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ECG Signal Processing Techniques and Applications
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ECG Signal Processing Techniques and Applications

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

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 46937

Special Issue Editors

Dr. Riccardo Pernice

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: portable/wearable monitoring systems; electrocardiographic (ECG) and photoplethysmographic (PPG) acquisition sensors and systems; biomedical signal processing; autonomic nervous system; heart rate variability (HRV) analysis; brain–heart interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo W. Rivolta

E-Mail Website
Guest Editor
Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
Interests: signal processing; feature extraction; computational intelligence and computerized simulations for ECG signal processing and characterization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Raquel Bailón

E-Mail Website
Guest Editor
BSICOS, Aragón Institute for Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50015 Zaragoza, Spain
Interests: biomedical signal processing; non-invasive autonomic nervous system assessment; wearable systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are very pleased to invite you to contribute to this Special Issue focused on Electrocardiographic (ECG) Signal Processing Techniques and Applications.

Electrocardiographic signals are recordings of the electrical activity of the heart and have been widely employed for detecting or monitoring abnormal heart function (e.g., arrhythmias or conduction disturbances) and for heart rate variability assessment. Thanks to continuous progress, more compact and portable ECG devices are nowadays becoming widely popular for applications ranging from early diagnosis of cardiovascular diseases at home to biometrics and user authenticantion in the business sector. However, these new applicative scenarios require the design of innovative ECG acquisition systems and processing tools.

This Special Issue aims to collect original articles, opinions and review papers in the field of ECG signal processing and characterization. Multidisciplinary papers are welcomed, ranging from novel sensors and devices for ECG acquisition, advanced signal processing techniques for features extraction, mathematical modelling of ECG signals, innovative deep learning and machine learning algorithms for ECG classification.

Potential topics include, but are not limited, to the following:

- novel ECG devices;

- advanced ECG signal processing and classification techniques;

- ECG biometrics for user authentication;

- ECG segmentation and pattern recognition techniques;

- advanced noise suppression algorithms;

- mathematical modelling of ECG signals;

- heart rate variability analyses;

- deep learning and machine learning algorithms;

- real-time monitoring or early diagnosis through multisensory portable or wearable devices;

- clinical applications.

Dr. Riccardo Pernice
Dr. Massimo W. Rivolta
Prof. Dr. Raquel Bailón
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.

Published Papers (16 papers)

