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Non-invasive arterial blood pressure waveform reconstruction algorithm based on Bi-UNet / 中国医学物理学杂志
Article de Zh | WPRIM | ID: wpr-1026191
Bibliothèque responsable: WPRO
ABSTRACT
A non-invasive deep learning method is proposed for reconstructing arterial blood pressure signals from photoplethysmography signals.The method employs U-Net as a feature extractor,and a module referred to as bidirectional temporal processor is designed to extract time-dependent information on an individual model basis.The bidirectional temporal processor module utilizes a BiLSTM network to effectively analyze time series data in both forward and backward directions.Furthermore,a deep supervision approach which involves training the model to focus on various aspects of data features is adopted to enhance the accuracy of the predicted waveforms.The differences between actual and predicted values are 2.89±2.43,1.55±1.79 and 1.52±1.47 mmHg on systolic blood pressure,diastolic blood pressure and mean arterial pressure,respectively,suggesting the superiority of the proposed method over the existing techniques,and demonstrating its application potential.
Mots clés
Texte intégral: 1 Indice: WPRIM langue: Zh Texte intégral: Chinese Journal of Medical Physics Année: 2024 Type: Article
Texte intégral: 1 Indice: WPRIM langue: Zh Texte intégral: Chinese Journal of Medical Physics Année: 2024 Type: Article