Assessment of Quality of Gyrocardiograms Based on Features Derived from Symmetric Projection Attractor Reconstruction

Szymon Sieciński*, Muhammad Tausif Irshad, Md Abid Hasan, Ewaryst Tkacz, Marcin Grzegorzek

*Corresponding author for this work

Abstract

Signal quality assessment is essential for biomedical signal processing, analysis, and interpretation. Various methods exist, including averaged numerical values, thresholding, time- or frequency-domain analysis, and nonlinear approaches. This study evaluated the quality of gyrocardiographic signals (GCG) using symmetric projection attractor reconstruction (SPAR) analysis. Two classifiers, random forest and bagged trees, were used to assess the performance of the SPAR-based approach. Eleven features were extracted from the variables v and w, calculated on the basis of the signal delay. These features included minimum and maximum values, mean, standard deviation (SD), median, and Euclidean distance. The results showed that the SPAR-based approach achieved high accuracy, precision, and recall. The random forest classifier achieved 0.729 accuracy, 0.726 precision, and 0.729 recall, while the bagged trees classifier achieved 0.792 accuracy, 0.804 precision, and 0.792 recall. These findings suggest that the SPAR-based approach is a promising method to accurately assess the quality of GCG signals.
Original languageEnglish
Pages15:1-15:5
Number of pages5
DOIs
Publication statusPublished - 10.2023
EventiWOAR '23: Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence - Fraunhofer IMTE, Lübeck, Germany
Duration: 21.09.202322.09.2023
https://doi.org/10.1145/3615834.3615855

Conference

ConferenceiWOAR '23: Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence
Abbreviated titleiWOAR'23
Country/TerritoryGermany
CityLübeck
Period21.09.2322.09.23
Internet address

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 205-07 Medical Informatics and Medical Bioinformatics

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