{"title":"基于Canopy、K-means和遗传模拟退火的微态聚类算法。","authors":"Jingting Liang, Xiangguo Yin, Mingxing Lin","doi":"10.1088/2057-1976/adda50","DOIUrl":null,"url":null,"abstract":"<p><p><i>Background</i>. Electroencephalogram (EEG) microstate analysis can capture transient patterns of brain activity and provide valuable insights into brain motor and cognitive functions. However, the performance of traditional microstate analysis algorithms limits a deeper understanding of the neural mechanisms behind complex conditions.<i>Methods</i>. This study proposed a Canopy-KM-GSA algorithm, which combines Canopy clustering algorithm, K-means algorithm and genetic simulated annealing framework to automatically determine the optimal number of microstates and refine the clustering sequence. Utilizing the proposed algorithm, the study performed microstate analysis of pedaling motor datasets, Passive Auditory Oddball Paradigm task datasets, and epileptic patients datasets. The performance of the proposed algorithm is compared with seven baseline algorithms (including traditional K-means algorithm, K-medoids algorithm, ICA algorithm, PCA algorithm, GMD driven density canopy K-means algorithm, modified K-means algorithm and Agglomerative Hierarchical Clustering(AAHC) algorithm).<i>Results</i>. The results demonstrated the superior performance of Canopy-KM-GSA, achieving a significantly higher total evaluation compared to baseline microstate analysis algorithms. With an average Global Explained Variance (GEV) of 94.43%, an average Calinski-Harabasz Index (CHI) of 537.99, and an average Davies-Bouldin Index (DBI) of 1.57 in pedaling motor datasets; an average GEV of 94.46%, an average CHI of 389.29, and an average DBI of 1.44 in Passive Auditory Oddball Paradigm task datasets; an average GEV of 58.40%, an average CHI of 254.11, and an average DBI of 1.53 in epileptic patients datasets.<i>Conclusions</i>. The novel microstate analysis algorithms offers a more accurate tool for EEG microstate analysis.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An enhanced microstate clustering algorithm based on canopy, K-means, and genetic simulated annealing.\",\"authors\":\"Jingting Liang, Xiangguo Yin, Mingxing Lin\",\"doi\":\"10.1088/2057-1976/adda50\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Background</i>. Electroencephalogram (EEG) microstate analysis can capture transient patterns of brain activity and provide valuable insights into brain motor and cognitive functions. 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引用次数: 0
摘要
背景:脑电图(EEG)微状态分析可以捕捉大脑活动的瞬态模式,并为大脑运动和认知功能提供有价值的见解。然而,传统的微状态分析算法的性能限制了对复杂条件背后的神经机制的深入理解。方法:提出了一种Canopy- km - gsa算法,该算法将Canopy聚类算法、K-means算法和遗传模拟退火框架相结合,自动确定最优微状态数并细化聚类序列。利用提出的算法,本研究对踏板运动数据集、被动听觉怪异范式任务数据集和癫痫患者数据集进行了微状态分析。将该算法与传统K-means算法、k - mediids算法、ICA算法、PCA算法、GMD驱动密度冠层K-means算法、改进K-means算法和Agglomerative Hierarchical Clustering(AAHC)算法等7种基线算法进行性能比较。结果:结果证明了Canopy-KM-GSA的优越性能,与基线微状态分析算法相比,获得了显着更高的总评价。踏板电机数据集的平均全局解释方差(GEV)为94.43%,Calinski-Harabasz指数(CHI)为537.99,Davies-Bouldin指数(DBI)为1.57;被动听觉怪球任务组的平均GEV为94.46%,平均CHI为389.29,平均DBI为1.44;癫痫患者数据集的平均GEV为58.40%,平均CHI为254.11,平均DBI为1.53。结论:新的微状态分析算法为脑电图微状态分析提供了更准确的工具。
An enhanced microstate clustering algorithm based on canopy, K-means, and genetic simulated annealing.
Background. Electroencephalogram (EEG) microstate analysis can capture transient patterns of brain activity and provide valuable insights into brain motor and cognitive functions. However, the performance of traditional microstate analysis algorithms limits a deeper understanding of the neural mechanisms behind complex conditions.Methods. This study proposed a Canopy-KM-GSA algorithm, which combines Canopy clustering algorithm, K-means algorithm and genetic simulated annealing framework to automatically determine the optimal number of microstates and refine the clustering sequence. Utilizing the proposed algorithm, the study performed microstate analysis of pedaling motor datasets, Passive Auditory Oddball Paradigm task datasets, and epileptic patients datasets. The performance of the proposed algorithm is compared with seven baseline algorithms (including traditional K-means algorithm, K-medoids algorithm, ICA algorithm, PCA algorithm, GMD driven density canopy K-means algorithm, modified K-means algorithm and Agglomerative Hierarchical Clustering(AAHC) algorithm).Results. The results demonstrated the superior performance of Canopy-KM-GSA, achieving a significantly higher total evaluation compared to baseline microstate analysis algorithms. With an average Global Explained Variance (GEV) of 94.43%, an average Calinski-Harabasz Index (CHI) of 537.99, and an average Davies-Bouldin Index (DBI) of 1.57 in pedaling motor datasets; an average GEV of 94.46%, an average CHI of 389.29, and an average DBI of 1.44 in Passive Auditory Oddball Paradigm task datasets; an average GEV of 58.40%, an average CHI of 254.11, and an average DBI of 1.53 in epileptic patients datasets.Conclusions. The novel microstate analysis algorithms offers a more accurate tool for EEG microstate analysis.
期刊介绍:
BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.