Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation
- URL: http://arxiv.org/abs/2608.18639v1
- Date: Wed, 19 Aug 2026 07:37:45 GMT
- Title: Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation
- Abstract summary: Non-invasive continuous blood pressure (BP) monitoring using photoplethysmography is a promising alternative to cuff-based measurements.<n>Existing PPG-based BP estimation studies rely on aggregated performance over entire evaluation intervals.<n>We propose a fluctuation-aware evaluation framework for PPG-based BP estimation based on time-series change point detection.
- Score: 4.782662383035205
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Non-invasive continuous blood pressure (BP) monitoring using photoplethysmography (PPG) is a promising alternative to cuff-based measurements. However, existing PPG-based BP estimation studies predominantly rely on aggregated performance metrics (e.g., mean absolute error) computed over entire evaluation intervals, which can obscure model failures during rapid BP fluctuations and limit clinical relevance. In this work, we propose a fluctuation-aware evaluation framework for PPG-based BP estimation based on time-series change point detection. Instead of heuristic BP thresholding (e.g., $Δ\mathrm{BP} > 10\mathrm{mmHg}$), we identify BP change points by capturing abrupt distributional shifts in BP trajectories and evaluate estimation performance specifically during these fluctuation periods. Our analysis shows that several state-of-the-art models exhibit substantial performance degradation around BP change points, and that periodic test-time calibration is insufficient to handle such dynamic BP variations. To address this limitation, we introduce a targeted re-calibration framework triggered by detected BP change points, improving robustness without modifying model architectures. To the best of our knowledge, this is the first systematic evaluation of PPG-based BP estimation from a BP change point perspective, highlighting the importance of fluctuation-aware evaluation and calibration for real-world continuous BP monitoring.
Related papers
- DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals [4.839226773937583]
Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making.<n>Photoplethysmography ( PPG) enables low-cost, wearable-friendly cuffless BP estimation.<n>We propose a Transformer-based network for cuffless BP estimation from PPG signal.
arXiv Detail & Related papers (2026-06-09T17:23:07Z) - Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
arXiv Detail & Related papers (2026-03-04T14:48:53Z) - Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [46.36100528165335]
Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
arXiv Detail & Related papers (2025-02-10T13:33:12Z) - A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting [6.311504297463515]
Continuous blood pressure (BP) monitoring is essential for timely diagnosis and intervention in critical care settings.
In this work, we propose a personalized BP forecasting model mainly using electrocardiogram (ECG) and photoplethysmogram ( photoplethysmogram) signals.
Experiments are conducted on datasets collected from three diverse scenarios with BP measurements from 60 subjects total.
arXiv Detail & Related papers (2024-09-07T04:24:15Z) - Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation [55.8930142490617]
We propose a decoder for QLDPC codes based on BP guided decimation (BPGD)
BPGD significantly reduces the BP failure rate due to non-convergence.
arXiv Detail & Related papers (2023-12-18T05:58:07Z) - Regression or Classification? Reflection on BP prediction from PPG data
using Deep Neural Networks in the scope of practical applications [3.867363075280544]
Photoplethysmographic signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring.
In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure estimation.
We argue that BP classification might provide diagnostic value that is equivalent to regression in many clinically relevant scenarios.
arXiv Detail & Related papers (2022-04-12T08:07:38Z) - Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in
Partially Observed Markov Decision Processes [65.91730154730905]
In applications of offline reinforcement learning to observational data, such as in healthcare or education, a general concern is that observed actions might be affected by unobserved factors.
Here we tackle this by considering off-policy evaluation in a partially observed Markov decision process (POMDP)
We extend the framework of proximal causal inference to our POMDP setting, providing a variety of settings where identification is made possible.
arXiv Detail & Related papers (2021-10-28T17:46:14Z) - A Deep Learning Approach to Predict Blood Pressure from PPG Signals [10.028103259763352]
Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions.
We propose an advanced data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals.
arXiv Detail & Related papers (2021-07-30T22:45:34Z) - When in Doubt: Neural Non-Parametric Uncertainty Quantification for
Epidemic Forecasting [70.54920804222031]
Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions.
Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations.
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
arXiv Detail & Related papers (2021-06-07T18:31:47Z) - Ambulatory blood pressure monitoring versus office blood pressure
measurement: Are there sex differences? [55.41644538483948]
Office Blood Pressure Measurement (OBP) is a technique performed in-office with the sphygmomanometer, while Ambulatory Blood Pressure Monitoring (ABPM) is a technique that measures blood pressure during 24h.
The aim of this study is to examine the possible influence of sex on the discrepancies between OBP and ABPM in 872 subjects with known or suspected hypertension.
arXiv Detail & Related papers (2021-06-04T10:09:44Z) - Continuous Monitoring of Blood Pressure with Evidential Regression [19.92542487970484]
Photoplethysmogram (MIC) signal-based blood pressure estimation is a promising candidate for modern BP measurements.
The proposed method provides the reliability of the predicted BP by estimating its uncertainty to help diagnose medical condition.
arXiv Detail & Related papers (2021-02-06T09:09:31Z) - On Signal-to-Noise Ratio Issues in Variational Inference for Deep
Gaussian Processes [55.62520135103578]
We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues.
We show that our fix can lead to consistent improvements in the predictive performance of DGP models.
arXiv Detail & Related papers (2020-11-01T14:38:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.