Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061299
Jindai Huang, Dianchen Zhang
Traditional methods for testing drug sensitivity values include broth dilution methods including micro broth dilution and macro broth dilution methods, agar dilution methods, E-test methods and paper diffusion methods[1]. These traditional methods have a common shortcoming, that is, the test often takes a long time to obtain drug sensitivity results, which can lead to missed diagnosis, misdiagnosis and delayed treatment.This paper describes how the electrical impedance counting method was used for the study of drug sensitivity testing and what advantages it has over traditional drug sensitivity testing. The impedance counting method still uses the broth dilution method and the existing parameter MIC (minimum inhibitory concentration)[1], which is the most relied upon parameter to guide the use of antibiotics, as an indicator, with the Coulter principle[2] technique as the underlying principle, and the IT system to collect, process and analyze the data. The MIC values were obtained by calculating the number of bacteria at different doses of antibiotics, analyzing the graphical changes such as growth indices, using software algorithms and referring to the CLSI M100[3] standard to discriminate the drug susceptibility of bacteria. The results show that the electrical impedance counting method will provide us with a more rapid and effective method for drug sensitivity analysis, and can be used in medical institutions and related medical industries for drug sensitivity analysis, which is of great significance for rapid and accurate clinical drug use and new antibiotic research.
{"title":"Drug Resistance Testing Using Electrical Impedance Counting Method","authors":"Jindai Huang, Dianchen Zhang","doi":"10.1109/ISBP57705.2023.10061299","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061299","url":null,"abstract":"Traditional methods for testing drug sensitivity values include broth dilution methods including micro broth dilution and macro broth dilution methods, agar dilution methods, E-test methods and paper diffusion methods[1]. These traditional methods have a common shortcoming, that is, the test often takes a long time to obtain drug sensitivity results, which can lead to missed diagnosis, misdiagnosis and delayed treatment.This paper describes how the electrical impedance counting method was used for the study of drug sensitivity testing and what advantages it has over traditional drug sensitivity testing. The impedance counting method still uses the broth dilution method and the existing parameter MIC (minimum inhibitory concentration)[1], which is the most relied upon parameter to guide the use of antibiotics, as an indicator, with the Coulter principle[2] technique as the underlying principle, and the IT system to collect, process and analyze the data. The MIC values were obtained by calculating the number of bacteria at different doses of antibiotics, analyzing the graphical changes such as growth indices, using software algorithms and referring to the CLSI M100[3] standard to discriminate the drug susceptibility of bacteria. The results show that the electrical impedance counting method will provide us with a more rapid and effective method for drug sensitivity analysis, and can be used in medical institutions and related medical industries for drug sensitivity analysis, which is of great significance for rapid and accurate clinical drug use and new antibiotic research.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134517914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061319
Yingying Wang, Guang-Jiao Zhou, Xiao-Wei Li
Objective: The extraction process of Chuilian Jianpi granules was optimized.Methods: The extraction process of Chuilian Jianpi granules was optimized by box-Behnken response surface method, and the comprehensive score was calculated based on the content of geniposide in the extract and the yield of extract.Results: The optimal extraction process was as follows: add water volume multiple of 10 times, cook twice, cook for 3h each time. Under this condition, the comprehensive score was 96.816. The comprehensive score of the validation test was 99.07(RSD=0.54%, n=3), and the results were basically consistent.Conclusion: The process is stable and feasible, which can provide basis for the preparation of Chuilian Jianpi granules.
{"title":"Box-Behnken Designs for the Optimization of the Ethanol Extraction Process for Chuilian Jianpi Granules","authors":"Yingying Wang, Guang-Jiao Zhou, Xiao-Wei Li","doi":"10.1109/ISBP57705.2023.10061319","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061319","url":null,"abstract":"Objective: The extraction process of Chuilian Jianpi granules was optimized.Methods: The extraction process of Chuilian Jianpi granules was optimized by box-Behnken response surface method, and the comprehensive score was calculated based on the content of geniposide in the extract and the yield of extract.Results: The optimal extraction process was as follows: add water volume multiple of 10 times, cook twice, cook for 3h each time. Under this condition, the comprehensive score was 96.816. The comprehensive score of the validation test was 99.07(RSD=0.54%, n=3), and the results were basically consistent.Conclusion: The process is stable and feasible, which can provide basis for the preparation of Chuilian Jianpi granules.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121756301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061306
Zhimin Gao, Tianyou Yu
Deep learning has achieved a great success in various fields, such as image classification, semantic segmentation and so on. But its excellent performance tends to rely on a large amount of data annotations that are hard to collect, especially in dense prediction tasks, like medical image segmentation. Semi-supervised learning (SSL), as a popular solution, relieves the burden of labeling. However, most of current semi-supervised medical image segmentation methods treat each pixel equally and underestimate the importance of indistinguishable and low-proportion pixels which are drowned in easily distinguishable but high-proportion pixels. We believe that these regions with less attention tend to contain crucial and indispensable information to obtain better segmentation performance. Therefore, we propose a simple but effective method for semi-supervised medical image segmentation task via enforcing low-confidence consistency and applying low-confidence class separation. Concretely, we separate low- and high-confidence pixels via the maximum probability values of model’s predictions and only low-confidence pixels are kept. For these remaining pixels, in the mean teacher framework, consistency is enforced for invariant predictions between student and teacher in the output level, and class separation is applied for promoting representations close to corresponding class prototypes in the feature level. We evaluated the proposed approach on two public datasets of cardiac, achieving a higher performance than the state-of-the-art semi-supervised methods on both datasets.
