Time Series Classification using Motifs and Characteristics Extraction: A Case Study on ECG Databases
André Gustavo Maletzke, Huei D. Lee, Gustavo E. A. P. A. Batista, Solange Oliveira Rezende, Renato B. Machado, Richardson Floriani Voltolini, Joylan Nunes Maciel, Fabiano Ferreira da Silva · 2013
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.