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Advances in ECG/EEG Monitoring
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Advances in ECG/EEG Monitoring

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 8229

Special Issue Editor

Prof. Dr. Piotr Augustyniak

E-Mail Website
Guest Editor
Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, Al. Adama Mickiewicza 30, 30-059 Kraków, Poland
Interests: ECG processing; medical instrumentation and algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Both principal electrophysiological techniques have recently reemerged as hot areas of scientific research due to new fundamental knowledge in physiology and modeling, new paradigms for signal processing and interpretation, and new areas of application. Every day, new scientific articles announce innovative approaches to detecting as well as processing biosignals and demonstrate their usefulness in various fields of medicine and beyond.

This Special Issue aims to collate the results and integrate the knowledge of research groups around the world engaged in recording methods and physical bases of biosignal transmission in living tissue, as well as signal processing/artificial intelligence specialists and inventors who are exploring new application areas such as driver assistance, lie detection, stress assessment, and much more.

Original research papers and reviews describing advances in ECG- or EEG-related sensors or sensor networks, paradigms, algorithms, methods, models, and approaches are highly welcome.

Prof. Dr. Piotr Augustyniak
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 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

  • electrocardiography
  • wearable sensors
  • machine learning
  • modeling of the cardiac electrical field
  • contactless recording
  • electroencephalography
  • anesthesia monitoring
  • emotion monitoring
  • drowsiness detection
  • epilepsy

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Published Papers (6 papers)