{"title":"Semi-supervised Medical Image Segmentation with Low-Confidence Consistency and Class Separation","authors":"Zhimin Gao, Tianyou Yu","doi":"10.1109/ISBP57705.2023.10061306","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061306","url":null,"abstract":"Deep learning has achieved a great success in various fields, such as image classification, semantic segmentation and so on. But its excellent performance tends to rely on a large amount of data annotations that are hard to collect, especially in dense prediction tasks, like medical image segmentation. Semi-supervised learning (SSL), as a popular solution, relieves the burden of labeling. However, most of current semi-supervised medical image segmentation methods treat each pixel equally and underestimate the importance of indistinguishable and low-proportion pixels which are drowned in easily distinguishable but high-proportion pixels. We believe that these regions with less attention tend to contain crucial and indispensable information to obtain better segmentation performance. Therefore, we propose a simple but effective method for semi-supervised medical image segmentation task via enforcing low-confidence consistency and applying low-confidence class separation. Concretely, we separate low- and high-confidence pixels via the maximum probability values of model’s predictions and only low-confidence pixels are kept. For these remaining pixels, in the mean teacher framework, consistency is enforced for invariant predictions between student and teacher in the output level, and class separation is applied for promoting representations close to corresponding class prototypes in the feature level. We evaluated the proposed approach on two public datasets of cardiac, achieving a higher performance than the state-of-the-art semi-supervised methods on both datasets.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129948867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061321
Xiaoyan Liu, Zhiwei Xu, Guangwen Liu, Limin Liu
In the compound selection process of anti-cancer drugs, safety properties such as drug activity and pharmacokinetics need to be considered simultaneously. To construct a more complete and precise drug screening mechanism, this paper proposed an intelligent compound selection method for anti-cancer drugs based on multi-objective optimization. The proposed model is executed in the MapReduce environment. Quantitatively analyze the biological activity of the compound, and qualitatively analyze the properties of pharmacokinetics and safety properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) to build a multi-objective optimization model. Guided by Pareto optimization theory, the set of non-inferior solution values was determined, and the compound combination that satisfies the optimization goal was found by genetic optimization. On this basis, a Monte Carlo hypothesis test was used to determine the equipped range of the compounds. Finally, an example of the compound selection of anti-breast cancer drugs is given, and the experimental evaluation proves that the algorithm can screen compounds limitedly, which provides a basis for anti-cancer drug synthesis.
{"title":"Intelligent Compound Selection of Anti-cancer Drugs Based on Multi-Objective Optimization","authors":"Xiaoyan Liu, Zhiwei Xu, Guangwen Liu, Limin Liu","doi":"10.1109/ISBP57705.2023.10061321","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061321","url":null,"abstract":"In the compound selection process of anti-cancer drugs, safety properties such as drug activity and pharmacokinetics need to be considered simultaneously. To construct a more complete and precise drug screening mechanism, this paper proposed an intelligent compound selection method for anti-cancer drugs based on multi-objective optimization. The proposed model is executed in the MapReduce environment. Quantitatively analyze the biological activity of the compound, and qualitatively analyze the properties of pharmacokinetics and safety properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) to build a multi-objective optimization model. Guided by Pareto optimization theory, the set of non-inferior solution values was determined, and the compound combination that satisfies the optimization goal was found by genetic optimization. On this basis, a Monte Carlo hypothesis test was used to determine the equipped range of the compounds. Finally, an example of the compound selection of anti-breast cancer drugs is given, and the experimental evaluation proves that the algorithm can screen compounds limitedly, which provides a basis for anti-cancer drug synthesis.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126733326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061313
Jinxiu Zhang, Xunbing Shen
Post-traumatic stress disorder (PTSD) is a series of long-lasting mental disorders caused by individuals experiencing or witnessing life-threatening events, which are characterized by the presence of three main symptom groups: persistent fear memory, hyperarousal and avoidance, causing severe social disorders and health damage to patients. With the development of computer science and technology, emerging virtual reality technology provides a new means of treatment for common mental illness. Based on the analysis of the pathogenesis of PTSD and the superiority of virtual reality technology, this paper reviews the application of virtual reality technology in the treatment of PTSD in recent years at home and abroad. We hope to bring inspiration for the combination of virtual reality technology with traditional therapy and its innovative application for better diagnosis and treatment of PTSD.
