Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

L Zhao, C Liang, Y Huang, G Zhou, Y **ao, N Ji… - npj Digital …, 2023‏ - nature.com
Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early
diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor …

Integrating machine learning and biosensors in microfluidic devices: a review.

G Antonelli, J Filippi, M D'Orazio, G Curci… - Biosensors and …, 2024‏ - Elsevier
Microfluidic devices are increasingly widespread in the literature, being applied to numerous
exciting applications, from chemical research to Point-of-Care devices, passing through drug …

A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram

S González, WT Hsieh, TPC Chen - Scientific Data, 2023‏ - nature.com
Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored
non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus …

A reinforcement learning based artificial bee colony algorithm with application in robot path planning

Y Cui, W Hu, A Rahmani - Expert Systems with Applications, 2022‏ - Elsevier
Artificial bee colony (ABC) algorithm is a popular optimization algorithm with excellent
exploration ability and various applications. Nevertheless, its effectiveness is limited by the …

Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation

K Sel, A Mohammadi, RI Pettigrew, R Jafari - npj Digital Medicine, 2023‏ - nature.com
The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of
off-the-shelf wearables that began a decade ago, has created immense opportunities to …

Revolutionizing cardiology through artificial intelligence—Big data from proactive prevention to precise diagnostics and cutting-edge treatment—A comprehensive …

E Stamate, AI Piraianu, OR Ciobotaru, R Crassas… - Diagnostics, 2024‏ - mdpi.com
Background: Artificial intelligence (AI) can radically change almost every aspect of the
human experience. In the medical field, there are numerous applications of AI and …

An efficient hybrid LSTM-ANN joint classification-regression model for PPG based blood pressure monitoring

NF Ali, M Atef - Biomedical Signal Processing and Control, 2023‏ - Elsevier
This paper investigates the importance of classification in optimizing the estimation accuracy
of blood pressure (BP) using photoplethysmography (PPG) signal features, with the aim of …

Advancement in the cuffless and noninvasive measurement of blood pressure: A review of the literature and open challenges

MMR Khan Mamun, A Sherif - Bioengineering, 2022‏ - mdpi.com
Hypertension is a chronic condition that is one of the prominent reasons behind
cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the …

Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives

K Qin, W Huang, T Zhang, S Tang - Artificial Intelligence Review, 2023‏ - Springer
Blood pressure (BP) estimation is one of the most popular and long-standing topics in health-
care monitoring area. The utilization of machine learning (ML) and deep learning (DL) for BP …

A review of deep learning methods for photoplethysmography data

G Nie, J Zhu, G Tang, D Zhang, S Geng, Q Zhao… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Photoplethysmography (PPG) is a highly promising device due to its advantages in
portability, user-friendly operation, and non-invasive capabilities to measure a wide range of …