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Eventogram: A Visual Representation of Main Events in Biomedical Signals
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5597265/ https://www.ncbi.nlm.nih.gov/pubmed/28952583 http://dx.doi.org/10.3390/bioengineering3040022 |
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author | Elgendi, Mohamed |
author_facet | Elgendi, Mohamed |
author_sort | Elgendi, Mohamed |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5597265 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-55972652017-09-21 Eventogram: A Visual Representation of Main Events in Biomedical Signals Elgendi, Mohamed Bioengineering (Basel) Communication 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. MDPI 2016-09-22 /pmc/articles/PMC5597265/ /pubmed/28952583 http://dx.doi.org/10.3390/bioengineering3040022 Text en © 2016 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Elgendi, Mohamed Eventogram: A Visual Representation of Main Events in Biomedical Signals |
title | Eventogram: A Visual Representation of Main Events in Biomedical Signals |
title_full | Eventogram: A Visual Representation of Main Events in Biomedical Signals |
title_fullStr | Eventogram: A Visual Representation of Main Events in Biomedical Signals |
title_full_unstemmed | Eventogram: A Visual Representation of Main Events in Biomedical Signals |
title_short | Eventogram: A Visual Representation of Main Events in Biomedical Signals |
title_sort | eventogram: a visual representation of main events in biomedical signals |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5597265/ https://www.ncbi.nlm.nih.gov/pubmed/28952583 http://dx.doi.org/10.3390/bioengineering3040022 |
work_keys_str_mv | AT elgendimohamed eventogramavisualrepresentationofmaineventsinbiomedicalsignals |