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EnglishThis 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.
Neural Recurrent Approches to Noninvasive Blood Pressure Estimation / Paviglianiti, Annunziata; Randazzo, Vincenzo; Cirrincione, Giansalvo; Pasero, Eros Gian Alessandro. - ELETTRONICO. - (2020), pp. 1-7. ((Intervento presentato al convegno 2020 International Joint Conference on Neural Networks, IJCNN 2020 tenutosi a Glasgow, United Kingdom, United Kingdom nel 19-24 July 2020.
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| Titolo: | Neural Recurrent Approches to Noninvasive Blood Pressure Estimation |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Abstract: | This paper presents a comparison between two recurrent neural networks (RNN) for arterial blood p...ressure (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. |
| ISBN: | 978-1-7281-6926-2 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| PID6437761.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri | |
| Paviglianiti-Neural.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2849888
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