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Sensors for Heart Rate Monitoring and Cardiovascular Disease
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Sensors for Heart Rate Monitoring and Cardiovascular Disease

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1177

Special Issue Editors

Dr. Helmut Karl Lackner

E-Mail Website
Guest Editor
Department of Physiology, Medical University of Graz, 8036 Graz, Austria
Interests: control mechanisms of heart rate dynamics; heart rate variability; short-term blood pressure regulation; signal preprocessing techniques; psychophysiology
Special Issues, Collections and Topics in MDPI journals
Dr. Alexander Müller

E-Mail Website
Guest Editor
Department of Physiology, Medical University of Graz, 8036 Graz, Austria
Interests: exercise physiology; heart rate variability; exercise testing; sports medicine; diabetes; energy metabolism; glucose metabolism; training therapy; humane performance

Special Issue Information

Dear Colleagues,

Heart rate monitoring and the early detection of cardiovascular diseases are pivotal aspects of contemporary healthcare. Advances in sensor technologies have led to the development of precise, portable, and cost-effective systems capable of ensuring continuous heart health monitoring. These sensors, ranging from traditional electrode-based technologies (like ECG) to optical PPG (photoplethysmography) sensors, provide significant insights into heart rate variability (HRV), arrhythmias, and stress levels.

Innovations such as wearable devices, implants, and contactless systems are revolutionizing the diagnosis and management of cardiovascular diseases. Artificial intelligence (AI) and machine learning (ML) significantly enhance data analysis, enabling the faster detection of anomalies and supporting personalized treatments. Emphasis is particularly placed on energy-efficient, wireless, and biocompatible designs, making long-term monitoring more comfortable and accessible.

The integration of heart rate sensors into smartwatches and smartphones fosters preventive health monitoring, while specialized medical devices enable precise diagnostics. However, challenges persist in areas such as data quality, noise suppression, and data privacy.

This Special Issue explores the latest advancements in heart rate monitoring sensor technology and its applications in preventing, diagnosing, and treating cardiovascular diseases. Contributions on innovative technologies, clinical applications, and their societal impact are particularly encouraged.

Dr. Helmut Karl Lackner
Dr. Alexander Müller
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • heart rate
  • cardiovascular disease
  • electrode-based technologies
  • photoplethysmography
  • wearable devices, implants, and contactless systems
  • artificial intelligence
  • machine learning
  • mHealth

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Published Papers (2 papers)

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Research

18 pages, 2776 KiB  
Article
Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach
by Martina Srutova, Vaclav Kremen and Lenka Lhotska
Sensors 2025, 25(7), 2253; https://doi.org/10.3390/s25072253 - 2 Apr 2025
Viewed by 235
Abstract
Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims [...] Read more.
Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims to improve the distinction between LQTS genotypes, particularly LQT3, through a novel electrocardiogram (ECG)-based approach. Patients with LQT3 are at elevated risk due to arrhythmia triggers associated with rest and sleep. Employing a database of genotyped long QT syndrome E-HOL-03-0480-013 ECG signals, we introduced two innovative parameterization techniques—area under the ECG curve and wave transformation into the unit circle—to classify LQT3 against LQT1 and LQT2 genotypes. Our methodology utilized single-lead ECG data with a 200 Hz sampling frequency. The support vector machine (SVM) model demonstrated the ability to discriminate LQT3 with a recall of 90% and a precision of 81%, achieving an F1-score of 0.85. This parameterization offers a potential substitute for genetic testing and is practical for low frequencies. These single-lead ECG data could enhance smartwatches’ functionality and similar cardiovascular monitoring applications. The results underscore the viability of ECG morphology-based genotype classification, promising a significant step towards streamlined diagnosis and improved patient care in LQTS. Full article
(This article belongs to the Special Issue Sensors for Heart Rate Monitoring and Cardiovascular Disease)
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21 pages, 6656 KiB  
Article
A Flexible PVDF Sensor for Forcecardiography
by Salvatore Parlato, Jessica Centracchio, Eliana Cinotti, Gaetano D. Gargiulo, Daniele Esposito, Paolo Bifulco and Emilio Andreozzi
Sensors 2025, 25(5), 1608; https://doi.org/10.3390/s25051608 - 6 Mar 2025
Viewed by 718
Abstract
Forcecardiography (FCG) uses force sensors to record the mechanical vibrations induced on the chest wall by cardiac and respiratory activities. FCG is usually performed via piezoelectric lead-zirconate titanate (PZT) sensors, which simultaneously record the very slow respiratory movements of the chest, the slow [...] Read more.
Forcecardiography (FCG) uses force sensors to record the mechanical vibrations induced on the chest wall by cardiac and respiratory activities. FCG is usually performed via piezoelectric lead-zirconate titanate (PZT) sensors, which simultaneously record the very slow respiratory movements of the chest, the slow infrasonic vibrations due to emptying and filling of heart chambers, the faster infrasonic vibrations due to movements of heart valves, which are usually recorded via Seismocardiography (SCG), and the audible vibrations corresponding to heart sounds, commonly recorded via Phonocardiography (PCG). However, PZT sensors are not flexible and do not adapt very well to the deformations of soft tissues on the chest. This study presents a flexible FCG sensor based on a piezoelectric polyvinylidene fluoride (PVDF) transducer. The PVDF FCG sensor was compared with a well-assessed PZT FCG sensor, as well as with an electro-resistive respiratory band (ERB), an accelerometric SCG sensor, and an electronic stethoscope for PCG. Simultaneous recordings were acquired with these sensors and an electrocardiography (ECG) monitor from a cohort of 35 healthy subjects (16 males and 19 females). The PVDF sensor signals were compared in terms of morphology with those acquired simultaneously via the PZT sensor, the SCG sensor and the electronic stethoscope. Moreover, the estimation accuracies of PVDF and PZT sensors for inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) were assessed against reference ECG and ERB measurements. The results of statistical analyses confirmed that the PVDF sensor provides FCG signals with very high similarity to those acquired via PZT sensors (median cross-correlation index of 0.96 across all subjects) as well as with SCG and PCG signals (median cross-correlation indices of 0.85 and 0.80, respectively). Moreover, the PVDF sensor provides very accurate estimates of IBIs, with R2 > 0.99 and Bland–Altman limits of agreement (LoA) of [−5.30; 5.00] ms, and of IBrIs, with R2 > 0.96 and LoA of [−0.510; 0.513] s. The flexibility of the PVDF sensor makes it more comfortable and ideal for wearable applications. Unlike PZT, PVDF is lead-free, which increases safety and biocompatibility for prolonged skin contact. Full article
(This article belongs to the Special Issue Sensors for Heart Rate Monitoring and Cardiovascular Disease)
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