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Bioengineering 2016, 3(4), 22; doi:10.3390/bioengineering3040022

Eventogram: A Visual Representation of Main Events in Biomedical Signals

1
Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, BC V6Z 2K5, Canada
2
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Academic Editor: Christoph Herwig
Received: 19 August 2016 / Revised: 15 September 2016 / Accepted: 18 September 2016 / Published: 22 September 2016
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Abstract

Biomedical signals carry valuable physiological information and many researchers have difficulty interpreting and analyzing long-term, one-dimensional, quasi-periodic biomedical signals. Traditionally, biomedical signals are analyzed and visualized using periodogram, spectrogram, and wavelet methods. However, these methods do not offer an informative visualization of main events within the processed signal. This paper attempts to provide an event-related framework to overcome the drawbacks of the traditional visualization methods and describe the main events within the biomedical signal in terms of duration and morphology. Electrocardiogram and photoplethysmogram signals are used in the analysis to demonstrate the differences between the traditional visualization methods, and their performance is compared against the proposed method, referred to as the “eventogram” in this paper. The proposed method is based on two event-related moving averages that visualizes the main time-domain events in the processed biomedical signals. The traditional visualization methods were unable to find dominant events in processed signals while the eventogram was able to visualize dominant events in signals in terms of duration and morphology. Moreover, eventogram-based detection algorithms succeeded with detecting main events in different biomedical signals with a sensitivity and positive predictivity >95%. The output of the eventogram captured unique patterns and signatures of physiological events, which could be used to visualize and identify abnormal waveforms in any quasi-periodic signal. View Full-Text
Keywords: quasi-periodic signals; time-series visualization; signal transformation; event detection; signal segmentation; time-domain representation; spatio-temporal analysis; pattern discovery; waveform recognition quasi-periodic signals; time-series visualization; signal transformation; event detection; signal segmentation; time-domain representation; spatio-temporal analysis; pattern discovery; waveform recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Elgendi, M. Eventogram: A Visual Representation of Main Events in Biomedical Signals. Bioengineering 2016, 3, 22.

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