arXiv preprint · 2025
When Validation Fails: Cross-Institutional Blood Pressure Prediction and the Limits of Electronic Health Record-Based Models
Azam, M.B. & Singh, S.I. ↗
131K+
ECG-Linked Patient Records
MIMIC-IV research cohort
4
Research Datasets
MIMIC-IV · eICU · AI-READI · D1NAMO
48
Model Configurations
SAGE-Net evaluation
2
Publications
arXiv · BMC (2025–2026)
What we're building
An independent healthcare AI startup.
Routine glucose monitoring usually means a finger-prick or a sensor inserted under the skin. Daibeats is investigating a different route: estimating glucose from the electrocardiogram (ECG) — a signal already recorded by hospital monitors, clinical ECG machines, and a growing number of wearables.
We are building software, not a new sensor. Our goal is a glucose-estimation tool that works from existing ECG data, starting with research and clinical partners and moving towards a practical healthcare product as the evidence matures.
Daibeats technology is under research and validation. It is not a medical device and has not received regulatory approval.
How it works
From electrical signal to glucose estimate.
A four-stage research pipeline that investigates how much glycemic information can be recovered from ECG recordings.
Our story
From research to startup.
Daibeats builds on research into ECG-based glucose estimation that was carried out before the company was founded, as part of Md Basit Azam's doctoral work in the Department of Computer Science & Engineering, Tezpur University.
In 2026, Daibeats was founded as an independent startup to develop this technology into a practical healthcare product. Daibeats is a separate venture and is not a university lab.
Current stage
Research & product validation.
Early-stage · Bootstrapped · Founded 2026. Daibeats is developing and validating its ECG-based glucose estimation technology using clinical and wearable datasets.
01 · Completed on retrospective data
Model development
51-feature ECG pipeline and safety-aware models developed on clinical and wearable datasets.
02 · In progress
Validation
External, subject-independent validation across datasets, reported with clinically meaningful error metrics.
03 · Planned
Product development
Turning validated models into a practical software product for research and clinical partners.
Founder
Who is behind Daibeats.
Md Basit Azam
Founder & Lead Researcher, Daibeats
PhD researcher in the Department of Computer Science & Engineering, Tezpur University, working on machine learning for physiological signals. Leads Daibeats' research and product development, from clinical dataset engineering and ECG feature pipelines to safety-aware modelling and external validation.