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Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A
Insights
Deep learning models show promise for improving non-invasive blood pressure (NIBP) estimation, offering potential benefits for cardiovascular health monitoring. Further research is needed to address limitations for widespread adoption of these AI-based methods.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence
- Cardiovascular Health
Background:
- Cardiovascular disease is the leading global cause of death, with elevated blood pressure (BP) as the primary risk factor.
- Automated non-invasive blood pressure (NIBP) devices are increasingly used for home monitoring, with cuff-based methods dominating.
- Machine learning and AI, particularly deep learning, offer novel approaches for NIBP estimation using large datasets.
Purpose of the Study:
- To review Artificial Intelligence (AI)-based blood pressure estimation methods.
- To focus on recent advances in deep learning approaches for NIBP estimation.
- To discuss the strengths, weaknesses, and future directions of AI in BP monitoring.
Main Methods:
- Literature review of AI-based NIBP estimation techniques.
- Analysis of various deep learning architectures and methodologies.
- Discussion of challenges and potential solutions for AI adoption in BP estimation.
Main Results:
- Deep learning techniques demonstrate significant potential for enhancing NIBP estimation accuracy.
- Various AI architectures offer different strengths in data classification and feature extraction for BP monitoring.
- Identified limitations exist that may impede the widespread clinical adoption of deep learning in this field.
Conclusions:
- Deep learning presents plausible benefits for the advancement of non-invasive blood pressure estimation.
- Addressing identified limitations is crucial for the successful integration of deep learning into routine cardiovascular health monitoring.
- Future frameworks are suggested to overcome challenges and promote the adoption of AI-driven NIBP solutions.
Abstract:
Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.
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