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Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications

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Abstract

With the proliferation of IoMT (Internet of Medical Things), billions of connected medical devices are constantly producing oceans of time series sensor data, dubbed as time series for short. Considering these time series reflect various functional states of the human body, how to effectively detect the corresponding abnormalities is of great significance for smart healthcare. Accordingly, we develop a horizontal visibility graph-based temporal classification model for disease diagnosis. We conduct extensive comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. Besides, we have released the codes and parameters to facilitate the community research.

Graphic Abstract

We propose an identifiable temporal feature selection via horizontal visibility graph for time series classification (TSC) based disease diagnosis. We conduct comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. As a side contribution, we have released the codes and parameters to facilitate the community research (https://github.com/sdujicun/SSVG).

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Notes

  1. Our method: https://github.com/sdujicun/SSVG.

  2. www.timeseriesclassification.com.

  3. https://github.com/sdujicun/SSVG.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the editors for their insightful comments and suggestions, which are greatly helpful for improving the quality of this paper. This work is supported by the Natural Science Foundation of Shandong Province (Grant no. ZR2020QF112); the project of CERNET Innovation (NGII20190109); the project of Qingdao Postdoctoral Applied Research (QDPostD20190901).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, Shandong, China

    Cun Ji & Xiangwei Zheng

  2. School of Computer Science and Technology, Shandong University, Qingdao, 266237, Shandong, China

    Yupeng Hu & Kun Wang

  3. School of Software, Shandong University, Jinan, 250101, Shandong, China

    Peng Zhan & Xueqing Li

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  1. Cun Ji
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  2. Yupeng Hu
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Correspondence to Yupeng Hu.

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Ji, C., Hu, Y., Wang, K. et al. Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications. Interdiscip Sci Comput Life Sci 13, 717–730 (2021). https://doi.org/10.1007/s12539-021-00460-5

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