期刊
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
卷 13, 期 4, 页码 717-730出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s12539-021-00460-5
关键词
Horizontal visibility graph; Temporal feature selection; Time series classification; Smart healthcare
资金
- Natural Science Foundation of Shandong Province [ZR2020QF112]
- project of CERNET Innovation [NGII20190109]
- project of Qingdao Postdoctoral Applied Research [QDPostD20190901]
This study developed a temporal classification model for disease diagnosis based on horizontal visibility graphs, and verified its superiority in accuracy and efficiency through extensive comparison experiments on benchmark datasets. Additionally, the codes and parameters have been released to facilitate community research.
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. [GRAPHICS] .
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