Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications,Interdisciplinary Sciences: Computational Life Sciences

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Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications
Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2021-07-14 , DOI: 10.1007/s12539-021-00460-5
Cun Ji ₁ , Yupeng Hu ₂ , Kun Wang ₂ , Peng Zhan ₃ , Xueqing Li ₃ , Xiangwei Zheng ₁

Affiliation

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).

中文翻译：

面向智能医疗应用的通过水平可见性图的可识别时间特征选择

摘要

随着 IoMT（医疗物联网）的普及，数十亿连接的医疗设备不断产生海量的时间序列传感器数据，简称时间序列。考虑到这些时间序列反映了人体的各种功能状态，如何有效检测出相应的异常对智慧医疗意义重大。因此，我们开发了一种用于疾病诊断的基于水平可见性图的时间分类模型。我们对基准数据集进行了广泛的比较实验，以证明我们的方法在准确性和效率方面的优越性。此外，我们已经发布了代码和参数，以方便社区研究。