TY - GEN
T1 - Assessment of Quality of Gyrocardiograms Based on Features Derived from Symmetric Projection Attractor Reconstruction
AU - Siecinski, Szymon
AU - Irshad, Muhammad Tausif
AU - Hasan, Md Abid
AU - Tkacz, Ewaryst
AU - Grzegorzek, Marcin
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/9/21
Y1 - 2023/9/21
N2 - 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.
AB - 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.
KW - Gyrocardiography
KW - Symmetric Projection Attractor Reconstruction
KW - signal quality
UR - https://www.scopus.com/pages/publications/85178281994
U2 - 10.1145/3615834.3615855
DO - 10.1145/3615834.3615855
M3 - Conference contribution
AN - SCOPUS:85178281994
T3 - ACM International Conference Proceeding Series
BT - iWOAR 2023 - 8th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence, Proceedings
A2 - Matthies, Denys J.C.
A2 - Grzegorzek, Marcin
A2 - Kuijper, Arjan
A2 - Leutheuser, Heike
PB - Association for Computing Machinery
T2 - 8th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence, iWOAR 2023
Y2 - 21 September 2023 through 22 September 2023
ER -