# DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals

arXiv: https://arxiv.org/abs/2606.11125 · HTML: https://mlsanity.com/paper/2606.11125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study](https://mlsanity.com/paper/2502.19167/) (2025)
- [NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification](https://mlsanity.com/paper/2506.14970/) (2025)
- [Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)](https://mlsanity.com/paper/2508.17207/) (2025)
- [TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing](https://mlsanity.com/paper/2404.15352/) (2024)
- [Benchmarking and Enhancing PPG-Based Cuffless Blood Pressure Estimation Methods](https://mlsanity.com/paper/2602.04725/) (2026)

## Abstract (arXiv)

Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation. However, even with recent progress, many PPG-based models are trained with BP regression alone and may rely on amplitude-dominated shortcuts. In addition, demographic covariates that systematically modulate vascular compliance are often incorporated only via late fusion, limiting subject-specific representation learning. We propose a Transformer-based network for cuffless BP estimation from PPG signal, leveraging self-attention to capture long-range dependencies across multiple cardiac cycles. To account for subject-specific vascular differences, the model is conditioned on demographics via FiLM-style feature modulation applied through the attention and feed-forward sublayers of Transformer blocks. In addition, we add an auxiliary morphology head to guide the model to attend to BP-relevant waveform morphology associated with arterial stiffness and wave reflection. Under calibration-based evaluation protocols on the large-scale PulseDB dataset, the proposed method achieves MAE of 4.56 mmHg for systolic BP and 2.62 mmHg for diastolic BP, reducing errors by 47% and 50% compared with prior demographic-enhanced PPG baselines. The resulting lightweight, single-sensor model supports scalable and clinically grounded cuffless BP estimation in calibration-enabled deployment settings.