TriDP-PTM: a three-stage distortion-perception tradeoff guides the pre-training model for radar cardiac sensing
2026-05-25 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method to monitor heart health without touching the body, using radar to capture heart signals. They combined two approaches: one that looks at radar data directly and another that converts radar signals into ECG (heart electrical activity) signals before analysis. Their system, called TriDP-PTM, uses medical knowledge about ECG patterns to improve accuracy. Tests showed that using the radar-to-ECG path worked better for tasks like spotting heart patterns and estimating blood pressure. They also found that balancing signal clarity and meaningful medical features is crucial for good results.
cardiovascular diseasesnon-contact cardiac monitoringradar-based sensingECG (electrocardiogram)distortion-perception trade-offmulti-scale fusionfeature discriminatorwaveform segmentationblood pressure regressionpre-training model
Authors
Jinye Li, Aidong Men, Yang Liu, Qingchao Chen
Abstract
Cardiovascular diseases (CVDs) remain a leading cause of death globally, necessitating continuous, accurate non-invasive cardiac monitoring. While non-contact radar-based approaches show great promise, they often employ a single "distortion-driven" or "perception-driven" paradigm, frequently facing a trade-off between "low distortion but weak semantic information" and "high perceptual fidelity but poor interpretability." To address this, we propose a Three-stage Distortion-Perception Pre-Training Model (TriDP-PTM), a radar-based multi-scale fusion dual-path framework that systematically compares the "direct radar-to-task" path against an "indirect radar-to-ECG-to-task" path. By integrating an ECG generator with a feature discriminator to form a composite loss function, our approach effectively incorporates medical priors - such as ECG morphology and rhythm - into downstream tasks. Through empirical analysis, we reveal that this trade-off manifests in three distinct phases (Positive-Sum, Coopetitive, and Negative-Sum), showing optimal downstream clinical accuracy typically emerges in the coopetitive stage. Extensive experiments on a dataset involving 30 subjects across 5 physiological states reveal that the indirect path consistently outperforms the direct path in diverse tasks, achieving 0.80 mean IoU in waveform segmentation, 98.3% average classification accuracy across four tasks, and a 56% MAE reduction in blood pressure regression compared to the strongest baselines. These findings validate our framework and indicate that, within the indirect radar-to-ECG pathway, appropriately weighting distortion and perception losses to operate in the coopetitive regime is critical for achieving both clinically interpretable ECG morphology and strong downstream accuracy in non-contact cardiac monitoring.