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arXiv Machine Learning

Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification

arXiv:2608. 12117v1 Announce Type: new Abstract: Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system.

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arXiv Machine Learning
Sep 14

Impact of Multiple Non-Invasive Biosignals on Cardiovascular Biomarker Estimation via Simulation-Based Inference

The study investigates how adding ballistocardiography (BCG) signals to photoplethysmography (PPG) and arterial pressure wave (APW) signals affects the estimation of cardiovascular biomarkers. Using a unified whole‑body cardiovascular model, synthetic PPG, APW, and BCG signals were generated and analyzed with neural posterior estimation and simulation‑based inference. Results show that incorporating BCG significantly improves estimation accuracy, converting multimodal posterior distributions from PPG/APW alone into unimodal distributions, thereby enhancing the reliability of cardiovascular biomarker estimation.

By Shusaku Maeda, Masahiro Nakano, Tomoharu Iwata, Kenji Komiya, Ryo Nishikimi, Kunio Kashino
multimodal
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arXiv Machine Learning
Sep 23

Robust Photoplethysmography Signal Denoising via Mamba Networks

The paper introduces DPNet, a Mamba-based deep learning framework for denoising photoplethysmography (PPG) signals while preserving physiological information. It incorporates a scale‑invariant signal‑to‑distortion ratio loss and an auxiliary heart‑rate predictor to enhance waveform fidelity and maintain heart‑rate accuracy. Experiments on the BIDMC dataset show that DPNet outperforms conventional filtering and existing neural models in robustness against synthetic noise and real‑world motion artifacts, making it suitable for wearable healthcare systems.

By I Chiu, Yu-Tung Liu, Kuan-Chen Wang, Hung-Yu Wei, Yu Tsao
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arXiv Machine Learning
Jun 10

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

arXiv:2606. 11125v1 Announce Type: cross Abstract: 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.

By Yidan Shen, Neville Mathew, Maham Rahimi, Deependra Dhakal, George Zouridakis, Xin Fu, Renjie Hu
llms
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arXiv AI
Sep 7

A Hybrid Predictive Ensemble of Machine Learning and Deep Neural Networks for Early Cardiovascular Disease Risk Assessment

The paper presents a hybrid predictive ensemble that merges machine learning and deep neural network techniques to detect and prognosticate cardiovascular disease early. It processes real‑time physiological data from IoMT devices, applying preprocessing, feature selection, and optimized classifiers (SVM, Random Forest, XGBoost) within an ensemble architecture. The cloud‑based system achieves higher accuracy, fewer false positives, and consistent performance on real‑world datasets, supporting continuous patient monitoring and clinical decision support.

By Balaji Venkateswaran
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arXiv Machine Learning
Jul 28

Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning

arXiv:2602. 04266v2 Announce Type: replace-cross Abstract: Aortic valve disease (AVD) represents a major public health burden, while its diagnosis relies on echocardiography, which is limited by cost and specialist expertise, restricting scalable screening and risk stratification.

By Jiaze Wang, Qinghao Zhao, Zizheng Chen, Zhejun Sun, Deyun Zhang, Yuxi Zhou, Shenda Hong
fine-tuning
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