Advanced Methods of Biomedical Signal Processing II

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1496

Special Issue Editor


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Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: biosensors; heart rate variability; autonomic nervous system; electrodermal activity; biomedical digital signal processing
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Special Issue Information

Dear Colleagues,

Biomedical sensing technology is most commonly perceived as a wearable device, such as smart glasses, smart watches, and smart clothing, which have become more and more popular in recent years. People have become more inclined to monitor themselves more closely than ever, and technology is enabling them to do so. The trend of wearable technology is predicted to continue as technology improves. The challenges of new sensing technologies include quality control, data corruption detection and correction, as well as automatic interpretation of massive amounts of data. For this reason, in recent years, many researchers have been working to advance the methods for processing biomedical signals, utilizing methods that include time-varying spectral analysis, entropy, adaptive filtering, multivariate probability distributions, machine learning, deep learning, nonlinear regression, Markov chains, Bayesian estimation, etc. In this Special Issue, we invite research papers presenting novel and advanced methods of biomedical signal processing, applied to, but not limited to, EDA, ECG, EMG, EEG, PPG, and other biomedical signals or images, as well as their application in the detection and correction of data corruption, and the interpretation, diagnosis, or prediction of physiological conditions or diseases.

Dr. Hugo Fernando Posada-Quintero
Guest Editor

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Keywords

  • biomedical signals
  • images
  • signal processing
  • signal analysis

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Published Papers (1 paper)

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Research

18 pages, 4773 KiB  
Article
Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques
by Filippo Laganà, Danilo Pratticò, Giovanni Angiulli, Giuseppe Oliva, Salvatore A. Pullano, Mario Versaci and Fabio La Foresta
Signals 2024, 5(3), 476-493; https://doi.org/10.3390/signals5030025 - 26 Jul 2024
Viewed by 1122
Abstract
The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis of surface electromyographic (sEMG) signals. The system analyzes sEMG signals to understand muscle function and [...] Read more.
The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis of surface electromyographic (sEMG) signals. The system analyzes sEMG signals to understand muscle function and neuromuscular control, employing convolutional neural networks (CNNs) for pattern recognition. The electrical signals analyzed on healthy and unhealthy subjects are acquired using a meticulously developed integrated circuit system featuring biopotential acquisition electrodes. The signals captured in the database are extracted, classified, and interpreted by the application of CNNs with the aim of identifying patterns indicative of neuromuscular problems. By leveraging advanced learning techniques, the proposed method addresses the non-stationary nature of sEMG recordings and mitigates cross-talk effects commonly observed in electrical interference patterns captured by surface sensors. The integration of an AI algorithm with the signal acquisition device enhances the qualitative outcomes by eliminating redundant information. CNNs reveals their effectiveness in accurately deciphering complex data patterns from sEMG signals, identifying subjects with neuromuscular problems with high precision. This paper contributes to the landscape of biomedical research, advocating for the integration of advanced computational techniques to unravel complex physiological phenomena and enhance the utility of sEMG signal analysis. Full article
(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing II)
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