Open AccessVisual Interpretation of Biomedical Time Series Using Parzen Window-Based Density-Amplitude Domain Transformation
Author(s) -
Selahaddin Batuhan Akben,
Ahmet Alkan
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0163569
Subject(s) - amplitude , kernel density estimation , computer science , series (stratigraphy) , probability density function , pattern recognition (psychology) , artificial intelligence , time series , density estimation , time domain , transformation (genetics) , bayes' theorem , representation (politics) , domain (mathematical analysis) , raw data , mathematics , machine learning , statistics , computer vision , bayesian probability , optics , physics , mathematical analysis , chemistry , estimator , law , biology , paleontology , biochemistry , political science , politics , gene
This study proposes a new method suitable for the visual analysis of biomedical time series that is based on the examination of biomedical signals in the density-amplitude domain. Toward this goal, we employed two publicly available datasets. In the first stage of the study, density coefficients were computed separately by using the Parzen Windowing method for each class of raw attribute data. Then, differences between classes were determined visually by using density coefficients and their related amplitudes. Visual interpretation of the processed data gave more successful classification results compared with the raw data in the first stage. Next the density-amplitude representations of the raw data were classified using classifiers (SVM, KNN and Naïve Bayes). The raw data (time-amplitude) and their frequency-amplitude representation were also classified using the same classification methods. The statistical results showed that the proposed method based on the density-amplitude representation increases the classification success up to 55% compared with methods using the time-amplitude domain and up to 75% compared with methods based on the frequency-amplitude domain. Finally, we have highlighted several statistical analysis suggestions as a result of the density-amplitude representation.