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Mobile and Wearable Technologies for Cardiovascular Monitoring and Disease Management
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Mobile and Wearable Technologies for Cardiovascular Monitoring and Disease Management

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3032

Special Issue Editor

Prof. Dr. Arash Arya

E-Mail Website
Guest Editor
Department of Electrophysiology, University Hospital Halle (Saale), Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
Interests: smartwatch; apple watch; electrocardiogram; feasibility; reliability; wearables; mobile health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aim and scope: Recent advances in artificial intelligence, combined with mobile and wearable technologies, are transforming cardiovascular medicine by enabling continuous, real-time monitoring and early detection of arrhythmias, heart failure decompensation, and other life-threatening cardiac events. These innovations offer the potential to improve patient outcomes, support remote management, and enable personalized treatment—particularly in preventing sudden cardiac death, the leading cause of mortality in industrialized nations.

This Special Issue aims to highlight the latest research and developments in mobile and wearable solutions for cardiovascular monitoring and disease management. We invite original research articles, reviews, and case reports addressing sensor design, biosignal acquisition, AI-driven diagnostics, clinical validation, and integration into telemedicine and remote care platforms.

Topics of interest include, but are not limited to, the following: smartwatch-, patch-, implantable-, or smartphone-based monitoring; detection and prediction of atrial and ventricular arrhythmias; applications of ECG, photoplethysmography (PPG), and novel biosignals; machine learning algorithms in cardiac diagnostics; and data privacy, security, and regulatory challenges in digital health.

This Special Issue encourages interdisciplinary contributions from clinicians, biomedical engineers, data scientists, and healthcare innovators, with a focus on translating technological advancements into meaningful clinical applications.

Prof. Dr. Arash Arya
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

  • artificial intelligence
  • wearable cardiac monitors
  • implantable cardiac monitors
  • mobile health (mHealth)
  • biosensors
  • arrhythmia detection
  • electrocardiography
  • photoplethysmography
  • remote patient monitoring
  • telemedicine

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Published Papers (2 papers)

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Research

20 pages, 2070 KB  
Article
Automated Detection of Normal, Atrial, and Ventricular Premature Beats from Single-Lead ECG Using Convolutional Neural Networks
by Dimitri Kraft and Peter Rumm
Sensors 2026, 26(2), 513; https://doi.org/10.3390/s26020513 - 12 Jan 2026
Viewed by 1215
Abstract
Accurate detection of premature atrial contractions (PACs) and premature ventricular contractions (PVCs) in single-lead electrocardiograms (ECGs) is crucial for early identification of patients at risk for atrial fibrillation, cardiomyopathy, and other adverse outcomes. In this work, we present a fully convolutional one-dimensional U-Net [...] Read more.
Accurate detection of premature atrial contractions (PACs) and premature ventricular contractions (PVCs) in single-lead electrocardiograms (ECGs) is crucial for early identification of patients at risk for atrial fibrillation, cardiomyopathy, and other adverse outcomes. In this work, we present a fully convolutional one-dimensional U-Net that reframes beat classification as a segmentation task and directly detects normal beats, PACs, and PVCs from raw ECG signals. The architecture employs a ConvNeXt V2 encoder with simple decoder blocks and does not rely on explicit R-peak detection, handcrafted features, or fixed-length input windows. The model is trained on the Icentia11k database and an in-house single-lead ECG dataset that emphasizes challenging, noisy recordings, and is validated on the CPSC2020 database. Generalization is assessed across several benchmark and clinical datasets, including MIT-BIH Arrhythmia (ADB), MIT 11, AHA, NST, SVDB, CST STRIPS, and CPSC2020. The proposed method achieves near-perfect QRS detection (sensitivity and precision up to 0.999) and competitive PVC performance, with sensitivity ranging from 0.820 (AHA) to 0.986 (MIT 11) and precision up to 0.993 (MIT 11). PAC detection is more variable, with sensitivities between 0.539 and 0.797 and precisions between 0.751 and 0.910, yet the resulting F1-score of 0.72 on SVDB exceeds that of previously published approaches. Model interpretability is addressed using Layer-wise Gradient-weighted Class Activation Mapping (LayerGradCAM), which confirms physiologically plausible attention to QRS complexes for PVCs and to P-waves for PACs. Overall, the proposed framework provides a robust, interpretable, and hardware-efficient solution for joint PAC and PVC detection in noisy, single-lead ECG recordings, suitable for integration into Holter and wearable monitoring systems. Full article
(This article belongs to the Special Issue Mobile and Wearable Technologies for Cardiovascular Monitoring and Disease Management)
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24 pages, 5717 KB  
Article
A Paralleled Multi-Task Learning-Based Framework for Single-Lead ECG Fine-Grained Noise Localization, Denoising and Signal Quality Assessment
by Yating Hu, Qing Liu, Zheng Zhou, Weize Xu and Hong Tang
Sensors 2025, 25(23), 7152; https://doi.org/10.3390/s25237152 - 23 Nov 2025
Viewed by 1246
Abstract
Wearable ECG monitoring devices have become indispensable in personalized healthcare. However, dynamic signal acquisition during daily activities often introduces transient noise, which complicates signal classification and denoising, and may compromise diagnostic reliability. To address this challenge, this study proposes an ECG preprocessing framework [...] Read more.
Wearable ECG monitoring devices have become indispensable in personalized healthcare. However, dynamic signal acquisition during daily activities often introduces transient noise, which complicates signal classification and denoising, and may compromise diagnostic reliability. To address this challenge, this study proposes an ECG preprocessing framework based on multi-task learning, in which a fine-grained noise localization task is introduced to guide and assist both ECG signal quality assessment and denoising. Built upon a Transformer backbone and optimized with three task-specific loss functions, the proposed model leveraged weak supervision and pathological ECG data to learn robust noise-invariant representations. This design incorporates intra-class awareness, enabling the model to overcome various noise within the same quality category and to perform adaptive denoising beyond conventional inter-class-based approaches. Extensive experiments demonstrated state-of-the-art performance in both denoising and quality assessment, with weighted average F1-scores ranging from 95.72% to 98.49% and classification accuracy exceeding 95.68%. Moreover, under extremely severe noise conditions, the signal-to-noise ratio (SNR) is improved from −1.95 ± 3.83 dB to 12.20 ± 2.51 dB while preserving waveform fidelity. After pruning and quantization, the model could be effectively compressed, thereby enhancing its suitability for real-time deployment in edge computing scenarios. Overall, the proposed method not only preserved diagnostically important ECG waveforms and provided interpretable noise localization but also offers an efficient and clinically relevant solution for large-scale, real-time ECG monitoring. Full article
(This article belongs to the Special Issue Mobile and Wearable Technologies for Cardiovascular Monitoring and Disease Management)
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