Time Series Classification using Motifs and Characteristics Extraction: A Case Study on ECG Databases

DOI

10.2991/.2013.40How to use a DOI?

Keywords

morphological pattern, attribute extraction, decision trees

Abstract

In the last decade, the interest for temporal data analysis methods has increased significantly in many application areas. One of these areas is the medical field, in which temporal data is in the core of innumerous diagnosis exams. However, only a small portion of all gathered medical data is properly analyzed, in part, due to the lack of appropriate temporal methods and tools. This work presents an alternative approach, based on global characteristics and motifs, to mine medical time series databases using machine learning algorithms. Characteristics are data statistics that present a global summary of the data. Motifs are frequently recurrent subsequences that usually represent interesting local patterns. We use a combination of global characteristics and local motifs to describe the data and feed machine learning algorithms. A case study is performed on three databases of Electrocardiogram exams. Our results show the superior performance of our approach in comparison to the naïve method that provides raw temporal data directly to the learning algorithms. We demonstrate that our approach is more accurate and provides more interpretable models than the method that does not extract features.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - André G. Maletzke
AU  - Huei D. Lee
AU  - Gustavo E.A.P.A. Batista
AU  - Solange O. Rezende
AU  - Renato B. Machado
AU  - Richardson F. Voltolini
AU  - Joylan N. Maciel
AU  - Fabiano Silva
PY  - 2013/10
DA  - 2013/10
TI  - Time Series Classification using Motifs and Characteristics Extraction: A Case Study on ECG Databases
BT  - Proceedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support
PB  - Atlantis Press
SP  - 322
EP  - 329
SN  - 1951-6851
UR  - https://doi.org/10.2991/.2013.40
DO  - 10.2991/.2013.40
ID  - Maletzke2013/10
ER  -