{"title":"Application of virtual reality technology in post-traumatic stress disorder","authors":"Jinxiu Zhang, Xunbing Shen","doi":"10.1109/ISBP57705.2023.10061313","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061313","url":null,"abstract":"Post-traumatic stress disorder (PTSD) is a series of long-lasting mental disorders caused by individuals experiencing or witnessing life-threatening events, which are characterized by the presence of three main symptom groups: persistent fear memory, hyperarousal and avoidance, causing severe social disorders and health damage to patients. With the development of computer science and technology, emerging virtual reality technology provides a new means of treatment for common mental illness. Based on the analysis of the pathogenesis of PTSD and the superiority of virtual reality technology, this paper reviews the application of virtual reality technology in the treatment of PTSD in recent years at home and abroad. We hope to bring inspiration for the combination of virtual reality technology with traditional therapy and its innovative application for better diagnosis and treatment of PTSD.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129760601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/isbp57705.2023.10061300
{"title":"ISBP 2023 Cover Page","authors":"","doi":"10.1109/isbp57705.2023.10061300","DOIUrl":"https://doi.org/10.1109/isbp57705.2023.10061300","url":null,"abstract":"","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120835752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061293
Haonan Zhou
Drug screening is crucial in the entire pharmaceutical chain. There are about 3500W of known structural drug compound molecules in the world. The massive amount of data has led to an enormous screening task for a single protein target. Therefore, how accelerating the speed of high-throughput screening is an urgent problem. We combine the advantages of computer CPU multi-core for parallel optimization of D3DOCKxb to achieve accelerated drug screening.
{"title":"High-efficiency drug design research based on virtual high-throughput screening","authors":"Haonan Zhou","doi":"10.1109/ISBP57705.2023.10061293","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061293","url":null,"abstract":"Drug screening is crucial in the entire pharmaceutical chain. There are about 3500W of known structural drug compound molecules in the world. The massive amount of data has led to an enormous screening task for a single protein target. Therefore, how accelerating the speed of high-throughput screening is an urgent problem. We combine the advantages of computer CPU multi-core for parallel optimization of D3DOCKxb to achieve accelerated drug screening.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130833442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061314
Yang Shiyilin, Shao Jie, Yang Xin, Chen Xin, Wang Xingxing
Heart disease is leading killers of human beings. Recognizing and categorizing Electrocardiogram (ECG) signals is crucial for early heart and cardiovascular disease prevention. A novel classification approach for ECG Arrhythmias based on Graph Bispectrum (GBispec) is proposed. First, the ECG signal is converted from the time domain to the Graph domain by using Graph Fourier Transform (GFT); Then, referring to the traditional bispectrum algorithm, the GFT results of ECG are converted into GBispec; Then, extract the graph features of Graph Integral Bispectrum (GIB), and use Deep Neural Networks(DNN) to process the GIB results. 4 different types of ECG signals are classified. Experiments results show that proposed method is effective in classification.
