132476 20260217205508.0 doi 10.1016/j.bspc.2024.106174 sideral 137635 ART-2024-137635 eng Savirón-Cornudella, Ricardo Diagnosis of cardiotocographic sinusoidal patterns by spectral analyses 2024 Access copy available to the general public Unrestricted Background The sinusoidal pattern in cardiotocographic (CTG) monitoring shows a sinus-shaped signal longer than 30 min without short-term variability. It is commonly linked to fetal morbidity, particularly severe fetal anemia. Pseudosinusoidal patterns resemble sinusoidal patterns but without adverse fetal outcomes. This study aims to characterise sinusoidal and pseudosinusoidal patterns using spectral analysis. Methods A multicenter study case-control was conducted between January 2012 and February 2023. Maternal characteristics, perinatal data, and CTG parameters through spectral analysis were examined. The spectrum of the electrocardiographic signal was calculated, and the proportion of energy (PE), short- and long-term variability, amplitude, and the differences between sinusoidal, pseudosinusoidal, and control groups were compared. A predictive model for signal type was built using a classification tree. Results 60 CTG records were collected, including 38 controls. Of the 13 sinusoidal patterns detected, all exhibited a sinusoidal pattern with a PE ratio > 0.3, 9 of them (69 %) had a PE ratio > 0.5, and 4 (31 %) were in the range of 0.3–0.5. Among the 9 cases diagnosed as pseudosinusoidal, all had a sinusoidal pattern with a PE within the range of 0.3–0.5. Every control exhibited a PE < 0.3, except for one case. Short-term variability demonstrated limited discriminatory capability, while long-term variability showed a strong discriminatory capacity. For the classification tree, accuracy diagnosis was 92.3 %, 88.8 %, and 97.3 % for the sinusoidal, pseudosinusoidal, and control groups, respectively. Conclusion Computerised spectral analysis and the variable PE within the frequency range of 1.8–3.5 are reliable parameters to discriminate sinusoidal patterns. info:eu-repo/grantAgreement/ES/DGA/T69-23R info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es 4.9 2024 ENGINEERING, BIOMEDICAL 32 / 124 = 0.258 2024 Q2 T1 1.229 2024 Biomedical Engineering 2024 Q1 Signal Processing 2024 Q1 Health Informatics 2024 Q1 11.5 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Laliena Bielsa, Antonio (orcid)0000-0002-9496-9714 Esteban-Escaño, Javier (orcid)0000-0001-7995-6969 Calvo Torres, Javier Chóliz Ezquerro, Marta Castán Larraz, Berta Díaz de Terán Martínez-Berganza, Elisa Rodríguez Castaño, María José Álvaro Navidad, Miguel Andeyro García, Mercedes Whyte Orozco, Jaime Universidad de Zaragoza (orcid)0000-0001-9372-4267 Castán Mateo, Sergio Universidad de Zaragoza (orcid)0000-0002-9048-121X Esteban, Luis Mariano (orcid)0000-0002-3007-302X 1003 027 Universidad de Zaragoza Dpto. Anatom.Histolog.Humanas Area Anatom.Embriol.Humana 1013 645 Universidad de Zaragoza Dpto. Cirugía Área Obstetricia y Ginecología 93 (2024), 106174 [10 pp.] Biomed. signal proces. control Biomedical Signal Processing and Control 1746-8094 5221045 http://zaguan.unizar.es/record/132476/files/texto_completo.pdf Versión publicada 2540258 http://zaguan.unizar.es/record/132476/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:132476 articulos driver 2026-02-17-20:24:06 ARTICLE