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Research

23 pages, 1705 KiB  
Article
Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals
by Rosanna Ferrara, Martino Giaquinto, Gennaro Percannella, Leonardo Rundo and Alessia Saggese
Sensors 2025, 25(9), 2715; https://doi.org/10.3390/s25092715 - 25 Apr 2025
Viewed by 432
Abstract
Electroencephalography is a widely used non-invasive method for monitoring brain electrical activity, critical for diagnosing and managing neurological disorders such as epilepsy. While clinical standards use 21 electrodes to capture comprehensive neural signals, a personalized approach can enhance performance by selecting patient-specific channels, [...] Read more.
Electroencephalography is a widely used non-invasive method for monitoring brain electrical activity, critical for diagnosing and managing neurological disorders such as epilepsy. While clinical standards use 21 electrodes to capture comprehensive neural signals, a personalized approach can enhance performance by selecting patient-specific channels, reducing noise and redundancy. This study introduces an innovative, lightweight deep learning system optimized for real-time seizure detection in personalized wearable devices. The system uses an efficient Convolutional Neural Network that processes data from just two channels. These channels are automatically selected using a data-driven mechanism that identifies the most informative scalp regions based on each patient’s unique seizure patterns. The proposed approach ensures high reliability, even with small datasets, and improves interpretability for clinicians by overcoming the limitations of more complex methods. The tailored channel selection boosts detection accuracy and ensures robust performance across different seizure types while reducing the computational burden typical of multi-electrode systems. Validation on the publicly available CHB-MIT dataset achieved an average balanced accuracy of 0.83 and a false-positive rate of approximately 0.1/h. The system’s performance matches, and in some cases outperforms, state-of-the-art systems that use four fixed channels in temporal regions, demonstrating the potential of two-channel wearable solutions, specifically with a non-negligible 30% reduction in the false-positive rate. This interpretable, patient-specific method enables the development of personalized, efficient, and compact wearable devices for reliable seizure detection in everyday life. Full article
(This article belongs to the Special Issue Advances in ECG/EEG Monitoring)
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18 pages, 3238 KiB  
Article
Reversible Watermarking for Electrocardiogram Protection
by Pavel Andreev, Anna Denisova and Victor Fedoseev
Sensors 2025, 25(7), 2185; https://doi.org/10.3390/s25072185 - 30 Mar 2025
Viewed by 288
Abstract
The electrocardiogram (ECG) is one of the widespread diagnostic methods used in telemedicine. However, in the telemedicine systems, the data transfer process to the end user may suffer from security risks. Reversible watermarking can preserve the security of electrocardiograms and keep their original [...] Read more.
The electrocardiogram (ECG) is one of the widespread diagnostic methods used in telemedicine. However, in the telemedicine systems, the data transfer process to the end user may suffer from security risks. Reversible watermarking can preserve the security of electrocardiograms and keep their original precision for correct diagnostics. In this paper, we present an extensive investigation of four reversible watermarking methods: prediction error expansion (PEE), reversible contrast mapping difference expansion (RCM), integer transform-based difference expansion (ITB), and compression-based watermarking. We discover new facets of the existing ECG watermarking methods (PEE and compression-based watermarking) and adapt image watermarking methods (RCM and ITB) to ECG signal. We compare different kinds of prediction and compression methods used in the studied methods and provide a watermark capacity comparison for different methods’ implementations. The research results will help in watermarking method selection in practice. Full article
(This article belongs to the Special Issue Advances in ECG/EEG Monitoring)
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19 pages, 13145 KiB  
Article
AI-Powered Noninvasive Electrocardiographic Imaging Using the Priori-to-Attention Network (P2AN) for Wearable Health Monitoring
by Shijie He, Hanrui Dong, Xianbin Zhang, Richard Millham, Lin Xu and Wanqing Wu
Sensors 2025, 25(6), 1810; https://doi.org/10.3390/s25061810 - 14 Mar 2025
Viewed by 586
Abstract
The rapid development of smart wearable devices has significantly advanced noninvasive, continuous health monitoring, enabling real-time collection of vital biosignals. Electrocardiographic imaging (ECGI), a noninvasive technique that reconstructs transmembrane potential (TMP) from body surface potential, has emerged as a promising method for reflecting [...] Read more.
The rapid development of smart wearable devices has significantly advanced noninvasive, continuous health monitoring, enabling real-time collection of vital biosignals. Electrocardiographic imaging (ECGI), a noninvasive technique that reconstructs transmembrane potential (TMP) from body surface potential, has emerged as a promising method for reflecting cardiac electrical activity. However, the ECG inverse problem’s inherent instability has hindered its practical application. To address this, we introduce a novel Priori-to-Attention Network (P2AN) that enhances the stability of ECGI solutions. By leveraging the one-dimensional nature of electrical signals and the body’s electrical propagation properties, P2AN uses small-scale convolutions for attention computation, integrating a priori physiological knowledge via cross-attention mechanisms. This approach eliminates the need for clinical TMP measurements and improves solution accuracy through normalization constraints. We evaluate the method’s effectiveness in diagnosing myocardial ischemia and ventricular hypertrophy, demonstrating significant improvements in TMP reconstruction and lesion localization. Moreover, P2AN exhibits high robustness in noisy environments, making it highly suitable for integration with wearable electrocardiographic clothing. By improving spatiotemporal accuracy and noise resilience, P2AN offers a promising solution for noninvasive, real-time cardiovascular monitoring using AI-powered wearable devices. Full article
(This article belongs to the Special Issue Advances in ECG/EEG Monitoring)
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22 pages, 873 KiB  
Article
EEG-Based Music Emotion Prediction Using Supervised Feature Extraction for MIDI Generation
by Oscar Gomez-Morales, Hernan Perez-Nastar, Andrés Marino Álvarez-Meza, Héctor Torres-Cardona and Germán Castellanos-Dominguez
Sensors 2025, 25(5), 1471; https://doi.org/10.3390/s25051471 - 27 Feb 2025
Viewed by 1160
Abstract
Advancements in music emotion prediction are driving AI-driven algorithmic composition, enabling the generation of complex melodies. However, bridging neural and auditory domains remains challenging due to the semantic gap between brain-derived low-level features and high-level musical concepts, making alignment computationally demanding. This study [...] Read more.
Advancements in music emotion prediction are driving AI-driven algorithmic composition, enabling the generation of complex melodies. However, bridging neural and auditory domains remains challenging due to the semantic gap between brain-derived low-level features and high-level musical concepts, making alignment computationally demanding. This study proposes a deep learning framework for generating MIDI sequences aligned with labeled emotion predictions through supervised feature extraction from neural and auditory domains. EEGNet is employed to process neural data, while an autoencoder-based piano algorithm handles auditory data. To address modality heterogeneity, Centered Kernel Alignment is incorporated to enhance the separation of emotional states. Furthermore, regression between feature domains is applied to reduce intra-subject variability in extracted Electroencephalography (EEG) patterns, followed by the clustering of latent auditory representations into denser partitions to improve MIDI reconstruction quality. Using musical metrics, evaluation on real-world data shows that the proposed approach improves emotion classification (namely, between arousal and valence) and the system’s ability to produce MIDI sequences that better preserve temporal alignment, tonal consistency, and structural integrity. Subject-specific analysis reveals that subjects with stronger imagery paradigms produced higher-quality MIDI outputs, as their neural patterns aligned more closely with the training data. In contrast, subjects with weaker performance exhibited auditory data that were less consistent. Full article
(This article belongs to the Special Issue Advances in ECG/EEG Monitoring)
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28 pages, 4312 KiB  
Article
Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)
by Vessela Krasteva, Ivo Iliev and Serafim Tabakov
Sensors 2024, 24(6), 1883; https://doi.org/10.3390/s24061883 - 15 Mar 2024
Cited by 1 | Viewed by 2547
Abstract
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. [...] Read more.
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300–2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3–7 μV, PRD = 2–5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points’ time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts. Full article
(This article belongs to the Special Issue Advances in ECG/EEG Monitoring)
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13 pages, 5333 KiB  
Communication
A High-Performance System for Weak ECG Real-Time Detection
by Kun Xu, Yi Yang, Yu Li, Yahui Zhang and Limin Zhang
Sensors 2024, 24(4), 1088; https://doi.org/10.3390/s24041088 - 7 Feb 2024
Cited by 1 | Viewed by 2309
Abstract
Wearable devices have been widely used for the home monitoring of physical activities and healthcare conditions, among which ambulatory electrocardiogram (ECG) stands out for the diagnostic cardiovascular information it contains. Continuous and unobtrusive sensing often requires the integration of wearable sensors to existing [...] Read more.
Wearable devices have been widely used for the home monitoring of physical activities and healthcare conditions, among which ambulatory electrocardiogram (ECG) stands out for the diagnostic cardiovascular information it contains. Continuous and unobtrusive sensing often requires the integration of wearable sensors to existing devices such as watches, armband, headphones, etc.; nonetheless, it is difficult to detect high-quality ECG due to the nature of low signal amplitude at these areas. In this paper, a high-performance system with multi-channel signal superposition for weak ECG real-time detection is proposed. Firstly, theoretical analysis and simulation is performed to demonstrate the effectiveness of this system design. The detection system, including electrode array, acquisition board, and the application (APP), is then developed and the electrical characteristics are measured. A common mode rejection ratio (CMRR) of up to 100 dB and input inferred voltage noise below 1 μV are realized. Finally, the technique is implemented in form of ear-worn and armband devices, achieving an SNR over 20 dB. Results are also compared with the simultaneous recording of standard lead I ECG. The correlation between the heart rates derived from experimental and standard signals is higher than 0.99, showing the feasibility of the proposed technique. Full article
(This article belongs to the Special Issue Advances in ECG/EEG Monitoring)
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