Karamchandani Sunil, Merchant S N, Desai U B, Jindal G D
Indian Institute of Technology, Bombay, Mumbai, India, 400076.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3978-81. doi: 10.1109/IEMBS.2010.5627983.
Radial Pulse forms the most basic and essential physical sign in clinical medicine. The paper proposes the application of crisp and fuzzy clustering algorithms under supervised and unsupervised learning scenarios for identifying non-trivial regularities and relationships of the radial pulse patterns obtained by using the Impedance Plethysmographic technique. The objective of our paper is to unearth the hidden patterns to capture the physiological variabilities from the arterial pulse for clinical analysis, thus providing a very useful tool for disease characterization. A variety of fuzzy algorithms including Gustafson-Kessel (GK) and Gath-Geva (GG)have been intensively tested over a diverse group of subjects and over 4855 data sets. Exhaustive testing over the data set show that about 80 % of the patterns are successfully classified thus providing promising results. A Rank Index of 0.7739 is obtained under supervised learning, which provides an excellent conformity of our process with the results of plethysmographic experts. A correlation of the patterns with the diseases of heart, liver and lungs is judiciously performed.
桡动脉脉搏是临床医学中最基本、最重要的体征。本文提出在监督学习和无监督学习场景下应用清晰聚类算法和模糊聚类算法，以识别使用阻抗体积描记技术获得的桡动脉脉搏模式的重要规律和关系。我们论文的目的是挖掘隐藏模式，从动脉脉搏中捕捉生理变异性用于临床分析，从而为疾病特征描述提供非常有用的工具。包括古斯塔夫森 - 凯塞尔（GK）和加思 - 格瓦（GG）在内的多种模糊算法已在不同组别的受试者和4855多个数据集上进行了深入测试。对数据集的详尽测试表明，约80%的模式被成功分类，从而提供了有前景的结果。在监督学习下获得的秩指数为0.7739，这表明我们的过程与体积描记专家的结果具有极好的一致性。明智地进行了模式与心脏、肝脏和肺部疾病的相关性分析。
