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ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2522

Special Issue Editors

Dr. Carlos Sánchez

E-Mail Website
Guest Editor
BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
Interests: multiscale computational modelling of the human heart and its applications; heart rate variability in hyperbaric environments
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jan Cornelis

E-Mail Website
Guest Editor
Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium
Interests: data analysis; image and video processing; medical imaging; remote sensing; biomedical engineering; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, there have been vast improvements in the biomedical signal processing field thanks to great advances in many technological fields such as electronics, communications, engineering, computational modelling, and machine learning. In particular, techniques used to analyse electrocardiographic signals (ECGs) have notably improved, and several of them have already been incorporated into ECG recording devices, facilitating their use among clinicians. This, together with computationally highly demanding cardiac activity simulations, has entailed significant advances in the personalization and adaption of therapies and treatments applied to a wide variety of patients.

The main topics for reviews and original research papers involved in this Special Issue focus on sensors and their application, including new methodologies, techniques, solutions, and potential applications in the field of cardiac signal processing and simulation. Some potential topics are as follows:

  • ECG recordings with wearables: offline and real-time embedded signal processing techniques;
  • In silico ECG simulations: from the ionic level up to the 3D thoracic volume;
  • ECG ambulatory monitoring in diseased patients;
  • ECG processing in extreme conditions (such as hyperbaric environments or abnormal temperature and humidity contexts);
  • ECG classification and risk stratification (classical and modern classifiers, machine learning, etc.);
  • The impact of the autonomic nervous system on cardiac activity with regards to monitoring and pathophysiological conditions.

Dr. Carlos Sánchez
Prof. Dr. Jan Cornelis
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

  • ECG sensors
  • ECG processing
  • wearable devices
  • cardiac signal processing
  • heart rate monitoring
  • arrhythmia detection
  • biomedical engineering

Related Special Issue

Published Papers (3 papers)

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Research

10 pages, 1907 KiB  
Communication
Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device
by Julie Payette, Fabrice Vaussenat and Sylvain G. Cloutier
Sensors 2024, 24(7), 2238; https://doi.org/10.3390/s24072238 - 31 Mar 2024
Viewed by 709
Abstract
Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and [...] Read more.
Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO’s DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10−3 comparing the DigiPen’s computed Δt (time between pulses) with the reference ECG data. The peaks’ timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals. Full article
(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)
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17 pages, 538 KiB  
Article
Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component
by María Dolores Peláez-Coca, Alberto Hernando, María Teresa Lozano, Juan Bolea, David Izquierdo and Carlos Sánchez
Sensors 2024, 24(2), 447; https://doi.org/10.3390/s24020447 - 11 Jan 2024
Viewed by 579
Abstract
This study’s primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve this, [...] Read more.
This study’s primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve this, 28 volunteers were placed in a dry hyperbaric chamber, where they experienced varying pressures from 1 to 5 atmospheres, with five sequential stops lasting five minutes each at different atmospheric pressures. The HRV was dissected into two components: the respiratory component, which is linked to respiration; and the residual component, which is influenced by factors beyond respiration. Nine parameters were assessed, including the respiratory rate, four classic HRV temporal parameters, and four frequency parameters. A k-nearest neighbors classifier based on cosine distance successfully identified the atmospheric pressures to which the subjects were exposed to. The classifier achieved an 88.5% accuracy rate in distinguishing between the 5 atm and 3 atm stages using only four features: respiratory rate, heart rate, and two frequency parameters associated with the subjects’ sympathetic responses. Furthermore, the study identified 6 out of 28 subjects as having atypical responses across all pressure levels when compared to the majority. Interestingly, two of these subjects stood out in terms of gender and having less prior diving experience, but they still exhibited normal responses to immersion. This suggests the potential for establishing distinct safety protocols for divers based on their previous experience and gender. Full article
(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)
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16 pages, 6308 KiB  
Article
Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach
by Takeru Shiraga, Hisaki Makimoto, Benita Kohlmann, Christofori-Eleni Magnisali, Yoshie Imai, Yusuke Itani, Asuka Makimoto, Fabian Schölzel, Alexandru Bejinariu, Malte Kelm and Obaida Rana
Sensors 2023, 23(24), 9834; https://doi.org/10.3390/s23249834 - 14 Dec 2023
Viewed by 907
Abstract
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at [...] Read more.
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications. Full article
(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)
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