{"title":"ECG arrhythmias Classification with a Graph Bispectrum method","authors":"Yang Shiyilin, Shao Jie, Yang Xin, Chen Xin, Wang Xingxing","doi":"10.1109/ISBP57705.2023.10061314","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061314","url":null,"abstract":"Heart disease is leading killers of human beings. Recognizing and categorizing Electrocardiogram (ECG) signals is crucial for early heart and cardiovascular disease prevention. A novel classification approach for ECG Arrhythmias based on Graph Bispectrum (GBispec) is proposed. First, the ECG signal is converted from the time domain to the Graph domain by using Graph Fourier Transform (GFT); Then, referring to the traditional bispectrum algorithm, the GFT results of ECG are converted into GBispec; Then, extract the graph features of Graph Integral Bispectrum (GIB), and use Deep Neural Networks(DNN) to process the GIB results. 4 different types of ECG signals are classified. Experiments results show that proposed method is effective in classification.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121428731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061307
Wanyong Tian, Fuqiang Li, Yibo Liu, Zichen Wang, Zhang Tao
Uncertain frequent pattern mining is usually challenged by the single probabilistic frequent threshold or the single expected support as the measurements of frequent itemsets. A promising solution based on multiple expected minimum support has been introduced in more recent studies to distinguish the mining values of each item, but the intrinsic combinatorial explosion still limited this strategy to be further improved for more generic scenarios. In this paper, a novel mining scheme for uncertain frequent itemsets is proposed. By ensembling multiple conditional item-wise supports, the problems of information redundancy as well as loss caused by a single probabilistic frequent threshold can be effectively improved. Furthermore, by using a variety of pruning strategies based on the property of sorted downward closure and the concept of least minimum probabilistic frequent threshold, an UFP-ECIS (Uncertain Frequent Pattern Mining with Ensembled Conditional Item-wise Supports) algorithm is also introduced. Substantial experiments have been proved to demonstrate that the proposed mining scheme and algorithm has enhanced the information precision of the uncertain frequent itemsets mining.
{"title":"Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports","authors":"Wanyong Tian, Fuqiang Li, Yibo Liu, Zichen Wang, Zhang Tao","doi":"10.1109/ISBP57705.2023.10061307","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061307","url":null,"abstract":"Uncertain frequent pattern mining is usually challenged by the single probabilistic frequent threshold or the single expected support as the measurements of frequent itemsets. A promising solution based on multiple expected minimum support has been introduced in more recent studies to distinguish the mining values of each item, but the intrinsic combinatorial explosion still limited this strategy to be further improved for more generic scenarios. In this paper, a novel mining scheme for uncertain frequent itemsets is proposed. By ensembling multiple conditional item-wise supports, the problems of information redundancy as well as loss caused by a single probabilistic frequent threshold can be effectively improved. Furthermore, by using a variety of pruning strategies based on the property of sorted downward closure and the concept of least minimum probabilistic frequent threshold, an UFP-ECIS (Uncertain Frequent Pattern Mining with Ensembled Conditional Item-wise Supports) algorithm is also introduced. Substantial experiments have been proved to demonstrate that the proposed mining scheme and algorithm has enhanced the information precision of the uncertain frequent itemsets mining.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126460151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1109/ISBP57705.2023.10061298
Lingyun Chen, Yi Niu
The study of EEG motor imagery adds a new therapeutic approach for patients with motor disorders, and the key to the problem study is how to improve the classification recognition of EEG motor imagery. The complex characteristics of EEG signals and the existence of multi-channel spatio-temporal properties increase the difficulty of their feature extraction and classification. There are spatial correlations between different channels and temporal correlations between different time series signals, so the selection process of signal features is complicated, resulting in low recognition rate. In this paper, we propose a spatial graph convolutional neural network based on a self-attentive mechanism. For the spatial characteristics of signals with different channels, we extract spatial features by constructing a graph structure and then by information aggregation; for its temporal characteristics, we use time slicing to calculate the importance weights of different time periods in the input signal by using the self-attentive mechanism, and then update the time segments by weighting and summing, so as to minimize the influence of other interfering signals, complete feature extraction and improve the The classification recognition rate is improved. From the experimental results, the recognition rate of this model reaches over 88% in the existing open EEG motion imagery dataset, which has good practicality and applicability.
{"title":"EEG Motion Classification Combining Graph Convolutional Network and Self-attentiion","authors":"Lingyun Chen, Yi Niu","doi":"10.1109/ISBP57705.2023.10061298","DOIUrl":"https://doi.org/10.1109/ISBP57705.2023.10061298","url":null,"abstract":"The study of EEG motor imagery adds a new therapeutic approach for patients with motor disorders, and the key to the problem study is how to improve the classification recognition of EEG motor imagery. The complex characteristics of EEG signals and the existence of multi-channel spatio-temporal properties increase the difficulty of their feature extraction and classification. There are spatial correlations between different channels and temporal correlations between different time series signals, so the selection process of signal features is complicated, resulting in low recognition rate. In this paper, we propose a spatial graph convolutional neural network based on a self-attentive mechanism. For the spatial characteristics of signals with different channels, we extract spatial features by constructing a graph structure and then by information aggregation; for its temporal characteristics, we use time slicing to calculate the importance weights of different time periods in the input signal by using the self-attentive mechanism, and then update the time segments by weighting and summing, so as to minimize the influence of other interfering signals, complete feature extraction and improve the The classification recognition rate is improved. From the experimental results, the recognition rate of this model reaches over 88% in the existing open EEG motion imagery dataset, which has good practicality and applicability.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132947242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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