Indian Journal of Science and Technology

Abstract

Background/Objectives: Quantification of Wrist Pulse Signals is helpful to take benefit of ancient approach i.e. Pulse Diagnosis. The objective of this paper is to effectively segregate outliers present within wrist pulses. Methods/Statistical Analysis: This work presents modification in Dynamic Time Warping (DTW) algorithm. The existing DTW algorithm searches for an optimal path using squared Euclidean distance to measure the local distance between segments. Here, we are discussing and integrating different local distance measures such as Correlation Distance, Manhattan Distance, Kendall’s τ Distance and Canberra Distance with DTW. All the discussed local distance measures were compared with existing Euclidean based DTW algorithm on the basis of Similarity Index parameter. Findings: Results shown that Manhattan Distance and Canberra Distance based DTW algorithm was efficient in optimal path selection and segregation of segments which lose their shape characteristics. In euclidean based DTW, outlier segregation was difficult as all values lied between 0 to 1.Correlation distance and Kendall’s tau distance algorithm were inappropriate in detecting outliers as results were not matched with visual observations. It was noticed that combination of Manhattan Distance and Canberra Distance based DTW algorithm were giving better outlier finding.

Keywords: Canberra Distance, DTW, Euclidean Distance, Manhattan Distance, Similarity Index, Wrist Pulse outliers