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

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 14684

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Dr. Carlos Sánchez

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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

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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
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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

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Keywords

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

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Related Special Issue

ECG Signal Processing and Analysis, Computational Technology and Applications in Sensors (12 articles)

Published Papers (8 papers)

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Research

19 pages, 3377 KiB

Open AccessArticle

AI-Enhanced Detection of Heart Murmurs: Advancing Non-Invasive Cardiovascular Diagnostics

by Maria-Alexandra Zolya, Elena-Laura Popa, Cosmin Baltag, Dragoș-Vasile Bratu, Simona Coman and Sorin-Aurel Moraru

Sensors 2025, 25(6), 1682; https://doi.org/10.3390/s25061682 - 8 Mar 2025

Viewed by 771

Abstract

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, claiming over 17 million lives annually. Early detection of conditions like heart murmurs, often indicative of heart valve abnormalities, is critical for improving patient outcomes. Traditional diagnostic methods, including physical auscultation and advanced imaging techniques, are constrained by their reliance on specialized clinical expertise, inherent procedural invasiveness, substantial financial costs, and limited accessibility, particularly in resource-limited healthcare environments. This study presents a novel convolutional recurrent neural network (CRNN) model designed for the non-invasive classification of heart murmurs. The model processes heart sound recordings using advanced pre-processing techniques such as z-score normalization, band-pass filtering, and data augmentation (Gaussian noise, time shift, and pitch shift) to enhance robustness. By combining convolutional and recurrent layers, the CRNN captures spatial and temporal features in audio data, achieving an accuracy of 90.5%, precision of 89%, and recall of 87%. These results underscore the potential of machine-learning technologies to revolutionize cardiac diagnostics by offering scalable, accessible solutions for the early detection of cardiovascular conditions. This approach paves the way for broader applications of AI in healthcare, particularly in underserved regions where traditional resources are scarce. Full article

(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)

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22 pages, 3695 KiB

Open AccessArticle

Dynamic Response of Heart Rate Variability to Active Standing in Aortic Valve Disease: Insights from Recurrence Quantification Analysis

by Itayetzin Beurini Cruz-Vega, Nydia Ávila-Vanzzini, Gertrudis Hortensia González-Gómez, Rashidi Springall, Juan C. Echeverría and Claudia Lerma

Sensors 2025, 25(5), 1535; https://doi.org/10.3390/s25051535 - 1 Mar 2025

Viewed by 935

Abstract

Introduction: Aortic valve disease (AVD) is an inflammatory, lipid infiltration and calcification disease that has been associated with changes in the conventional linear heart rate variability (HRV) indices showing a marked shift towards sympathetic predominance and a deterioration of the autonomic control. Objective: To explore the HRV dynamics in AVD patients through nonlinear methods by recurrence quantification analysis (RQA). Methods: In total, 127 subjects participated in a cross-sectional study categorized into three groups: healthy valve (HV), aortic valve sclerosis (AVSc), and aortic valve stenosis (AVS), as determined by echocardiographic assessment. HRV data were collected from five-minute ECG recordings at both a supine position and active standing. RQA indices were calculated using the Cross Recurrence Plot Toolbox. Results: In the supine position, patients with AVS exhibited larger determinism and trapping time than those with AVSc and HV. The analysis of these differences revealed that determinism and laminarity increased progressively from HV to AVS. In the same way, the magnitude of change (Δ) between positions decreased and presented the lowest values in AVS in most of the nonlinear indices. Conclusion: RQA indices of HRV in AVD patients indicate a rigidizing dynamic characterized by larger determinism and extended trapping times in fewer system states in relation to the severity of AVD. These findings establish a precedent for future perspective assessments for the implementation of these methods in medical software or devices. Full article

(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)

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8 pages, 853 KiB

Open AccessCommunication

Clinical Utility of Synthesized 18-Lead Electrocardiography

by Tetsushi Yamamoto, Hiroyuki Awano, Shuichiro Ogawa and Masafumi Matsuo

Sensors 2024, 24(18), 5947; https://doi.org/10.3390/s24185947 - 13 Sep 2024

Viewed by 1635

Abstract

Eighteen-lead electrocardiography (18-ECG) includes, in addition to those in standard 12-lead ECG (12-ECG), six additional chest leads: V7–V9 and V3RV5R. Leads V7–V9 require the patient to be in a lateral decubitus position for the electrodes to be attached to the back. Synthesized 18-ECG (syn18-ECG) is a method that only records 12-ECG and uses computational logic to record the posterior wall (V7–V9) and right-sided (V3R–V5R) leads. We review the clinical utility of syn18-ECG in conditions including acute coronary syndromes, arrhythmias, acute pulmonary embolism, and Duchenne muscular dystrophy. The syn18-ECG waveform correlates well with the actual 18-ECG waveform, indicating that syn18-ECG is an excellent substitute for 18-ECG, excluding negative T waves. ST elevation in leads V7–V9 has the effect of reducing missed acute coronary syndromes in the posterior wall. In cases of arrhythmia, syn18-ECG can accurately estimate the target site of radiofrequency catheter ablation using a simple algorithm. The use of additional leads in Duchenne muscular dystrophy is expected to provide new insights. To facilitate gaining more knowledge regarding diseases that have not yet been investigated, it is imperative that the cost of syn18-ECG is reduced in the future. Full article

(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)

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30 pages, 7122 KiB

Open AccessArticle

Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder–Decoders with Residual and Recurrent Connections

by Vessela Krasteva, Todor Stoyanov, Ramun Schmid and Irena Jekova

Sensors 2024, 24(14), 4645; https://doi.org/10.3390/s24144645 - 17 Jul 2024

Cited by 2 | Viewed by 1820

Abstract

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder–decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder–decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm’s measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (−2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (−2.4 ± 5.4 ms), and QT-interval (−0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance. 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, 3379 KiB

Open AccessArticle

Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates

by Nadia Muhammad Hussain, Bilal Amin, Barry James McDermott, Eoghan Dunne, Martin O’Halloran and Adnan Elahi

Sensors 2024, 24(11), 3357; https://doi.org/10.3390/s24113357 - 24 May 2024

Cited by 2 | Viewed by 1410

Abstract

Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse oximeters can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative noninvasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate the prominent ECG features based on pH estimation that could potentially be used to explore the noninvasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset used contained 274 segments of ECG and pH values recorded simultaneously. After preprocessing the data, principal component analysis and the Pan–Tompkins algorithm were used for each segment to determine the most significant ECG cycle and to compute the ECG features. Descriptive statistics were performed to describe the main properties of the processed dataset. A Kruskal–Wallis nonparametric test was then used to analyze differences between the asphyxiated and non-asphyxiated groups. Finally, a Dunn–Šidák post hoc test was used for individual comparison among the mean ranks of all groups. The findings of this study showed that ECG features (T/QRS, T Amplitude, Tslope, Tslope/T, Tslope/|T|, HR, QT, and QTc) based on pH estimation differed significantly (p < 0.05) in asphyxiated neonates. All these key ECG features were also found to be significantly different between the two groups. Full article

(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)

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10 pages, 1907 KiB

Open AccessCommunication

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

Cited by 2 | Viewed by 3591

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 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

Δ t

(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 =

\frac{60}{Δ t}

) 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

Open AccessArticle

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

Cited by 2 | Viewed by 1354

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, 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

Open AccessArticle

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

Cited by 3 | Viewed by 1853

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 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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