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14 pages, 2614 KiB  
Article
Comparison of ECG Saline-Conduction Technique and ECG Wire-Based Technique for Peripherally Inserted Central Catheter Insertion: A Randomized Controlled Trial
by Giuseppe Gullo, Pierre Frossard, Anaïs Colin and Salah Dine Qanadli
Sensors 2024, 24(3), 894; https://0-doi-org.brum.beds.ac.uk/10.3390/s24030894 - 30 Jan 2024
Viewed by 1096
Abstract
(1) Background: The peripherally inserted central catheter (PICC) is commonly used in medicine. The tip position was shown to be a major determinant in PICC function and related complications. Recent advances in ECG guidance might facilitate daily practice. This study aimed to compare [...] Read more.
(1) Background: The peripherally inserted central catheter (PICC) is commonly used in medicine. The tip position was shown to be a major determinant in PICC function and related complications. Recent advances in ECG guidance might facilitate daily practice. This study aimed to compare two ECG techniques, in terms of their tip-position accuracy, puncture site layout, and signal quality; (2) Methods: This randomized open study (1:1) included 320 participants. One PICC guidance technique used ECG signal transmission with saline (ST); the other technique used a guidewire (WT). Techniques were compared by the distance between the catheter tip and the cavoatrial junction (DCAJ) on chest X-rays, insertion-point hemostasis time, and the extracorporeal catheter length between the hub and the insertion point; (3) Results: The mean DCAJs were significantly different between ST (1.36 cm, 95% CI: 1.22–1.37) and WT (1.12 cm, 95% CI: 0.98–1.25; p = 0.013) groups. When DCAJs were classified as optimal, suboptimal, or inadequate, the difference between techniques had limited clinical impact (p = 0.085). However, the hemostasis time at the puncture site was significantly better with WT (no delay in 82% of patients) compared to ST (no delay in 50% of patients; p < 0.001). Conversely, ST achieved optimal and suboptimal extracorporeal lengths significantly more frequently than WT (100% vs. 66%; p < 0.001); (4) Conclusions: ECG guidance technologies achieved significantly different tip placements, but the difference had minimal clinical impact. Nevertheless, each technique displayed an important drawback at the PICC insertion point: the extracorporeal catheter was significantly longer with WT and the hemostasis delay was significantly longer with ST. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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11 pages, 732 KiB  
Article
Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis
by Spyridon Kontaxis, Estela Laporta, Esther Garcia, Matteo Martinis, Letizia Leocani, Lucia Roselli, Mathias Due Buron, Ana Isabel Guerrero, Ana Zabala, Nicholas Cummins, Srinivasan Vairavan, Matthew Hotopf, Richard J. B. Dobson, Vaibhav A. Narayan, Maria Libera La Porta, Gloria Dalla Costa, Melinda Magyari, Per Soelberg Sørensen, Carlos Nos, Raquel Bailon, Giancarlo Comi and on behalf of the RADAR-CNS Consortiumadd Show full author list remove Hide full author list
Sensors 2023, 23(13), 6017; https://0-doi-org.brum.beds.ac.uk/10.3390/s23136017 - 29 Jun 2023
Cited by 1 | Viewed by 1160
Abstract
The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from [...] Read more.
The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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20 pages, 2698 KiB  
Article
Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation
by Markus Lueken, Michael Gramlich, Steffen Leonhardt, Nikolaus Marx and Matthias D. Zink
Sensors 2023, 23(12), 5618; https://0-doi-org.brum.beds.ac.uk/10.3390/s23125618 - 15 Jun 2023
Cited by 7 | Viewed by 1425
Abstract
Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an [...] Read more.
Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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22 pages, 378 KiB  
Article
A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
by Md Moklesur Rahman, Massimo Walter Rivolta, Fabio Badilini and Roberto Sassi
Sensors 2023, 23(11), 5237; https://0-doi-org.brum.beds.ac.uk/10.3390/s23115237 - 31 May 2023
Cited by 5 | Viewed by 3421
Abstract
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been [...] Read more.
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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15 pages, 617 KiB  
Article
Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging
by Ahmad Suliman, Md Rakibul Mowla, Alaleh Alivar, Charles Carlson, Punit Prakash, Balasubramaniam Natarajan, Steve Warren and David E. Thompson
Sensors 2023, 23(5), 2693; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052693 - 1 Mar 2023
Cited by 1 | Viewed by 1708
Abstract
Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals [...] Read more.
Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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17 pages, 9352 KiB  
Article
Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients
by Israel Campero Jurado, Ilde Lorato, John Morales, Lonneke Fruytier, Shavini Stuart, Pradeep Panditha, Daan M. Janssen, Nicolò Rossetti, Natallia Uzunbajakava, Irina Bianca Serban, Lars Rikken, Margreet de Kok, Joaquin Vanschoren and Aarnout Brombacher
Sensors 2023, 23(4), 2130; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042130 - 14 Feb 2023
Cited by 5 | Viewed by 3117
Abstract
Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing [...] Read more.
Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing socio-economic burden on health institutions and government. Therefore, developing technologies and tools to diagnose CVD in a timely way and detect AF is an important research topic. ECG monitoring patches allowing ambulatory patient monitoring over several days represent a novel technology, while we witness a significant proliferation of ECG monitoring patches on the market and in the research labs, their performance over a long period of time is not fully characterized. This paper analyzes the signal quality of ECG signals obtained using a single-lead ECG patch featuring self-adhesive dry electrode technology collected from six cardiac patients for 5 days. In particular, we provide insights into signal quality degradation over time, while changes in the average ECG quality per day were present, these changes were not statistically significant. It was observed that the quality was higher during the nights, confirming the link with motion artifacts. These results can improve CVD diagnosis and AF detection in real-world scenarios. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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9 pages, 532 KiB  
