Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography

• URL: http://arxiv.org/abs/2507.09009v1
• Date: Fri, 11 Jul 2025 20:24:10 GMT
• Title: Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography
• Authors: Zhengxiao He, Huayu Li, Geng Yuan, William D. S. Killgore, Stuart F. Quan, Chen X. Chen, Ao Li,
• Abstract summary: We developed a self-supervised deep learning model that extracts meaningful patterns from multi-modal signals.<n> ECG-derived features were predictive of both prevalent and incident cardiac conditions.<n>EEG-derived features were predictive of incident hypertension and CVD mortality.
• Score: 11.59070680647139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Methods: We developed a self-supervised deep learning model that extracts meaningful patterns from multi-modal signals (Electroencephalography (EEG), Electrocardiography (ECG), and respiratory signals). The model was trained on data from 4,398 participants. Projection scores were derived by contrasting embeddings from individuals with and without CVD outcomes. External validation was conducted in an independent cohort with 1,093 participants. The source code is available on https://github.com/miraclehetech/sleep-ssl. Results: The projection scores revealed distinct and clinically meaningful patterns across modalities. ECG-derived features were predictive of both prevalent and incident cardiac conditions, particularly CVD mortality. EEG-derived features were predictive of incident hypertension and CVD mortality. Respiratory signals added complementary predictive value. Combining these projection scores with the Framingham Risk Score consistently improved predictive performance, achieving area under the curve values ranging from 0.607 to 0.965 across different outcomes. Findings were robustly replicated and validated in the external testing cohort. Conclusion: Our findings demonstrate that the proposed framework can generate individualized CVD risk scores directly from PSG data. The resulting projection scores have the potential to be integrated into clinical practice, enhancing risk assessment and supporting personalized care.

Related papers

• Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning [3.4335475695580127]
  Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality.<n>We developed a machine learning framework to predict 30-day in-hospital mortality among ICU patients with HKD.
  arXiv  Detail & Related papers  (2025-07-25T00:48:23Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease [0.0]
  This study aims to develop and evaluate a sophisticated machine learning prediction model to assess postoperative stroke risk.<n>The dataset has 70% training and 30% test. Numerical values were normalized, whereas categorical variables were one-hot encoded.<n> Logistic Regression, XGBoost, SVM, and CatBoost were employed for predictive modeling, and SHAP analysis assessed stroke risk for each variable.
  arXiv  Detail & Related papers  (2025-03-15T02:50:32Z)
• A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans [12.652540031719571]
  We propose a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker.
  arXiv  Detail & Related papers  (2024-10-24T10:06:45Z)
• Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
  Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
  arXiv  Detail & Related papers  (2024-10-18T12:37:51Z)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study [0.46180371154032895]
  The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments.
  arXiv  Detail & Related papers  (2021-04-20T16:01:50Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

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.