Bimodal ECG and PCG Cardiovascular Disease Detection: Exploring the Potential and Modality Contribution.
Early detection of cardiovascular diseases (CVDs) is crucial for improving patient outcomes and alleviating healthcare burdens. Electrocardiograms (ECGs) and phonocardiograms (PCGs) offer low-cost, non-invasive, and easily integrable solutions for preventive care settings. In this work, we propose a...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 12 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
9/12/2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187971626&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187971626 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 9/12/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187971626 187971626 187971626 10.1007/s10916-025-02245-5 187971626 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Bimodal ECG and PCG Cardiovascular Disease Detection: Exploring the Potential and Modality Contribution. aug: au: Calzoni, Alessia Savardi, Mattia Silvestri, Marco Benini, Sergio Signoroni, Alberto affil: https://ror.org/02q2d2610 Department of Information Engineering, University of Brescia, Via Branze 38, 25123, Brescia, Italy sug: subj: Cardiovascular Diseases Diagnosis Electrocardiography Methods Early Diagnosis Audiorecording Deep Learning Convolutional Neural Networks Human Funding Source Comparative Studies Exploratory Research Descriptive Statistics Signal Processing, Computer Assisted Quality Improvement Heart Rate Wilcoxon Rank Sum Test Confidence Intervals Sensitivity and Specificity ab: Early detection of cardiovascular diseases (CVDs) is crucial for improving patient outcomes and alleviating healthcare burdens. Electrocardiograms (ECGs) and phonocardiograms (PCGs) offer low-cost, non-invasive, and easily integrable solutions for preventive care settings. In this work, we propose a novel bimodal deep learning model that combines ECG and PCG signals to enhance the early detection of CVDs. To address the challenge of limited bimodal data, we fine-tuned a Convolutional Neural Network (CNN) pre-trained on large-scale audio recordings, leveraging all publicly available unimodal PCG datasets. This PCG branch was then integrated with a 1D-CNN ECG branch via late fusion. Evaluated on an augmented version of MITHSDB, currently the only publicly available bimodal dataset, our approach achieved an AUROC of 96.4%, significantly outperforming ECG-only and PCG-only models by approximately 3%pts and 11%pts, respectively. To interpret the model's decisions, we applied three explainability techniques, quantifying the relative contributions of the electrical and acoustic features. Furthermore, by projecting the learned embeddings into two dimensions using UMAP, we revealed clear separation between normal and pathological samples. Our results conclusively demonstrate that combining ECG and PCG modalities yields substantial performance gains, with explainability and visualization providing critical insights into model behavior. These findings underscore the importance of multimodal approaches for CVDs diagnosis and prevention, and strongly motivate the collection of larger, more diverse bimodal datasets for future research. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|