AI Article Synopsis

  • This paper presents a new method for assessing the quality of quasi-periodic cardiovascular signals, particularly focusing on the photoplethysmogram (PPG).
  • The method segments the PPG signals into individual pulse periods, normalizes them for comparison, and evaluates similarity between adjacent segments to classify signal quality.
  • The effectiveness of this approach is demonstrated with impressive sensitivity (97.9%), specificity (85.3%), and accuracy (93.8%) using a database of PPG signals collected during various activities.

Article Abstract

This paper proposes a novel signal quality assessment method for quasi-periodic cardiovascular signals, chiefly focus on the photoplethysmogram (PPG). The proposed method utilizes the fact that most cardiovascular signals are slowly time varying and thus morphological aspects of the two adjacent beats are almost identical. In order to implement this idea, the method first identifies pulse onset to divide the signal into several segments each of which contains one period of the signal. The segmented pulse signals having different pulse durations are then temporarily normalized by resampling them at a specific rate. Finally, the quality of the signals is evaluated as the signal similarity between the two adjacent segments. Optimal thresholds for the classification between high-and low-quality PPG signals are determined using the equal training sensitivity and specificity criterion. The proposed method is evaluated using a database where PPG signals are collected during a variety of activities such as cycling exercise. It attains a sensitivity of 97.9%, a specificity of 85.3%, and an accuracy of 93.8%, compared to manually annotated results. The promising results indicate that the proposed method is affordable to simply determine the quality of quasi-periodic cardiovascular signals, particularly PPG signals. In addition, based on the quasi-periodic characteristics of cardiovascular signals, the proposed method can also be used to indicate the reliability and the availability of the collected signals.

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Source
http://dx.doi.org/10.1109/EMBC.2018.8512341DOI Listing

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