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Applied Sciences
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Advances in Electrocardiogram (ECG) Signal Processing and Its Applications
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Advances in Electrocardiogram (ECG) Signal Processing and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 951

Special Issue Editor

Dr. Keun-Chang Kwak

E-Mail Website
Guest Editor
Department of Electronics Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: biometrics; computational intelligence; human-robot interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is concerned with Electrocardiogram (ECG) signal processing and its applications. Furthermore, it includes preprocessing, feature extraction, disease diagnosis, biometrics, and various real-world applications based on deep learning and computational intelligence.

Dr. Keun-Chang Kwak
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.

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Keywords

  • ECG preprocessing
  • ECG feature extraction
  • ECG disease diagnosis
  • ECC biometrics
  • ECG applications using deep learning
  • biosignal applications using computational intelligence

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Published Papers (1 paper)

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Research

13 pages, 4569 KiB  
Article
End-to-End Electrocardiogram Signal Transformation from Continuous-Wave Radar Signal Using Deep Learning Model with Maximum-Overlap Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Network Layers
by Tae-Wan Kim and Keun-Chang Kwak
Appl. Sci. 2024, 14(19), 8730; https://doi.org/10.3390/app14198730 - 27 Sep 2024
Viewed by 596
Abstract
This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory [...] Read more.
This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) with a maximum-overlap discrete wavelet transform (MODWT) layer and an adaptive neuro-fuzzy network (ANFN) layer. The proposed method has the advantage of developing existing deep networks and machine learning to reconstruct signals through CW radars to acquire ECG biological information in a non-contact manner. The fully connected (FC) layer of the CNN is replaced by an ANFN layer suitable for resolving black boxes and handling complex nonlinear data. The MODWT layer is activated via discrete wavelet transform frequency decomposition with maximum-overlap to extract ECG-related frequency components from radar signals to generate essential information. In order to evaluate the performance of the proposed model, we use a dataset of clinically recorded vital signs with a synchronized reference sensor signal measured simultaneously. As a result of the experiment, the performance is evaluated by the mean squared error (MSE) between the measured and reconstructed ECG signals. The experimental results reveal that the proposed model shows good performance in comparison to the existing deep learning model. From the performance comparison, we confirm that the ANFN layer preserves the nonlinearity of information received from the model by replacing the fully connected layer used in the conventional deep learning model. Full article
(This article belongs to the Special Issue Advances in Electrocardiogram (ECG) Signal Processing and Its Applications)
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