Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Neural Recurrent Approches to Noninvasive Blood Pressure Estimation

Abstract : This paper presents a comparison between two recurrent neural networks (RNN) for arterial blood pressure (ABP) estimation. ABP is a parameter closely related to the cardiac activity, for this reason its monitoring implies decreasing the risk of heart disease. In order to predict the ABP values (both systolic and diastolic), electrocardiographic (ECG) and photoplethysmographic (PPG) signals are used, separately, as inputs of the networks. To train the artificial neural networks, the synchronized signals are extracted from the Physionet MIMIC database. The output-error Neural networks (NNOE) and the Long Short Term Memory (LSTM) architectures are compared in terms of RMSE and absolute error. NNOE neural network, with ECG signal as input, results the best configuration in terms of both the proposed metrics. The predicted ABP falls within the values of the normative ANSI/AAMI/ISO 81060-2:2013 for sphygmomanometer certification.
Type de document :
Communication dans un congrès
Liste complète des métadonnées
Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : mardi 5 avril 2022 - 16:25:14
Dernière modification le : vendredi 5 août 2022 - 11:21:49


  • HAL Id : hal-03631440, version 1



Annunziata Paviglianiti, Vincenzo Randazzo, Giansalvo Cirrincione, Eros Pasero. Neural Recurrent Approches to Noninvasive Blood Pressure Estimation. 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), Jul 2020, Glasgow, United Kingdom. ⟨hal-03631440⟩



Consultations de la notice