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Advanced Sensor Technologies for Biomedical-Information Processing
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Advanced Sensor Technologies for Biomedical-Information Processing

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 11229

Special Issue Editors

Dr. Shuo-Tsung Chen

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Guest Editor
Department of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan
Interests: Internet of Things; biomedicine; artificial intelligence; digital image processing; digital signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chihyu Hsu

E-Mail Website
Guest Editor
School of Transportation, Fujian University of Technology, Fuzhou, Fujian 350118, China
Interests: artificial intelligence; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Huang-Nan Huang

E-Mail Website
Guest Editor
Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan
Interests: biomedicine; artificial intelligence; digital signal processing
Dr. Chur-Jen Chen

E-Mail Website
Guest Editor
Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan
Interests: biomedicine; artificial intelligence; digital signal processing

Special Issue Information

Dear Colleagues,

With the rapid development of sensors, the Internet of Things, and Artificial Intelligence, academic research and industrial development related to biomedicine are innovating significantly. This Special Issue covers a wide range of topics related to sensor technology in biomedicine, including bio-signal processing, bio-image processing, healthcare, telemedicine, medicine and nursing, etc. Paper submissions are now welcome.

Dr. Shuo-Tsung Chen
Prof. Dr. Chihyu Hsu
Dr. Huang-Nan Huang
Dr. Chur-Jen Chen
Guest Editors

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

  • sensors
  • Internet of Things
  • artificial Intelligence
  • biomedicine

Published Papers (3 papers)

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Research

14 pages, 4202 KiB  
Article
Ultrasound Image Temperature Monitoring Based on a Temporal-Informed Neural Network
by Yuxiang Han, Yongxing Du, Limin He, Xianwei Meng, Minchao Li and Fujun Cao
Sensors 2024, 24(15), 4934; https://doi.org/10.3390/s24154934 - 30 Jul 2024
Viewed by 313
Abstract
Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using [...] Read more.
Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25–65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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13 pages, 1791 KiB  
Article
Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals
by Shuo-Tsung Chen, Ren-Jie Ye, Tsung-Hsien Wu, Chun-Wen Cheng, Po-You Zhan, Kuan-Ming Chen and Wan-Yu Zhong
Sensors 2023, 23(22), 9199; https://doi.org/10.3390/s23229199 - 15 Nov 2023
Viewed by 1236
Abstract
The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an [...] Read more.
The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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13 pages, 1760 KiB  
Article
Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments
by Wei-Hsun Wang and Wen-Shin Hsu
Sensors 2023, 23(13), 5913; https://doi.org/10.3390/s23135913 - 26 Jun 2023
Cited by 19 | Viewed by 9024
Abstract
With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement [...] Read more.
With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement data for care or exposure. These systems provide comprehensive communication and home exposure reports and enable the involvement of rehabilitation specialists and other experts. Silver technology enables the realization of health management in long-term care services, workplace care, and health applications, facilitating disease prevention and control, improving disease management, reducing home isolation, alleviating family burden in terms of nursing, and promoting health and disease control. Research and development efforts in forward-looking cross-domain precision health technology, system construction, testing, and integration are carried out. This integrated project consists of two main components. The Integrated Intelligent Long-Term Care Service Management System focuses on building a personalized care service system for the elderly, encompassing health, nutrition, diet, and health education aspects. The Wearable Internet of Things Care System primarily supports the development of portable physiological signal detection devices and electronic fences. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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