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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: 30 November 2024 | Viewed by 1965

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

Published Papers (2 papers)

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Research

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://0-doi-org.brum.beds.ac.uk/10.3390/s24061883 - 15 Mar 2024
Cited by 1 | Viewed by 821
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://0-doi-org.brum.beds.ac.uk/10.3390/s24041088 - 7 Feb 2024
Viewed by 863
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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