Visual Interpretation of Biomedical Time Series Using Parzen Window-Based Density-Amplitude Domain Transformation.

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

Characteristics of the EEG Dataset Used in the Study.

<p>Characteristics of the EEG Dataset Used in the Study.</p

The density-amplitude representations of classes belonging to the same attribute.

<p>This attribute is also used in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163569#pone.0163569.g007" target="_blank">7</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163569#pone.0163569.g008" target="_blank">8</a>.</p

The density-amplitude graph of the 3rd attribute of the dataset (Class-1 is divided to get two fake (pseudo) classes.

<p>Both fake classes are related to the ‘eyes open’ status).</p

Display of two different signals represented in the time-amplitude and frequency-amplitude domains.

<p>Display of two different signals represented in the time-amplitude and frequency-amplitude domains.</p

Density-amplitude domain representation of 3rd channel data (Blue: class-1 data, Red: class-2 data).

<p>Density-amplitude domain representation of 3rd channel data (Blue: class-1 data, Red: class-2 data).</p

Calculation of the density coefficients for two elements in a sample dataset.

<p>Calculation of the density coefficients for two elements in a sample dataset.</p

Classification Success Rates of sEMG Datasets.

<p>Classification Success Rates of sEMG Datasets.</p

Flow diagram of the study (Attribute (m-n) means the class m data in the nth attribute).

<p>Flow diagram of the study (Attribute (m-n) means the class m data in the nth attribute).</p

The time-amplitude representations of classes belonging to the same attribute.

<p>This attribute is also used in this study.</p