AIMC Topic: Blood Pressure Determination

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Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

IEEE journal of biomedical and health informatics
This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by int...

DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model.

Medical & biological engineering & computing
Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introd...

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement.

IEEE journal of biomedical and health informatics
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address t...

A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning.

Artificial intelligence in medicine
Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable healthcare electronics. To assess the central aortic pressure using radial-based measurements, there i...

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload.

Sensors (Basel, Switzerland)
Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilita...

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

IEEE journal of biomedical and health informatics
Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly...

Artificial Intelligence-Derived Risk Prediction: A Novel Risk Calculator Using Office and Ambulatory Blood Pressure.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Quantification of total cardiovascular risk is essential for individualizing hypertension treatment. This study aimed to develop and validate a novel, machine-learning-derived model to predict cardiovascular mortality risk using office bl...

Mechanistic Assessment of Cardiovascular State Informed by Vibroacoustic Sensors.

Sensors (Basel, Switzerland)
Monitoring blood pressure, a parameter closely related to cardiovascular activity, can help predict imminent cardiovascular events. In this paper, a novel method is proposed to customize an existing mechanistic model of the cardiovascular system thro...

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias.

Sensors (Basel, Switzerland)
Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices...