Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks
11 Jul 2022
TL;DR: In this paper , a cycle generative adversarial network (CycleGAN) based approach was proposed to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal.
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Abstract: Continuous monitoring of blood pressure (BP) can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethys-mograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends the GAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2× in BP estimation.
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Citations
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms
Emma Pemulwuy,Ingo Saenger +1 more
- 15 Nov 2022
TL;DR: PPG2ABP as mentioned in this paper is a two-stage cascaded deep learning-based method that manages to estimate the continuous arterial blood pressure waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude and phase in unison.
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NABNet: A Nested Attention-guided BiConvLSTM network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG signals
Sakib Mahmud,Nabil Ibtehaz,Amith Khandakar,M. Sohel Rahman,Antonio JR. Gonzales,Tawsifur Rahman,Md Shafayet Hossain,Md. Sakib Abrar Hossain,Md. Ahasan Atick Faisal,Farhan Fuad Abir,Farayi Musharavati,Muhammad E. H. Chowdhury +11 more
TL;DR: In this paper , the authors propose the NABNet architecture which utilizes Convolutional LSTM and Attention Guidance concepts for improving construction error accumulating due to ABP phase lag during segmentation, which is trained on a large, variable dataset with highly varying PPG and ECG waveforms to enhance the robustness and generalizability of the model.
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A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform
Tasbiraha Athaya,Sunwoong Choi +1 more
TL;DR: The purpose of this review is to summarize the current state of knowledge regarding the BP waveforms, three methodologies used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs.
PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods
TL;DR: PulseDB as discussed by the authors is the largest cleaned dataset for measuring the generalizability of cuff-less blood pressure estimation models with respect to the number of subjects and the applied preprocessing steps for the data that is eventually used for training and testing.
Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges
TL;DR: A literature review of the studies conducted on the cuffless non-invasive measurement of BP using biomedical signals is presented in this paper , where the authors focus on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies.
References
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TL;DR: In this paper , a deep neural network (called MLP-BP) was proposed to estimate BP from plethysmography (PPG) and electrocardiograph (ECG) signal.
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Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography
TL;DR: An inhomogeneous resilient backpropagation (IRBP) algorithm to calculate the weight of hidden layer nodes is investigated and it is found that the IRBP improves the convergence speed and reconstruction accuracy.
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