[HTML][HTML] A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data
The use of machine learning techniques in medicine has increased in recent years due to a
rise in publicly available datasets. These techniques have been applied in high blood …
rise in publicly available datasets. These techniques have been applied in high blood …
A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors
Over the past two decades, machine learning systems have been proliferating in the
healthcare industry domains, such as digital health, fitness tracking, patient monitoring, and …
healthcare industry domains, such as digital health, fitness tracking, patient monitoring, and …
Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood
pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff …
pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff …
Cuffless blood pressure monitoring: promises and challenges
Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP
monitoring, at 15-to 30-minute intervals usually over 24 hours, provides sufficiently …
monitoring, at 15-to 30-minute intervals usually over 24 hours, provides sufficiently …
Advancement in the cuffless and noninvasive measurement of blood pressure: A review of the literature and open challenges
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 …
cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the …
A clustering based Swarm Intelligence optimization technique for the Internet of Medical Things
Abstract Internet of Medical Things (IoMT) is a recently introduced paradigm which has
gained relevance as an emerging technology for widely connected and heterogeneous …
gained relevance as an emerging technology for widely connected and heterogeneous …
Survey and evaluation of hypertension machine learning research
Background Machine learning (ML) is pervasive in all fields of research, from automating
tasks to complex decision‐making. However, applications in different specialities are …
tasks to complex decision‐making. However, applications in different specialities are …
Hybrid CNN-SVR blood pressure estimation model using ECG and PPG Signals
S Rastegar, H Gholam Hosseini, A Lowe - Sensors, 2023 - mdpi.com
Continuous blood pressure (BP) measurement is vital in monitoring patients' health with a
high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular …
high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular …
Study on the relationship between body mass index and blood pressure indices in children aged 7–17 during COVID-19
SJ Mao, GP Qian, KW **ao, H Xu, H Zhou… - Frontiers in Public …, 2024 - frontiersin.org
Background To explore the relationship between body mass index (BMI), age, sex, and
blood pressure (systolic blood pressure, SBP; diastolic blood pressure, DBP) in children …
blood pressure (systolic blood pressure, SBP; diastolic blood pressure, DBP) in children …
Predictive modeling of biomedical temporal data in healthcare applications: review and future directions
Predictive modeling of clinical time series data is challenging due to various factors. One
such difficulty is the existence of missing values, which leads to irregular data. Another …
such difficulty is the existence of missing values, which leads to irregular data. Another …