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AI on Biomedical Signal Sensing and Processing for Health Monitoring—2nd Edition

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Biosensors".

Deadline for manuscript submissions: 31 May 2027 | Viewed by 1108

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Guest Editor
Department of Computer Sciences and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan
Interests: speech recognition; biomedical signal processing; intelligent computation; digital signal processing; control systems
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Special Issue Information

Dear Colleagues,

Following the success of the first edition of our Special Issue entitled “AI on Biomedical Signal Sensing and Processing for Health Monitoring”, we again invite our colleagues from across the world to contribute their expertise, insight, and findings in the form of original research articles and reviews for the current new Special Issue, entitled “AI on Biomedical Signal Sensing and Processing for Health Monitoring—2nd Edition”.

Due to the significant progress in hardware and software development of information technologies in recent years, artificial intelligent (AI) technologies have become more powerful and feasible than before. As a result, it is promising to explore the topic of AI methods implemented for practical applications in biomedical signal sensing and processing. This Special Issue aims to bring together researchers in this area to break down barriers and develop innovative realizable AI biomedical signal processing systems. The developed systems can then predict the health conditions for users more precisely in the applications of health motoring. This Special Issue will hence focus on emerging AI technologies for biomedical signal processing applications, including the measurement and analysis of biomedical signals and images in clinical medicine. Original research and review articles are both welcome.

Potential topics include, but are not limited to, the following:

  • AI on biomedical signal processing for sleep status detection;
  • AI on biomedical signal processing for emotion recognition;
  • Detection of arrhythmia based on biomedical signals by using AI technologies;
  • AI on biomedical signal processing for brain diseases;
  • Feature extraction and pattern recognition of biomedical signals;
  • Applications of biomedical signals on health monitoring by using non-invasion devices;
  • AI on biomedical signal processing for communication disorders detection;
  • Biomedical signal-inspired non-invasion devices design for sensing and monitoring.

Prof. Dr. Shing-Tai Pan
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • artificial intelligence (AI)
  • biomedical signal processing
  • biomedical signal sensing
  • disease diagnosis
  • human health monitoring
  • non-invasion device

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Published Papers (1 paper)

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Research

29 pages, 5934 KB  
Article
Autonomic Signature-Driven Anesthesia Depth Monitoring with Biomimetic Wearable ECG and Knowledge Graph-Augmented Deep Networks
by Aoran Bao and Cheng Ding
Sensors 2026, 26(11), 3498; https://doi.org/10.3390/s26113498 - 2 Jun 2026
Viewed by 656
Abstract
Considerable efforts have been devoted to accurately monitoring the depth of anesthesia to ensure patient safety during surgery. Traditional approaches typically rely on electroencephalogram (EEG)-based indices, such as the Bispectral Index (BIS), which require specialized equipment. In contrast, electrocardiogram (ECG) signals are widely [...] Read more.
Considerable efforts have been devoted to accurately monitoring the depth of anesthesia to ensure patient safety during surgery. Traditional approaches typically rely on electroencephalogram (EEG)-based indices, such as the Bispectral Index (BIS), which require specialized equipment. In contrast, electrocardiogram (ECG) signals are widely available in clinical settings and can be conveniently acquired via wearable devices, while also exhibiting strong responsiveness to anesthetic agents. Inspired by biomimetic physiological regulation mechanisms, this study proposes a wearable-compatible ECG-based framework for depth-of-anesthesia detection that leverages autonomic nervous system characteristics and a knowledge graph-enhanced graph convolutional network (GCN). ECG recordings from 110 patients were preprocessed, and 20 anesthesia-related features were extracted, spanning morphological, statistical, spectral, heart rate variability (HRV), and entropy-based descriptors; feature selection methods identified 13 discriminative features. A patient-level knowledge graph was first constructed using the 88 training patients (1760 nodes), and test patient nodes were incorporated only after training was complete for inductive inference. Experimental results demonstrate that the proposed deep knowledge GCN achieves a test accuracy of 98.18% in distinguishing between awake and deep sleep anesthesia states, indicating that biomimetic, wearable-compatible ECG analysis combined with knowledge graph learning holds strong potential as a cost-effective alternative to traditional EEG-based anesthesia monitoring systems. Full article
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