ArticleSensors (Basel, Switzerland)2020
Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Dynamic beat-to-beat blood pressure estimation using a multi-modal wearable deep learning approach.Physiological measurement · 2026Article
- Towards trustworthy AI-driven cuffless blood pressure monitoring.NPJ digital medicine · 2026Article
- Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation.Nature reviews. Cardiology · 2025Review
- Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure.Bioengineering (Basel, Switzerland) · 2025Article
- A review: Blood pressure monitoring based on PPG and circadian rhythm.APL bioengineering · 2024Review
- A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.Sensors (Basel, Switzerland) · 2024Article
- Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring.NPJ digital medicine · 2023Review
- Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet.Bioengineering (Basel, Switzerland) · 2023Article
- A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram.Scientific data · 2023Article
- Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.Sensors (Basel, Switzerland) · 2023Article
- Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges.Bioengineering (Basel, Switzerland) · 2022Review
- A Model for Predicting Cervical Cancer Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2022Article
- A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform.Sensors (Basel, Switzerland) · 2022Review
- Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Special Issue "Advanced Signal Processing in Wearable Sensors for Health Monitoring".Sensors (Basel, Switzerland) · 2022Article
- Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals.BioMed research international · 2022Review
- Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder.Sensors (Basel, Switzerland) · 2021Article
- Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.Sensors (Basel, Switzerland) · 2021Article
- Computerized Tomography Image Feature under Convolutional Neural Network Algorithm Evaluated for Therapeutic Effect of Clarithromycin Combined with Salmeterol/Fluticasone on Chronic Obstructive Pulmonary Disease.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.
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