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 …

Blood pressure monitoring is one avenue to monitor people's health conditions. Early
detection of abnormal blood pressure can help patients to get early treatment and reduce …

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies
over days, minutes, or even seconds for short-term cases. Most of photoplethysmography …

Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP)
estimation models have been developed based on physiological parameters extracted from …

Recent works on the machine learnings designed for cuffless blood pressure (BP)
estimation based on measured photoplethysmogram (PPG) waveforms are reviewed by this …

Cuffless blood pressure (BP) measurement could improve hypertension awareness and
control and is being widely pursued. Some have proposed to estimate BP from the …

In this study, we propose a blood pressure estimation that employs a combined 1-D squeeze
and excitation network (SENet-LSTM) architecture. Combining photoplethysmography …

The regression and classification performances of deep-learning algorithms depend on the
tuning of the model and require significant intervention to train the model. A blood pressure …

Remote health monitoring systems play an important role in the healthcare sector. Edge
computing is a key enabler for realizing these systems, where it is required to collect big …

Background and objective Hypertension is a potentially dangerous health condition that can
be detected by measuring blood pressure (BP). Blood pressure monitoring and …