 and a median value of 0.939 7,which behaved better in accuracy than manual feature extraction and deep learning-based feature extraction.The kernel function comparison test results indicated that the MKL-SVM algorithm had the best diagnostic performance with the mean value of F value of 0.924 3,the maximum value of 0.935 0 and the AUC value of 0.987 3.The Sigmoid kernel comparison test results found that PSO-SA al-gorithm had the best fitness value of 0.943 7,which gained advantages over the PSO algorithm.The model comparison test revealed that compared with the lung CAD model,the CF+MKL-SVM model had advantages in generalization ability,AUC value(0.9845),the values of all the indexes(all higher than 0.9),specificity and precision.Conclusion The proposed method can be used for automatic recognition of lung cancer and enhances the accuracy for detecting lung cancer.

m / 中国生物化学与分子生物学报

N6, 2′ -O-dimethyladenosine (m

Research progress on attention level evaluation based on electroencephalogram signals / 生物医学工程学杂志

Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.

A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction / 生物医学工程学杂志

Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.

A study to identify obstructive sleep apnea syndrome based on 24 h ambulatory blood pressure data / 生物医学工程学杂志

Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.

Key technologies for intelligent brain-computer interaction based on magnetoencephalography / 生物医学工程学杂志

Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.

A three dimensional convolutional neural network pulmonary nodule detection algorithm based on the multi-scale attention mechanism / 生物医学工程学杂志

Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.

A review of researches on decoding algorithms of steady-state visual evoked potentials / 生物医学工程学杂志

Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.

iSucc-PseAAC:Prediction of Lysine Succinylation Modification Sites Based on Ensemble Machine Learning / 中国生物化学与分子生物学报

Lysine succinylation is a novel post-translational modification, which plays an important role in regulating distinct cellular functions control, therefore it is necessary to accurately identify succinylation sites in proteins. As traditional experiments consume material and financial resources, prediction by calculation is an efficient method being proposed recently. In this study, we developed a new prediction method iSucc-PseAAC, which uses a variety of classification algorithms combined with different feature extraction methods. Moreover, it is found that under the feature extraction based on coupled sequence (PseAAC), the classification effect of support vector machine is the best, and it could be combined with ensemble learning to solve the problem of data imbalance. Compared with the existing methods, iSucc-PseAAC has more significance and practicality in predicting lysine succinylation sites.

Análisis de texturas homogéneas para la estimación volumétrica de la materia cerebral por tomografía computarizada / Analysis of Homogeneous Textures for the VolumetricEstimation of Brain Matter by Computed Tomography

Las aplicaciones de análisis de texturas y su extracción de características son consideradas tendencias de investigación en las neurociencias. La textura como método de análisis de imágenes ha mostrado resultados prometedores en la detección de lesiones visibles y no visibles, y en estudios de tomografía computarizada (TC) son escasos. La presente investigación tiene como objetivo determinar la aplicabilidad del procesamiento automático de índices de texturas homogéneas en la estimación volumétrica de la sustancia gris cerebral en imágenes de TC craneal. Para ello se utilizaron imágenes artificiales con regiones predefinidas y la selección de imágenes de TC en los pacientes con indicaciones previas de TC de cráneo. Dos pasos fundamentales son conducidos para la implementación de este enfoque. Como resultado se obtuvo un método automático de reconocimiento de patrones sin ventanas por medio de la extracción de características de textura homogéneas a través de la matriz de co-ocurrencia(AU)

Texture analysis applications and their extraction of features are considered research trends in neuroscience. Texture as a method of image analysis has shown promising results in the detection of visible and non-visible lesions, and in computed tomography (CT) studies they are scarce. The present research aims to determine the applicability of the automatic processing of homogeneous texture indices in the volumetric estimation of brain gray matter in cranial CT images. For this, artificial images with predefined regions and the selection of CT images were used in patients with previous indications for CT of the skull. Two fundamental steps are taken for the implementation of this approach. As a result, an automatic windowless pattern recognition method was obtained by means of the extraction of homogeneous texture characteristics through the co-occurrence matrix(AU)

Review on identity feature extraction methods based on electroencephalogram signals / 生物医学工程学杂志

Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.

