J Biomed Inform - A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

Tópicos

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Resumo

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).

Resumo Limpo

now domain inform record period time lead sequenc data known time seri mani domain like medicin time seri analysi requir focus certain region interest known event rather analyz whole time seri paper propos framework knowledg discoveri onedimension multidimension time seri contain event show approach can use classifi medic time seri mean process identifi event time seri generat time seri refer model repres event compar two time seri analyz event common appli framework time seri generat area electroencephalographi eeg stabilometri framework perform evalu term classif accuraci result confirm propos schema potenti classifi eeg stabilometr signal propos framework use discov knowledg medic time seri contain event stabilometr electroencephalograph time seri result equal applic medic domain generat iconograph time seri exampl electrocardiographi ecg

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