This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

To address the problem of large reconstruction errors in 3D pulse signals caused by excessively small out-of-plane displacement of the contact membrane in the existing traditional Chinese medicine fingertip tactile binocular vision detection technology, this study proposes a 3D pulse image detection method based on subtle motion magnification technology and explores its application in pulse pattern recognition. Firstly, a 3D pulse image detection system based on binocular vision to obtain pulse image signals is developed as experimental data. Then, the phase motion video magnification algorithm is used to amplify the original signals, and the amplified signals are reconstructed in three dimensions to obtain 3D pulse signals. On this basis, nine features are extracted from the 3D pulse signals and features selection is performed using a two-sample Kolmogorov-Smirnov test. Finally, machine learning algorithms such as decision trees and random forests are used to identify the five types of pulse conditions: deep pulse, intermittent pulse, flooding pulse, slippery pulse, and rapid pulse. The experimental results show that compared to the methods without subtle motion magnification technology, the proposed method significantly improves waveform clarity, amplitude stability, and periodic regularity. Meanwhile, the average accuracy in pulse pattern recognition reaches 96.29%±0.26%.
Algorithms ; Imaging, Three-Dimensional/methods* ; Pattern Recognition, Automated ; Medicine, Chinese Traditional ; Motion ; Humans ; Pulse ; Signal Processing, Computer-Assisted ; Machine Learning

Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.
Humans ; Medicine, Chinese Traditional ; Pattern Recognition, Automated ; Asian People ; Language ; Learning

This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers. A novel gesture recognition approach was proposed, which transformed surface electromyography signals into grayscale images and employed convolutional neural networks as classifiers. The method began by segmenting the active portions of the surface electromyography signals using an energy threshold approach. Temporal voltage values were then processed through linear scaling and power transformations to generate grayscale images for convolutional neural network input. Subsequently, a multi-view convolutional neural network model was constructed, utilizing asymmetric convolutional kernels of sizes 1 × n and 3 × n within the same layer to enhance the representation capability of surface electromyography signals. Experimental results showed that the proposed method achieved recognition accuracies of 98.11% for 13 gestures and 98.75% for 12 multi-finger movements, significantly outperforming existing machine learning approaches. The proposed gesture recognition method, based on surface electromyography grayscale images and multi-view convolutional neural networks, demonstrates simplicity and efficiency, substantially improving recognition accuracy and exhibiting strong potential for practical applications.
Electromyography/methods* ; Neural Networks, Computer ; Humans ; Gestures ; Signal Processing, Computer-Assisted ; Machine Learning ; Pattern Recognition, Automated/methods* ; Algorithms ; Convolutional Neural Networks

To explore the research hotspots and frontier directions of pyroptosis in the field of traditional Chinese medicine(TCM), the authors searched CNKI and Web of Science for literature related to pyroptosis in TCM, screened literature according to the search strategy and inclusion criteria, and analyzed the publication trend of the included literature. VOSviewer was used to draw author cooperation and keyword co-occurrence network diagrams, and CiteSpace was employed for keyword clustering, emergence, and timeline view. Finally, 507 Chinese literature and 464 English literature were included, and it was found that the number of Chinese and English literature was increasing rapidly year by year. The co-occurrence of the authors showed that in terms of Chinese literature, there was a representative research team composed of DU Guan-hua, WANG Shou-bao and FANG Lian-hua, and for English literature, the representative research team was composed of XIAO Xiao-he, BAI Zhao-fang and XU Guang. The network visualization of Chinese and English keywords revealed that inflammation, apoptosis, oxidative stress, autophagy, organ damage, fibrosis, atherosclerosis, and ischemia-reperfusion injury were the primary research diseases and pathological processes in TCM; berberine, resveratrol, puerarin, na-ringenin, astragaloside Ⅳ, and baicalin were the representative active ingredients; NLRP3/caspase-1/GSDMD, TLR4/NF-κB/NLRP3, and p38/MAPK signaling pathways were the main research pathways. Keyword clustering, emergence, and timeline analysis indicated that the pyroptosis research in TCM focused on the mechanism of TCM monomers and compounds intervening in diseases and pathological processes. Pyroptosis is a research hotspot in the area of TCM, and the current discussion mainly focuses on the mechanism of the therapeutic effect of TCM.
Pyroptosis ; Medicine, Chinese Traditional ; NLR Family, Pyrin Domain-Containing 3 Protein ; Pattern Recognition, Automated ; Apoptosis

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.
Humans ; Moxibustion ; Carbuncle ; Pattern Recognition, Automated ; Acupuncture Therapy ; Acupuncture Points

Bibliometric and scientific knowledge graph methods were used to analyze the research status and hot spots of acupuncture-moxibustion in treatment of myofascial pain syndrome (MPS) and explore its development trend. The articles of both Chinese and English versions relevant to MPS treated by acupuncture-moxibustion were searched in CNKI, VIP, Wanfang, SinoMed and WOS from the database inception to March 20, 2023. Using Excel2016, CiteSpace6.2.R2 and VOSviewer1.6.18, the visual analysis was conducted by means of the cooperative network, keyword co-occurrence, keyword timeline, keyword emergence, etc. From Chinese databases and WOS database, 910 Chinese articles and 300 English articles were included, respectively. The annual publication volume showed an overall rising trend. Literature output of English articles was concentrated in Spain, China, and the United States, of which, there was less cross-regional cooperation. In the keyword analysis, regarding acupuncture-moxibustion therapy, Chinese articles focused on "acupuncture", "electroacupuncture" and "acupotomy"; while, "dry needling" and "injection" were dominated for English one. Clinical study was the current hot spot in Chinese databases, in comparison, the randomized controlled double-blind clinical trial was predominant in WOS. Both Chinese and English articles were limited in the report of mechanism research. The cooperation among research teams should be strengthened to conduct comparative research, dose-effect research and effect mechanism research with different methods of acupuncture-moxibustion involved so that the evidences can be provided for deeper exploration.
Humans ; Moxibustion ; Pattern Recognition, Automated ; Acupuncture Therapy ; Myofascial Pain Syndromes/therapy* ; Electroacupuncture

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
Pattern Recognition, Automated ; Medical Informatics

Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
Algorithms ; Brain ; physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Pattern Recognition, Automated

According to the requirements for developing the quality control technology in Chinese medicine( CM) manufacturing process and the practical scenarios in applying a new generation of artificial intelligence to CM industry,we present a method of constructing the knowledge graph( KG) for CM manufacture to solve key problems about quality control in CM manufacturing process.Based on the above,a " pharmaceutical industry brain" model for CM manufacture has been established. Further,we propose founding the KG-based methodology for quality control in CM manufacturing process,and briefly describe the design method,system architecture and main functions of the KG system. In this work,the KG for manufacturing Shuxuening Injection( SXNI) was developed as a demonstration study. The KG version 1. 0 platform for intelligent manufacturing SXNI has been built,which could realize technology leap of the quality control system in CM manufacturing process from perceptual intelligence to cognitive intelligence.
Artificial Intelligence ; Drug Industry/standards* ; Drugs, Chinese Herbal/standards* ; Medicine, Chinese Traditional/standards* ; Pattern Recognition, Automated ; Quality Control ; Technology, Pharmaceutical