This paper presents a deep learning model'PP-Net'which is the first of its kind, having the
capability to estimate the physiological parameters: Diastolic blood pressure (DBP), Systolic …

Cardiovascular disease (CVD) is one of the most serious diseases threatening human
health. Arterial blood pressure (ABP) waveforms, containing vivid cardiovascular …

A new method for accurately estimating heart rates based on a single
photoplethysmography (PPG) signal and accelerations is proposed in this study …

In this paper, we propose a continuous physiological parameter estimation model based on
a deep learning network for photoplethysmography (PPG) sensor signals. Signals of 8-s …

Objective. The quality of the arterial blood pressure (ABP) waveform is crucial for predicting
the value of blood pressure. The ABP waveform is predicted through experiments, and then …

Physical activity can severely influence the quality of photoplethysmographic (PPG) signals
due to motion artefacts (MA). This study aims to extract heart rate (HR) and respiration rate …

In recent years, vector-to-vector mapping-based raw waveform biosignal enhancement
methods have gained significant attention in remote health monitoring system. In this paper …

The report presents an algorithm for noise reduction in photoplethysmographic signals,
applying a hybrid method based on wavelet analysis and adaptive processing threshold …

Pulse signals are widely used to evaluate the status of the human cardiovascular,
respiratory, and circulatory systems. In the process of being collected, the signals are usually …

Pervasive monitoring of vitals especially blood pressure and heart rate are considered the
most valuable parameters because they are the most important biomarkers as well as risk …