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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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2306-5354/3/4/22 |
| _version_ | 1797717945590218752 |
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| author | Mohamed Elgendi |
| author_facet | Mohamed Elgendi |
| author_sort | Mohamed Elgendi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T08:43:49Z |
| format | Article |
| id | doaj.art-5475def4d0c3416d99b00f8e1443e22b |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-12T08:43:49Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-5475def4d0c3416d99b00f8e1443e22b2023-09-02T16:32:29ZengMDPI AGBioengineering2306-53542016-09-01342210.3390/bioengineering3040022bioengineering3040022Eventogram: A Visual Representation of Main Events in Biomedical SignalsMohamed Elgendi0Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, BC V6Z 2K5, CanadaBiomedical 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.http://www.mdpi.com/2306-5354/3/4/22quasi-periodic signalstime-series visualizationsignal transformationevent detectionsignal segmentationtime-domain representationspatio-temporal analysispattern discoverywaveform recognition |
| spellingShingle | Mohamed Elgendi Eventogram: A Visual Representation of Main Events in Biomedical Signals Bioengineering quasi-periodic signals time-series visualization signal transformation event detection signal segmentation time-domain representation spatio-temporal analysis pattern discovery waveform recognition |
| 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 | quasi-periodic signals time-series visualization signal transformation event detection signal segmentation time-domain representation spatio-temporal analysis pattern discovery waveform recognition |
| url | http://www.mdpi.com/2306-5354/3/4/22 |
| work_keys_str_mv | AT mohamedelgendi eventogramavisualrepresentationofmaineventsinbiomedicalsignals |