EM Topological Signaling and Computing

Kouzaev, Guennadi A.

doi:10.1007/978-3-642-30310-4_10

Cited by 1 publication

(1 citation statement)

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“…However, these techniques do not provide detailed information of the dominant events in terms of morphology and duration. Many researchers attempted to visualize signals in the time domain [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]; however, this massive research effort did not lead to a generic, intuitive implementation framework that is specifically applied across different disciplines, including current clinical problems [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Eventogram: A Visual Representation of Main Events in Biomedical Signals

Elgendi

2016

Bioengineering

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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.

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Section: Introductionmentioning

confidence: 99%

Eventogram: A Visual Representation of Main Events in Biomedical Signals

Elgendi

2016

Bioengineering

View full text Add to dashboard Cite

show abstract

EM Topological Signaling and Computing

Cited by 1 publication

References 97 publications

Eventogram: A Visual Representation of Main Events in Biomedical Signals

Eventogram: A Visual Representation of Main Events in Biomedical Signals

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