Article
Algorithm for Mobile Platform-Based Real-Time QRS Detection
by Luca Neri, Matt T. Oberdier, Antonio Augello, Masahito Suzuki, Ethan Tumarkin, Sujai Jaipalli, Gian Angelo Geminiani, Henry R. Halperin and Claudio Borghi
Sensors 2023, 23(3), 1625; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031625 - 2 Feb 2023
Cited by 4 | Viewed by 2200
Abstract
Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but [...] Read more.
Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan–Tompkins (AMPT), which is a simplified version of the well-established Pan–Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan–Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5–20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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14 pages, 1548 KiB  
Article
Modeling the Design Characteristics of Woven Textile Electrodes for long−Term ECG Monitoring
by Peter J. Brehm and Allison P. Anderson
Sensors 2023, 23(2), 598; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020598 - 4 Jan 2023
Cited by 2 | Viewed by 1354
Abstract
An electrocardiograph records the periodic voltage generated by the heart over time. There is growing demand to continuously monitor the ECG for proactive health care and human performance optimization. To meet this demand, new conductive textile electrodes are being developed which offer an [...] Read more.
An electrocardiograph records the periodic voltage generated by the heart over time. There is growing demand to continuously monitor the ECG for proactive health care and human performance optimization. To meet this demand, new conductive textile electrodes are being developed which offer an attractive alternative to adhesive gel electrodes but they come with their own challenges. The key challenge with textile electrodes is that the relationship between the manufacturing parameters and the ECG measurement is not well understood, making design an iterative process without the ability to prospectively develop woven electrodes with optimized performance. Here we address this challenge by applying the traditional skin−electrode interface circuit model to woven electrodes by constructing a parameterized model of the ECG system. Then the unknown parameters of the system are solved for with an iterative MATLAB optimizer using measured data captured with the woven electrodes. The results of this novel analysis confirm that yarn conductivity and total conductive area reduce skin electrode impedance. The results also indicate that electrode skin pressure and moisture require further investigation. By closing this gap in development, textile electrodes can be better designed and manufactured to meet the demands of long−term ECG capture. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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18 pages, 1812 KiB  
Article
An Effective Method of Detecting Characteristic Points of Impedance Cardiogram Verified in the Clinical Pilot Study
by Ilona Karpiel, Monika Richter-Laskowska, Daniel Feige, Adam Gacek and Aleksander Sobotnicki
Sensors 2022, 22(24), 9872; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249872 - 15 Dec 2022
Cited by 5 | Viewed by 1800
Abstract
Accurate and reliable determination of the characteristic points of the impedance cardiogram (ICG) is an important research problem with a growing range of applications in the cardiological diagnostics of patients with heart failure (HF). The shapes of the characteristic waves of the ICG [...] Read more.
Accurate and reliable determination of the characteristic points of the impedance cardiogram (ICG) is an important research problem with a growing range of applications in the cardiological diagnostics of patients with heart failure (HF). The shapes of the characteristic waves of the ICG signal and the temporal location of the characteristic points B, C, and X provide significant diagnostic information. On this basis, essential diagnostic cardiological parameters can be determined, such as, e.g., cardiac output (CO) or stroke volume (SV). Although the importance of this problem is obvious, we face many challenges, including noisy signals and the big variability in the morphology, which altogether make the accurate identification of the characteristic points quite difficult. The paper presents an effective method of ICG points identification intended for conducting experimental research in the field of impedance cardiography. Its effectiveness is confirmed in clinical pilot studies. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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16 pages, 1915 KiB  
Article
Non-Standard Electrode Placement Strategies for ECG Signal Acquisition
by Margus Metshein, Andrei Krivošei, Anar Abdullayev, Paul Annus and Olev Märtens
Sensors 2022, 22(23), 9351; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239351 - 1 Dec 2022
Cited by 1 | Viewed by 2029
Abstract
Background: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, [...] Read more.
Background: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. Methods: In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. Results: Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. Conclusions: The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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20 pages, 3276 KiB  
Article
Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures
by Gabriele Volpes, Chiara Barà, Alessandro Busacca, Salvatore Stivala, Michal Javorka, Luca Faes and Riccardo Pernice
Sensors 2022, 22(23), 9149; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239149 - 25 Nov 2022
Cited by 8 | Viewed by 1882
Abstract
Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on [...] Read more.
Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time–domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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13 pages, 596 KiB  
Article
Novel Generalized Low-Pass Filter with Adjustable Parameters of Exponential-Type Forgetting and Its Application to ECG Signal
by Ivo Petráš
Sensors 2022, 22(22), 8740; https://doi.org/10.3390/s22228740 - 12 Nov 2022
Cited by 4 | Viewed by 1967
