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Advances in Signal Processing for Biomedical Applications and Healthcare

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Dear Colleagues,

Various technologies in the areas of communication/computer networks and innovations in healthcare have introduced a radical change in medical environment including patient diagnostic data and patient biological signal facilities and processing. In fact, new ideas, services, processes, and products have been introduced with a view to improving treatment, diagnosis, education, outreach, prevention and research with long-term goals of enhancing quality, safety, outcomes, efficiency and costs. At present, healthcare technologies include several parameter categories such as devices (equipment and supplies), medical and surgical procedures (e.g., laparoscopy), support systems (e.g., telehealth, telemedicine), and organizing and administrative systems. Projections into the future predict an even greater role of technology in medical practice and healthcare.

This Special Issue addresses the most recent research in medical application and healthcare. Topics of interest for this Special Issue include, but are not limited to, the following:

Development of computer-aided detection/diagnosis systems for medical applications;

Effective trends for biosignal/bioimage processing and analysis for monitoring treatment efficacy;
Advances in healthcare monitoring systems;
Digital technologies for supporting healthcare;
Algorithms and techniques for signal, image and video processing in healthcare;
Multidimensional digital signal processing to support diagnosis systems.

For this Special Issue, original research articles and reviews are welcome. We look forward to receiving your contributions.

Dr. Cataldo Guaragnella
Dr. Maria Rizzi
Guest Editors

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Keywords

computer-aided diagnosis systems
digital technologies for healthcare
biomedical signal/image/video processing
algorithms and techniques for healthcare

Published Papers (2 papers)

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17 pages, 48171 KiB

Open AccessArticle

S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine

by Donglei Song, Hongda Zhang, Lida Shi, Hao Xu and Ying Xu

Sensors 2024, 24(13), 4046; https://doi.org/10.3390/s24134046 - 21 Jun 2024

Viewed by 541

Abstract

Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject’s cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy. Full article

(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)

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18 pages, 3559 KiB

Open AccessArticle

Novel Metric for Non-Invasive Beat-to-Beat Blood Pressure Measurements Demonstrates Physiological Blood Pressure Fluctuations during Pregnancy

by David Zimmermann, Hagen Malberg and Martin Schmidt

Sensors 2024, 24(10), 3151; https://doi.org/10.3390/s24103151 - 15 May 2024

Viewed by 820

Abstract

Beat-to-beat (B2B) variability in biomedical signals has been shown to have high diagnostic power in the treatment of various cardiovascular and autonomic disorders. In recent years, new techniques and devices have been developed to enable non-invasive blood pressure (BP) measurements. In this work, we aim to establish the concept of two-dimensional signal warping, an approved method from ECG signal processing, for non-invasive continuous BP signals. To this end, we introduce a novel BP-specific beat annotation algorithm and a B2B-BP fluctuation (B2B-BPF) metric novel for BP measurements that considers the entire BP waveform. In addition to careful validation with synthetic data, we applied the generated analysis pipeline to non-invasive continuous BP signals of 44 healthy pregnant women (30.9 ± 5.7 years) between the 21st and 30th week of gestation (WOG). In line with established variability metrics, a significant increase (p < 0.05) in B2B-BPF can be observed with advancing WOGs. Our processing pipeline enables robust extraction of B2B-BPF, demonstrates the influence of various factors such as increasing WOG or exercise on blood pressure during pregnancy, and indicates the potential of novel non-invasive biosignal sensing techniques in diagnostics. The results represent B2B-BP changes in healthy pregnant women and allow for future comparison with those signals acquired from women with hypertensive disorders. Full article

(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)

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