Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
January 1, 2026IEEE AccessOpen Access

Deep Learning (DL) models perform better than traditional Machine Learning (ML) models for cuffless blood pressure estimation, especially in the case of multimodal ECG-PPG fusion.

View Full Paper
Ask AI
Bookmark
Share

Why the study?

Continuous blood pressure monitoring is necessary for conditions like hypertension but difficult with a sphygmomanometer, prompting the need to review non-invasive AI-based BP estimation techniques using ECG and PPG.

Do AI-based models using ECG and PPG accurately estimate cuffless blood pressure?

Comparison

AI-based BP estimation techniques using ECG, PPG, and their combination

Design

PRISMA-guided systematic review

Authors

CChandanaSMSukesh Rao MSBSoumya J. Bhat

Discussion

Loading...

Member takes

Overview

DL models merit priority for cuffless BP device development; extends ML benchmarks by confirming multimodal ECG-PPG fusion gains.

Structured PICO

Do AI-based models using ECG and PPG accurately estimate cuffless blood pressure?

P
Population
Studies evaluating cuffless blood pressure estimation techniques
I
Intervention
Artificial Intelligence (AI) based models (Deep Learning and Machine Learning) using Electrocardiography (ECG), Photoplethysmography (PPG), and their combination
C
Comparator
Traditional Machine Learning models (compared against Deep Learning models)
O
Outcome
Performance and accuracy of blood pressure estimationsurrogate

Deep learning models utilizing multimodal ECG-PPG fusion offer superior performance for continuous, non-invasive cuffless blood pressure estimation compared to traditional machine learning approaches.

Cite This Study

Chandana et al. (2026) studied this question.

synapsesocial.com/papers/6a8a664cc2b067567ebd5740https://doi.org/10.1109/access.2026.3722377
View Full Paper
Ask AI
Bookmark
Share