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Artificial Intelligence and Sensors in Cardiovascular Disease Monitoring
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Artificial Intelligence and Sensors in Cardiovascular Disease Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 25 October 2026 | Viewed by 1364

Special Issue Editor

Dr. Alauddin Bhuiyan

E-Mail Website
Guest Editor
iHealthScreen Inc, Richmond Hill, New York, NY, USA
Interests: artificial intelligence; sensors; deep neural network; fundus imaging; medical imaging; image processing; computer vision; pattern recognition; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on artificial intelligence-based systems and sensors for screening and identification of individuals at risk of cardiovascular diseases (CVDs), stroke, and heart attack/myocardial infarction (MI). The publications can include AI or any sensing technologies for the automatic detection of subjects with CVDs or at risk of incidence of CVDs. The studies can utilize retrospective or prospective datasets to validate the results or the automatic detection of the risk factors, which could be based on traditional or novel images.

We welcome original research articles and reviews that contribute to the development of innovative, sensor-based solutions powered by AI. Topics of interest include, but are not limited to, the following:

  1. Development of novel sensor technologies for cardiovascular monitoring.
  2. AI-based approaches for analyzing sensor data to detect early signs of CVDs.
  3. Wearable and non-invasive sensing systems for real-time health monitoring.
  4. Machine learning and deep learning applications in cardiovascular diagnostics.
  5. Sensor fusion techniques for comprehensive cardiovascular assessment, and ECG, EKG data analysis.

Dr. Alauddin Bhuiyan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cardiovascular monitoring
  • artificial intelligence
  • wearable and non-invasive sensing systems
  • real-time health monitoring
  • machine learning
  • deep learning
  • cardiovascular diagnostics
  • sensor fusion
  • ECG data analysis
  • CVD prediction
  • heart attack prediction
  • stroke prediction
  • CVD diagnosis or detection

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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22 pages, 3648 KB  
Article
Enhancing ECG Classification Generalization Through Unified Multi-Dataset Training
by Minchan Kim and Miyoung Shin
Sensors 2026, 26(6), 1830; https://doi.org/10.3390/s26061830 - 13 Mar 2026
Viewed by 424
Abstract
Atrial fibrillation (AF) is one of the most prevalent and clinically significant cardiac arrhythmias, and electrocardiography (ECG) is widely used for its detection. However, existing models often exhibit performance degradation when applied to unseen data due to dataset-specific biases and distributional shifts. This [...] Read more.
Atrial fibrillation (AF) is one of the most prevalent and clinically significant cardiac arrhythmias, and electrocardiography (ECG) is widely used for its detection. However, existing models often exhibit performance degradation when applied to unseen data due to dataset-specific biases and distributional shifts. This limited generalization remains a major obstacle to reliable clinical deployment. To address this, we propose a multi-dataset ECG classification framework designed to improve cross-dataset robustness. The model employs supervised contrastive learning and layer-wise normalization to stabilize training and mitigate the influence of domain-specific variations. The proposed approach was evaluated under a Leave-One-Dataset-Out setting, achieving an average accuracy of 97.5% and an F1-score of 89.3%. It consistently demonstrated superior performance compared with single-dataset training and naïve multi-dataset aggregation. These results indicate that the proposed framework can contribute to more stable automated AF detection across diverse clinical environments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors in Cardiovascular Disease Monitoring)
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Review

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24 pages, 2504 KB  
Review
AI-Enabled Sensor Technologies for Remote Arrhythmic Monitoring in High-Risk Cardiomyopathy Genotypes
by Nardi Tetaj, Andrea Segreti, Francesco Piccirillo, Aurora Ferro, Virginia Ligorio, Alberto Spagnolo, Michele Pelullo, Simone Pasquale Crispino and Francesco Grigioni
Sensors 2026, 26(7), 2078; https://doi.org/10.3390/s26072078 - 26 Mar 2026
Viewed by 512
Abstract
Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are [...] Read more.
Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors in Cardiovascular Disease Monitoring)
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