Feature Extraction of Foot Movement Based on Plantar Pressure Distributions / 医用生物力学

Objective To analyze the statistical behavior of plantar pressure distribution, extract the characteristics of foot movement, and provide references for application of gait recognition in medical clinical diagnosis, rehabilitation training and public health. Methods The collected foot pressure data were prepossessed, statistical analysis on the data was performed, the footprint reconstruction was realized, and the pressure distribution rates of the footprints, segmented regions and each region were compared and analyzed, so as to decompose the foot motion characteristics. Results Based on the cross point of pressure peak curve in different regions, the plantar region was divided into toe region, metatarsal region, arch region and heel region, which could accurately extract the foot movement characteristics. Conclusions The peak plantar pressure is used to extract the characteristics of foot movement, which is divided into landing stage, whole foot contact stage, heel tiptoe stage and ground off stage.

Application of neural network autoencoder algorithm in the cancer informatics research / 生物工程学报

Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand challenge for bioinformatics research. As a typical neural network model, the autoencoder is able to efficiently learn the features of input data by unsupervised training method and further help integrate and mine the biological data. In this article, the primary structure and workflow of the autoencoder model are introduced, followed by summarizing the advances of the autoencoder model in cancer informatics using various types of biomedical data. Finally, the challenges and perspectives of the autoencoder model are discussed.

Recognition of producing areas of Angelicae Sinensis Radix based on structure-texture image decomposition / 中国中药杂志

The pharmacological effects of Angelicae Sinensis Radix from different producing areas are uneven. Accurate identification of its producing areas by computer vision and machine learning(CVML) is conducive to evaluating the quality of Angelicae Sinensis Radix. This paper collected the high-definition images of Angelicae Sinensis Radix from different producing areas using a digital camera to construct an image database, followed by the extraction of texture features based on the grayscale relationship of adjacent pixels in the image. Then a support vector machine(SVM)-based prediction model for predicting the producing areas of Angelicae Sinensis Radix was built. The experimental results showed that the prediction accuracy reached up to 98.49% under the conditions of the model training set occupying 80%, the test set occupying 20%, and the sampling radius(r) of adjacent pixels being 2. When the training set was set to 10%, the prediction accuracy was still over 93%. Among the three producing areas of Angelicae Sinensis Radix, Huzhu county, Qinghai province exhibited the highest error rate, while Heqing county, Yunnan province the lowest error rate. Angelicae Sinensis Radix from Minxian county, Gansu province and Huzhu county, Qinghai province were both wrongly attributed to Heqing county, Yunnan province, while most of those from Huzhu county, Qinghai province were misjudged as the samples produced in Minxian county, Gansu province. The method designed in this paper enabled the rapid and non-destructive prediction of the producing areas of Angelicae Sinensis Radix, boasting high accuracy and strong stability. There were definite morphological differences between Angelicae Sinensis Radix samples from Minxian county, Gansu province and those from Huzhu county, Qinghai province. The wrongly predicted samples from Minxian county, Gansu province and Huzhu city, Qinghai province shared similar morphological characteristics with those from Heqing county, Yunnan province. Most wrongly predicted samples from Heqing county, Yunnan province were similar to the ones from Minxian county, Gansu province in morphological characteristics.

Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks / 生物医学工程学杂志

Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise

Classification Model of Corneal Opacity Based on Digital Image Features / 中国医疗器械杂志

OBJECTIVE@#According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity.@*METHODS@#The cornea digital images of dead pigs were collected, part of the color features and texture features were extracted according to the previous experience, and the SVM multi classification model was established. The test results of the model were evaluated by precision, sensitivity and @*RESULTS@#In the classification of corneal opacity, the highest @*CONCLUSIONS@#The SVM multi classification model can classify the degree of corneal opacity.