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Advanced Sensing Techniques in Biomedical Signal Processing

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 9513

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Special Issue Editor

Prof. Dr. Dakun Lai

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Guest Editor

School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: IOTs and wearable devices; biomedical imaging and signal processing; bioelectromagnetism and medical applications; AI-based diagnosis of cardiac/neuro-electrical disorders
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Special Issue Information

Dear Colleagues,

The diagnosis and prognosis of human heart and brain conditions in hospital predominantly rely on biomedical signals and sensor data processing; this medical data is analyzed to inform the study of a patient’s cardiac and neurological health. Currently, more innovative technologies and intelligent systems are required to process a larger volume of medical data and provide a higher quality of healthcare services, enabling the automatic and accurate detection of symptoms of diseases in their early stages.

The aim of this Special Issue is to present the latest innovative research from scholars and experts in the field of electrophysiological signal and image processing using computer pattern recognition and/or deep learning. This includes papers that cover areas such as biomedical engineering, computer vision, and Internet of Things, as well as theoretical and practical aspects of various sensors and information theory in medical image processing.

Original research and review articles for this Special Issue can address topics including, but not limited to, the following:

ECG/EEG sensing and signal processing;
Medical image processing for heart or brain imaging;
Bioinformatics for healthcare engineering;
Supervised/unsupervised learning algorithm for ECG/EEG diagnosis applications;
Explainable AI in ECG/EEG applications;
Image retrieval, segmentation, grouping and shape;
AI chips and their implantation of machine learning and deep learning algorithms;
Internet of Things (IOT);
Applying machine learning-empowered sensing to industrial scenarios

Prof. Dr. Dakun Lai
Guest Editor

Manuscript Submission Information

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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

deep learning
biomedical imaging
electrophysiological signal
heart
brain

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Published Papers (7 papers)

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Research

11 pages, 1981 KB

Open AccessArticle

Eye Drift Signal Analysis Caused by Goggle Slippage in vHIT Measurements: A Signal Processing Perspective

by Ha Ngoc Khoan, Le Ky Bien, Tran Thi Nhan and Tran Van Nghia

Sensors 2026, 26(9), 2880; https://doi.org/10.3390/s26092880 - 5 May 2026

Viewed by 252

Abstract

This Technical Report presents a quantitative signal processing approach to analyze and correct eye drift during vestibulo-ocular reflex (VOR) measurements using the video Head Impulse Test (vHIT). The objective is to determine the extent of drift caused by goggle slippage—a technical artifact that can distort the VOR gain index. A total of 57 impulses were categorized into three protocols: Lateral, LARP, and RALP. For each impulse, peak head velocity and eye drift (estimated from the average velocity during the pre- and post-impulse rest periods) were extracted using a custom signal processing pipeline implemented in MATLAB R2020b and Python 3.11 64 bit. Results showed the highest drift in the RALP group (−7.41 deg/s) and the lowest in the LARP group (−3.08 deg/s). The correlation between head velocity and drift was most prominent in the RALP group (r > 0.7), highlighting the impact of stimulation direction on goggle stability. This study proposes a drift detection method to be integrated into VOR correction algorithms, thereby enhancing gain analysis and saccade detection in automated systems. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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21 pages, 6601 KB

Open AccessArticle

UDC-SNN: An Uncertainty-Aware Dynamic Cascading Framework with Spiking Neural Network for Balancing Performance and Energy in Multimodal Emotion Recognition

by Guihao Ran, Shengzhe Li, Zhiwen Jiang, Han Zhang, Xinyuan Long and Dakun Lai

Sensors 2026, 26(9), 2859; https://doi.org/10.3390/s26092859 - 3 May 2026

Viewed by 1164

Abstract

The aim of this study is to propose an uncertainty-aware dynamic cascading framework based on spiking neural network (UDC-SNN) for multimodal emotion recognition, particularly to address the inherent trade-off between recognition performance and energy efficiency. An asymmetric dynamic routing mechanism was proposed to enable demand-driven activation of the high-power electroencephalogram (EEG) branch, coupled with preliminary inference on a low-power electrocardiogram (ECG) branch and uncertainty quantification via Shannon entropy. Meanwhile, a parameter-free log-linear aggregation strategy was developed to transform modality-specific entropy into dynamic Bayesian weights through an exponential decay function, effectively mitigating the negative transfer effects induced by unimodal noise. The UDC-SNN was evaluated on the multimodal affective dataset DREAMER, comprising 23 subjects (170,660 segments). The averaged recognition accuracy and energy consumption across the three dimensions of valence, arousal, and dominance were 90.75% and 4.62

