# Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

arXiv: https://arxiv.org/abs/2005.03357 · HTML: https://mlsanity.com/paper/2005.03357/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 13 of 13)

- [Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals](https://mlsanity.com/paper/2412.09758/) (2024)
- [GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals](https://mlsanity.com/paper/2503.08015/) (2025)
- [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography](https://mlsanity.com/paper/2202.05735/) (2022)
- [A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals](https://mlsanity.com/paper/2111.08480/) (2021)
- [A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images](https://mlsanity.com/paper/2106.14207/) (2021)
- [Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals](https://mlsanity.com/paper/2510.14254/) (2025)
- [A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations](https://mlsanity.com/paper/2203.15151/) (2022)
- [ReViSe: Remote Vital Signs Measurement Using Smartphone Camera](https://mlsanity.com/paper/2206.08748/) (2022)
- [Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features](https://mlsanity.com/paper/2308.01930/) (2023)
- [Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios](https://mlsanity.com/paper/2512.01653/) (2025)
- [A Novel Non-Invasive Estimation of Respiration Rate from Photoplethysmograph Signal Using Machine Learning Model](https://mlsanity.com/paper/2102.09483/) (2021)
- [PPG Signals for Hypertension Diagnosis: A Novel Method using Deep Learning Models](https://mlsanity.com/paper/2304.06952/) (2023)
- [Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact](https://mlsanity.com/paper/2504.02735/) (2025)

## Abstract (arXiv)

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.