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.
Highlights
Periodogram, spectrogram, and wavelet methods are the current standard methods for visualizing and analyzing one-dimensional, quasi-periodic biosignals
The aim of this paper is to introduce a new, simple, easy-to-use visualization algorithm that extracts the main events in signals, especially in one-dimensional, quasi-periodic biosignals such as electrogradiograms (ECGs) and photoplethysmograms (PPGs)
The results show that the eventogram is suitable for any quasi-periodic biomedical signals, as it is robust against the irregular rhythm, non-stationary effects
Summary
Periodogram, spectrogram, and wavelet methods are the current standard methods for visualizing and analyzing one-dimensional, quasi-periodic biosignals. Such visualizations generate temporal-spatial information in order to gain scientific insights into the physical measurement. These powerful visualization techniques play a key role in understanding large and complex signals. The periodogram is a frequency visualization of the processed signal, while the spectrogram and wavelet are time-frequency visualizations. 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]; this massive research effort did not lead to a generic, intuitive implementation framework that is applied across different disciplines, including current clinical problems [9]
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