μ

J, respectively. The obtained results suggest that the proposed framework could potentially achieve a favorable balance between high emotion recognition and low energy consumption, thereby establishing its applicability for real-time monitoring in resource-constrained scenarios. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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18 pages, 2687 KB

Open AccessArticle

A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation

by Han Zhang, Xudong Hu, Xizhuang Zhang, Zhencheng Chen, Yongbo Liang and Gang Wang

Sensors 2026, 26(9), 2847; https://doi.org/10.3390/s26092847 - 2 May 2026

Viewed by 941

Abstract

Hypertension is a major risk factor for cardiovascular disease and requires effective long-term monitoring. Photoplethysmography (PPG), acquired from wearable optical sensors, offers a convenient and non-invasive signal source for cuffless blood pressure (BP) estimation, but existing studies have mainly emphasized model architecture optimization, with limited systematic investigation of signal representation. This study systematically compares seven one-dimensional-to-two-dimensional signal transformation methods and evaluates multiple architectural variants for PPG-based cuffless BP estimation under a unified framework. Experiments were conducted using PPG and arterial BP signals from the UCI Open Blood Pressure Database. The best-performing configuration, based on continuous wavelet transform (CWT), achieved estimation errors of 3.80 ± 5.02 mmHg for systolic BP and 1.65 ± 2.70 mmHg for diastolic BP. Further real-world validation on 26 participants using an Omron cuff-based monitor as the reference showed good consistency, with correlation coefficients of R = 0.96 for SBP and R = 0.74 for DBP. The results demonstrate that appropriate signal representation, particularly CWT, plays a critical role in improving estimation accuracy and robustness, and may facilitate the development of wearable cuffless BP monitoring systems. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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20 pages, 2130 KB

Open AccessArticle

A Functional Shape Framework for the Detection of Multiple Sclerosis Using Optical Coherence Tomography Images

by Homa Tahvilian, Raheleh Kafieh, Fereshteh Ashtari, M. N. S. Swamy and M. Omair Ahmad

Sensors 2026, 26(8), 2399; https://doi.org/10.3390/s26082399 - 14 Apr 2026

Viewed by 418

Abstract

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease. Optical coherence tomography (OCT) is a non-invasive imaging technique of the retina. The thickness of the ganglion cell–inner plexiform layer (GCIPL) obtained from an OCT image is a valuable biomarker for monitoring MS. Since the functional shape (F-shape)-based technique has proven to be an effective platform for detecting glaucoma using OCT images, in this paper, we develop an F-shape-based framework to distinguish MS subjects from healthy ones using the thickness of GCIPL. The thickness of the GCIPL layers in the macula region of OCT images in a selected region of interest (ROI) for a set of healthy and MS subjects is represented as F-shape objects, which are registered to a common template using atlas registration. The residual F-shapes, defined as the difference between the F-shape of this common template and the individual registered F-shapes, are used to train an support vector machine (SVM) classifier and subsequently to detect MS. Accuracy, sensitivity, specificity, and area under the curve (AUC) are used to evaluate and compare the classification performance of the proposed F-shape-based scheme and those of sectoral-based schemes. The proposed F-shape-based scheme is shown to significantly outperform the sectoral-based schemes. The superior performance of the proposed F-shape-based scheme can be attributed to the use of (i) a highly dense mesh formed on the ROI in the macula region, (ii) atlas registration that puts the F-shapes of all the subjects on a common platform, and (iii) residual thicknesses as input features for the classification. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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21 pages, 5131 KB

Open AccessArticle

Design and Characterization of a Hyperspectral Colposcope Based on Dual-LCTF VNIR Narrow-Band Illumination

by Carlos Vega, Raquel Leon, Norberto Medina, Himar Fabelo, Alicia Martín and Gustavo M. Callico