Abstract
In this paper, a novel form of the Gaussian filter, the Mittag–Leffler filter is presented. This new filter uses the Mittag–Leffler function in the probability-density function. Such Mittag–Leffler distribution is used in the convolution kernel of the filter. The filter has three parameters [...] Read more.
In this paper, a novel form of the Gaussian filter, the Mittag–Leffler filter is presented. This new filter uses the Mittag–Leffler function in the probability-density function. Such Mittag–Leffler distribution is used in the convolution kernel of the filter. The filter has three parameters that may adjust the curve shape due to the filter-forgetting factor. Illustrative examples present the main advantages of the proposed filter compared to classical Gaussian filtering techniques, as well as real ECG-signal denoising. Some implementation notes, along with the Matlab function, are also presented. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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13 pages, 4231 KiB  
Article
Validity of the Polar H10 Sensor for Heart Rate Variability Analysis during Resting State and Incremental Exercise in Recreational Men and Women
by Marcelle Schaffarczyk, Bruce Rogers, Rüdiger Reer and Thomas Gronwald
Sensors 2022, 22(17), 6536; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176536 - 30 Aug 2022
Cited by 36 | Viewed by 8967
Abstract
Heart rate variability (HRV) is frequently applied in sport-specific settings. The rising use of freely accessible applications for its recording requires validation processes to ensure accurate data. It is the aim of this study to compare the HRV data obtained by the Polar [...] Read more.
Heart rate variability (HRV) is frequently applied in sport-specific settings. The rising use of freely accessible applications for its recording requires validation processes to ensure accurate data. It is the aim of this study to compare the HRV data obtained by the Polar H10 sensor chest strap device and an electrocardiogram (ECG) with the focus on RR intervals and short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA a1) as non-linear metric of HRV analysis. A group of 25 participants performed an exhaustive cycling ramp with measurements of HRV with both recording systems. Average time between heartbeats (RR), heart rate (HR) and DFA a1 were recorded before (PRE), during, and after (POST) the exercise test. High correlations were found for the resting conditions (PRE: r = 0.95, rc = 0.95, ICC3,1 = 0.95, POST: r = 0.86, rc = 0.84, ICC3,1 = 0.85) and for the incremental exercise (r > 0.93, rc > 0.93, ICC3,1 > 0.93). While PRE and POST comparisons revealed no differences, significant bias could be found during the exercise test for all variables (p < 0.001). For RR and HR, bias and limits of agreement (LoA) in the Bland–Altman analysis were minimal (RR: bias of 0.7 to 0.4 ms with LoA of 4.3 to −2.8 ms during low intensity and 1.3 to −0.5 ms during high intensity, HR: bias of −0.1 to −0.2 ms with LoA of 0.3 to −0.5 ms during low intensity and 0.4 to −0.7 ms during high intensity). DFA a1 showed wider bias and LoAs (bias of 0.9 to 8.6% with LoA of 11.6 to −9.9% during low intensity and 58.1 to −40.9% during high intensity). Linear HRV measurements derived from the Polar H10 chest strap device show strong agreement and small bias compared with ECG recordings and can be recommended for practitioners. However, with respect to DFA a1, values in the uncorrelated range and during higher exercise intensities tend to elicit higher bias and wider LoA. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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19 pages, 2464 KiB  
Article
Monitoring of Serum Potassium and Calcium Levels in End-Stage Renal Disease Patients by ECG Depolarization Morphology Analysis
by Hassaan A. Bukhari, Carlos Sánchez, José Esteban Ruiz, Mark Potse, Pablo Laguna and Esther Pueyo
Sensors 2022, 22(8), 2951; https://0-doi-org.brum.beds.ac.uk/10.3390/s22082951 - 12 Apr 2022
Cited by 2 | Viewed by 2033
Abstract
Objective: Non-invasive estimation of serum potassium, [K+], and calcium, [Ca2+], can help to prevent life-threatening ventricular arrhythmias in patients with advanced renal disease, but current methods for estimation of electrolyte levels have limitations. We [...] Read more.
Objective: Non-invasive estimation of serum potassium, [K+], and calcium, [Ca2+], can help to prevent life-threatening ventricular arrhythmias in patients with advanced renal disease, but current methods for estimation of electrolyte levels have limitations. We aimed to develop new markers based on the morphology of the QRS complex of the electrocardiogram (ECG). Methods: ECG recordings from 29 patients undergoing hemodialysis (HD) were processed. Mean warped QRS complexes were computed in two-minute windows at the start of an HD session, at the end of each HD hour and 48 h after it. We quantified QRS width, amplitude and the proposed QRS morphology-based markers that were computed by warping techniques. Reference [K+] and [Ca2+] were determined from blood samples acquired at the time points where the markers were estimated. Linear regression models were used to estimate electrolyte levels from the QRS markers individually and in combination with T wave morphology markers. Leave-one-out cross-validation was used to assess the performance of the estimators. Results: All markers, except for QRS width, strongly correlated with [K+] (median Pearson correlation coefficients, r, ranging from 0.81 to 0.87) and with [Ca2+] (r ranging from 0.61 to 0.76). QRS morphology markers showed very low sensitivity to heart rate (HR). Actual and estimated serum electrolyte levels differed, on average, by less than 0.035 mM (relative error of 0.018) for [K+] and 0.010 mM (relative error of 0.004) for [Ca2+] when patient-specific multivariable estimators combining QRS and T wave markers were used. Conclusion: QRS morphological markers allow non-invasive estimation of [K+] and [Ca2+] with low sensitivity to HR. The estimation performance is improved when multivariable models, including T wave markers, are considered. Significance: Markers based on the QRS complex of the ECG could contribute to non-invasive monitoring of serum electrolyte levels and arrhythmia risk prediction in patients with renal disease. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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Review

