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A multichannel time-frequency and multi-wavelet toolbox for uterine electromyography processing and visualisation.
Batista, Arnaldo G; Najdi, Shirin; Godinho, Daniela M; Martins, Catarina; Serrano, Fátima C; Ortigueira, Manuel D; Rato, Raul T.
Afiliación
  • Batista AG; UNINOVA, Campus de Caparica, 2829-516 Caparica, Portugal; Faculty of Sciences and Technology of the Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal. Electronic address: agb@fct.unl.pt.
  • Najdi S; UNINOVA, Campus de Caparica, 2829-516 Caparica, Portugal.
  • Godinho DM; Faculty of Sciences and Technology of the Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal.
  • Martins C; Faculty of Sciences and Technology of the Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal.
  • Serrano FC; Nova Medical School /Faculty of Medical Sciences, Universidade Nova de Lisboa, Portugal.
  • Ortigueira MD; UNINOVA, Campus de Caparica, 2829-516 Caparica, Portugal; Faculty of Sciences and Technology of the Universidade Nova de Lisboa, Campus de Caparica, 2829-516 Caparica, Portugal.
  • Rato RT; UNINOVA, Campus de Caparica, 2829-516 Caparica, Portugal; Escola Superior de Tecnologia do Instituto Politécnico de Setúbal, 2910-761 Setúbal, Portugal.
Comput Biol Med ; 76: 178-91, 2016 09 01.
Article en En | MEDLINE | ID: mdl-27474810
The uterine electromyogram, also called electrohysterogram (EHG), is an electrical signal generated by the uterine contractile activity. The EHG has been considered a promising biomarker for labour and preterm labour prediction, for which there is a demand for accurate estimation methods. Preterm labour is a significant public health concern and one of the major causes of neonatal mortality and morbidity [1]. Given the non-stationary properties of the EHG signal, time-frequency domain analysis can be used. For real life signals it is not generally possible to determine a priori the suitable quadratic time-frequency kernel or the appropriate wavelet family and relative parameters, regarding, for instance, the adequate detection of the signal frequency variation in time. There has been a lack of a comprehensive software tool for the selection of the appropriate time frequency representation of a multichannel EHG signal and extraction of relevant spectral and temporal information. The presented toolbox (Uterine Explorer) has been specifically designed for the EHG analysis and exploration in view of the characterisation of its components. The starting point is the multichannel scalogram or spectrogram representation from which frequency and time marginals, instantaneous frequency and bandwidth are obtained as EHG features. From this point the detected components undergo parametric and non-parametric spectral estimation and wavelet packet analysis. Intrauterine pressure estimation (IUP) is obtained using the Teager, RMS, wavelet marginal and Hilbert operators over the EHG. This toolbox has been tested to build up a dictionary of 288 EHG components [2], useful for research in preterm labour prediction.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Monitoreo Uterino / Electromiografía Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Comput Biol Med Año: 2016 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Monitoreo Uterino / Electromiografía Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Comput Biol Med Año: 2016 Tipo del documento: Article