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Stylistic data-driven possibilistic fuzzy clustering and real-life application on epilepsy biomedical electronic signals detection

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Abstract

Biomedical electronic signals play an important role in clinical diagnosis. EEG as one kind of biomedical electronic signals has been widely used for an epilepsy diagnosis. EEG data are strongly characterized by the inner cluster style. The classic clustering-based detection techniques such as FCM, K-means, and AP cannot effectively partition the manifold data without considering the inner cluster style. Therefore, in this paper, we propose a novel stylistic data-driven possibilistic fuzzy clustering technique (SD-PFC). SD-PFC has its merits in two aspects: (1) a stylistic standardization matrix is used to represent the stylistic information of samples contained in the inner clusters. (2) The distance matrix is re-constructed by samples which are transformed by style normalization matrices. Extensive experiments on artificial datasets and real-life datasets show the effectiveness of the novel clustering technique.

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Acknowledgements

This work is partly supported by Key Natural Science Research Project of Anhui Provincial Department of Education (No. KJ2017A708 and No. KJ2018A0821), Support Plan for Outstanding Young Talents in Colleges and Universities of Anhui Province (No. gxyq2017109), Bozhou College Excellent Talent Training Plan (No. 42 [2015]) and Bozhou College School-Enterprise Software Engineering Innovation Practice Base (No. 2016xqhz01).

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Authors and Affiliations

  1. Department of Electronics and Information Engineering, Bozhou University, Anhui, China

    Guanglei Sheng, Chao Zhang, Hengfei Wu & Xiujian Hu

  2. Department of Medical Informatics, Nantong University, Nantong, China

    Guanglei Sheng & Yuanpeng Zhang

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  1. Guanglei Sheng
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  2. Chao Zhang
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  3. Hengfei Wu
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  4. Xiujian Hu
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  5. Yuanpeng Zhang
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Correspondence to Yuanpeng Zhang.

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Sheng, G., Zhang, C., Wu, H. et al. Stylistic data-driven possibilistic fuzzy clustering and real-life application on epilepsy biomedical electronic signals detection. J Ambient Intell Human Comput 14, 5451–5462 (2023). https://doi.org/10.1007/s12652-020-02112-w

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