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29 pages, 2565 KiB  
Review
Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review
by Luca Neri, Matt T. Oberdier, Kirsten C. J. van Abeelen, Luca Menghini, Ethan Tumarkin, Hemantkumar Tripathi, Sujai Jaipalli, Alessandro Orro, Nazareno Paolocci, Ilaria Gallelli, Massimo Dall’Olio, Amir Beker, Richard T. Carrick, Claudio Borghi and Henry R. Halperin
Sensors 2023, 23(10), 4805; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104805 - 16 May 2023
Cited by 10 | Viewed by 4292
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices [...] Read more.
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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24 pages, 4151 KiB  
Review
Rate-Responsive Cardiac Pacing: Technological Solutions and Their Applications
by Ewa Świerżyńska, Artur Oręziak, Renata Główczyńska, Antonio Rossillo, Marcin Grabowski, Łukasz Szumowski, Francesco Caprioglio and Maciej Sterliński
Sensors 2023, 23(3), 1427; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031427 - 27 Jan 2023
Cited by 4 | Viewed by 5417
Abstract
Modern cardiac pacemakers are equipped with a function that allows the heart rate to adapt to the current needs of the patient in situations of increased demand related to exercise and stress ("rate-response" function). This function may be based on a variety of [...] Read more.
Modern cardiac pacemakers are equipped with a function that allows the heart rate to adapt to the current needs of the patient in situations of increased demand related to exercise and stress ("rate-response" function). This function may be based on a variety of mechanisms, such as a built-in accelerometer responding to increased chest movement or algorithms sensing metabolic demand for oxygen, analysis of intrathoracic impedance, and analysis of the heart rhythm (Q-T interval). The latest technologies in the field of rate-response functionality relate to the use of an accelerometer in leadless endocavitary pacemakers; in these devices, the accelerometer enables mapping of the mechanical wave of the heart’s work cycle, enabling the pacemaker to correctly sense native impulses and stimulate the ventricles in synchrony with the cycles of atria and heart valves. Another modern system for synchronizing pacing rate with the patient’s real-time needs requires a closed-loop system that continuously monitors changes in the dynamics of heart contractions. This article discusses the technical details of various solutions for detecting and responding to situations related to increased oxygen demand (e.g., exercise or stress) in implantable pacemakers, and reviews the results of clinical trials regarding the use of these algorithms. Full article
(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)
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