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Intelligent Sensing and Computing for Biomedical Signal Processing and Analysis

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

Deadline for manuscript submissions: 25 December 2026 | Viewed by 832

Editors

School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China
Interests: artificial intelligence; medical image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Information and Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: remote sensing; deep learning; hyperspectral imaging and applications
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: artificial intelligence; medical image processing

E-Mail Website
Guest Editor
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: artificial intelligence; medical image processing;computer vision

Special Issue Information

Dear Colleagues,

Recent years have witnessed rapid advancements in intelligent sensing and computing for biomedical signal processing and analysis, driven by the integration of artificial intelligence with modern signal processing, data-driven modeling, and healthcare technologies. These developments have significantly enhanced the acquisition, representation, and interpretation of complex biomedical data, encompassing physiological signals, medical imaging data, and heterogeneous multimodal clinical information. Such progress has enabled transformative applications in areas such as disease diagnosis, health monitoring, brain–computer interfaces, and personalized medicine. Nevertheless, challenges such as noise interference, limited labeled data, inter-subject variability, and the need for real-time and reliable analysis continue to hinder the effectiveness of conventional approaches, underscoring the necessity for more robust, adaptive, and intelligent computational frameworks.

This Special Issue is organized in conjunction with CISP-BMEI 2026 (http://www.cisp-bmei.cn/), a premier international congress that brings together the communities of image and signal processing (CISP) and biomedical engineering and informatics (BMEI), fostering interdisciplinary exchange and collaboration. Building upon the vibrant discussions and innovative research presented at CISP-BMEI 2026, this Special Issue aims to disseminate state-of-the-art advances in intelligent sensing and computing for biomedical signal processing and analysis, as well as provide an open forum for researchers and practitioners to share novel algorithms, system designs, and real-world applications that advance both theoretical insights and practical impact.

We welcome contributions addressing theoretical foundations, innovative methodologies, and practical applications in this interdisciplinary field. Topics of interest include, but are not limited to, the following:

  • Intelligent sensing technologies for biomedical signal acquisition;
  • Deep learning for biomedical signal processing and analysis;
  • Biomedical signal-based disease diagnosis and prediction.

Dr. Zhe Liu 
Dr. Jiansheng Wang
Dr. Jun Chen
Dr. Kai Han
Guest Editors

Manuscript Submission Information

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Keywords

  • intelligent sensing
  • biomedical signal processing
  • artificial intelligence
  • deep learning
  • medical image processing

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Published Papers (1 paper)

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Research

21 pages, 14719 KB  
Article
Respiratory Disease Classification Using NMF-Enhanced Log-Mel Spectrograms and Convolutional Recurrent Neural Networks
by Bowen Han, Wei Quan, Bogdan Matuszewski and Dennis Corbett
Sensors 2026, 26(13), 4268; https://doi.org/10.3390/s26134268 - 4 Jul 2026
Viewed by 538
Abstract
Respiratory disease classification using lung sound recordings remains challenging due to signal interference, heterogeneous acquisition conditions, and substantial overlap among clinically related acoustic patterns. This study presents a framework for respiratory disease classification using NMF-enhanced log-mel spectrograms and deep neural classifiers. Respiratory sound [...] Read more.
Respiratory disease classification using lung sound recordings remains challenging due to signal interference, heterogeneous acquisition conditions, and substantial overlap among clinically related acoustic patterns. This study presents a framework for respiratory disease classification using NMF-enhanced log-mel spectrograms and deep neural classifiers. Respiratory sound recordings from two publicly available datasets were harmonized into a unified label space comprising Asthma, Bronchiectasis, Bronchiolitis, COPD, Healthy, Pneumonia and URTI. Following signal standardization and fixed-length segmentation, a non-negative matrix factorization (NMF)-based enhancement stage was applied to increase the salience of respiratory components prior to log-mel spectrogram generation. The proposed classifier was a convolutional recurrent neural network (CRNN) that combined convolutional feature extraction, bidirectional recurrent modelling, and attention-based temporal aggregation. For comparison, RDLINet, a conventional CNN, ResNet, and a YOLO-style backbone were implemented under the same preprocessing and training framework. Experimental results demonstrated that the proposed CRNN achieved the best overall performance, attaining 96.14 ± 0.50% accuracy and 94.05 ± 1.21% Macro-F1 on the unified seven-class cohort. Class-wise analysis, confusion-matrix evaluation, and output-space visualization further showed that the CRNN provided more balanced recognition across disease categories and clearer class separation than competing architectures. These findings indicate that NMF-enhanced spectro-temporal modelling combined with convolutional recurrent learning offers an effective approach for automated multi-class respiratory disease classification. Full article
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