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

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 ).