Sensors 2026, 26(4), 1255; https://doi.org/10.3390/s26041255 - 14 Feb 2026

Viewed by 442

Abstract

Early detection of precancerous cervical lesions is critical for improving patient management and clinical outcomes. Hyperspectral imaging has emerged as a promising non-invasive, label-free imaging modality for rapid medical diagnosis. This work presents the development of a liquid-crystal-tunable-filter-based hyperspectral colposcopy system covering the visible and near-infrared spectral ranges. The proposed system integrates two tunable filters into an existing Optomic OP-C5 clinical colposcope, enabling hyperspectral acquisition from 460 to 1000 nm with 130 spectral bands at 5 nm resolution using a panchromatic camera. Two alternative acquisition strategies were investigated: (i) filtering the light received by the system, or (ii) filtering the light emitted toward the sample. In addition, wavelength-dependent exposure control was studied to compensate for reduced system sensitivity and improve the signal-to-noise ratio in low-efficiency spectral regions. The system was benchmarked against a previous custom hyperspectral implementation based on a commercial camera. The comparative analysis highlights the advantages and limitations of both approaches, demonstrating the proposed system’s suitability for integration into clinical workflows and its potential for early detection of precancerous cervical lesions during routine colposcopic examinations. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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18 pages, 1376 KB

Open AccessArticle

EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection

by Ruixuan Chen, Xin Ma, Xusheng Li, Linfeng Sui, Taiyo Maeda, Qipeng Chen and Jianting Cao

Sensors 2026, 26(2), 474; https://doi.org/10.3390/s26020474 - 11 Jan 2026

Cited by 1 | Viewed by 1879

Abstract

Electroencephalography (EEG) plays a crucial role in clinical neurodiagnostics, particularly in epileptic focus localization and deep sleep detection. However, the limited availability of annotated EEG data hinders the generalization capability of deep learning models. This study proposes a unified EEG classification framework that applies three lightweight data augmentation techniques, namely time shifting, amplitude scaling, and noise addition, to enrich training diversity and enhance model robustness. The framework is evaluated using DeepConvNet, ShallowConvNet, and EEGNet on two public datasets that represent physiological and pathological EEG tasks. Experimental results show that data augmentation consistently improves classification performance across all models and tasks. Importantly, even when baseline accuracies are already high, the proposed augmentation strategies provide additional gains of up to approximately 2.06% in deep sleep detection and 4.07% in epileptic focus localization. These findings demonstrate that simple augmentation methods can effectively improve the robustness and classification performance of EEG-based deep learning models, especially under data-limited conditions. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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16 pages, 2489 KB

Open AccessArticle

Sentence-Level Silent Speech Recognition Using a Wearable EMG/EEG Sensor System with AI-Driven Sensor Fusion and Language Model

by Nicholas Satterlee, Xiaowei Zuo, Kee Moon, Sung Q. Lee, Matthew Peterson and John S. Kang

Sensors 2025, 25(19), 6168; https://doi.org/10.3390/s25196168 - 5 Oct 2025

Cited by 1 | Viewed by 3760

Abstract

Silent speech recognition (SSR) enables communication without vocalization by interpreting biosignals such as electromyography (EMG) and electroencephalography (EEG). Most existing SSR systems rely on high-density, non-wearable sensors and focus primarily on isolated word recognition, limiting their practical usability. This study presents a wearable SSR system capable of accurate sentence-level recognition using single-channel EMG and EEG sensors with real-time wireless transmission. A moving window-based few-shot learning model, implemented with a Siamese neural network, segments and classifies words from continuous biosignals without requiring pauses or manual segmentation between word signals. A novel sensor fusion model integrates both EMG and EEG modalities, enhancing classification accuracy. To further improve sentence-level recognition, a statistical language model (LM) is applied as post-processing to correct syntactic and lexical errors. The system was evaluated on a dataset of four military command sentences containing ten unique words, achieving 95.25% sentence-level recognition accuracy. These results demonstrate the feasibility of sentence-level SSR using wearable sensors through a window-based few-shot learning model, sensor fusion, and ML applied to limited simultaneous EMG and EEG signals. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques in Biomedical Signal Processing)

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