title: A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.
abstract: There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG).
authors.affiliation: Open University of Madrid, UDIMA, Facultad de Enseñanzas Técnicas, Ctra, De la Coruña, km 38.500, Vía de Servicio, 15, 28400 Collado Villalba, Madrid, Spain. Electronic address: juanalfonso.lara@udima.es.
authors.forename: Juan A
authors.initials: JA
authors.lastname: Lara
authors.affiliation: Open University of Madrid, UDIMA, Facultad de Enseñanzas Técnicas, Ctra, De la Coruña, km 38.500, Vía de Servicio, 15, 28400 Collado Villalba, Madrid, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Lizcano
authors.affiliation: Technical University of Madrid, School of Computer Science, Campus de Montegancedo, s/n, 28660 Boadilla del Monte, Madrid, Spain.
authors.forename: Aurora
authors.initials: A
authors.lastname: Pérez
authors.affiliation: Technical University of Madrid, School of Computer Science, Campus de Montegancedo, s/n, 28660 Boadilla del Monte, Madrid, Spain.
authors.forename: Juan P
authors.initials: JP
authors.lastname: Valente
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Classification; Electroencephalography; Event; Medical data mining; Stabilometry; Time series analysis