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Advanced Sensing and Image Processing Techniques for Healthcare Applications—2nd Edition

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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: closed (10 August 2023) | Viewed by 11873

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Special Issue Editors

Dr. Vahid Abolghasemi

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Guest Editor

School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester, UK
Interests: biomedical signal and image processing; compressive sensing; dictionary learning; blind source separation
Special Issues, Collections and Topics in MDPI journals

Dr. Hossein Anisi

E-Mail Website
Guest Editor

School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: wireless sensor and actuator networks; body area networks; internet of things
Special Issues, Collections and Topics in MDPI journals

Dr. Saideh Ferdowsi

E-Mail Website
Guest Editor

School of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester, UK
Interests: biomedical signal and image processing; data fusion; blind source separation and machine/deep learning; EEG; fMRI; ECG
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Developing new technologies for health and social care sections have always been of particular attention to researchers. Additionally, the recent increasing rate of aging population, particularly in developed countries, has doubled the demand for intelligent systems for the elderly. On the other hand, super-fast advances in technology and science have raised expectations of inventing new monitoring and assistive technologies for accurate and delay-sensitive acquisition, processing, transmission, and interpretation of humans’ physiological and behavioural data. Therefore, a variety of enabling techniques such as signal and image processing, machine learning, and compression techniques could be used to improve such systems and achieve the aforementioned goal. Therefore, the ultimate outcome would be increased quality of life and improve the healthcare services to the older populations.

Owing to the success of the first volume, we have edited this second one. This Special Issue aims to attract the latest research and findings in design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificial intelligent devices and algorithms to assist/monitor elderly, patients, and disabled population.

Topics of interest include, but are not restricted to:

Biomedical signal and image processing;
Smart monitoring and assisted living systems;
Deep learning for healthcare data;
Sensor fusion of biomedical data;
Compressive sensing of biomedical data;
Cloud/Edge/Fog computing for healthcare systems;
Smart phone-based vital signal monitoring;
Brain Computer Interface for disabled;
Wireless body sensor networks;
Risks and accidents detection for elderly care;
Activity recognition;
Big data analysis for healthcare applications;
IoT Applications in healthcare;
Sensors and actuators in healthcare systems;
Mental disorder detection;
Smart breathing activity monitoring;
Non-invasive glucose monitoring.

Dr. Vahid Abolghasemi
Dr. Hossein Anisi
Dr. Saideh Ferdowsi
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

biomedical engineering
signal processing
image processing
machine learning
wireless sensor networks
internet of things
body area network
deep neural networks
dictionary learning
compressive sensing
big data
brain computer interface
artificial intelligence
healthcare technology
telemedicine

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Published Papers (5 papers)

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Research

16 pages, 2023 KiB

Open AccessArticle

Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg

by Anna Zulauf-Czaja, Bethel Osuagwu and Aleksandra Vuckovic

Sensors 2023, 23(12), 5601; https://doi.org/10.3390/s23125601 - 15 Jun 2023

Viewed by 1831

Abstract

The aim of the study was to test the feasibility of visual-neurofeedback-guided motor imagery (MI) of the dominant leg, based on source analysis with real-time sLORETA derived from 44 EEG channels. Ten able-bodied participants took part in two sessions: session 1 sustained MI without feedback and session 2 sustained MI of a single leg with neurofeedback. MI was performed in 20 s on and 20 s off intervals to mimic functional magnetic resonance imaging. Neurofeedback in the form of a cortical slice presenting the motor cortex was provided from a frequency band with the strongest activity during real movements. The sLORETA processing delay was 250 ms. Session 1 resulted in bilateral/contralateral activity in the 8–15 Hz band dominantly over the prefrontal cortex while session 2 resulted in ipsi/bilateral activity over the primary motor cortex, covering similar areas as during motor execution. Different frequency bands and spatial distributions in sessions with and without neurofeedback may reflect different motor strategies, most notably a larger proprioception in session 1 and operant conditioning in session 2. Single-leg MI might be used in the early phases of rehabilitation of stroke patients. Simpler visual feedback and motor cueing rather than sustained MI might further increase the intensity of cortical activation. Full article

(This article belongs to the Special Issue Advanced Sensing and Image Processing Techniques for Healthcare Applications—2nd Edition)

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15 pages, 6375 KiB

Open AccessArticle

Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging

by Matilde Pato, Ricardo Eleutério, Raquel C. Conceição and Daniela M. Godinho

Sensors 2023, 23(3), 1496; https://doi.org/10.3390/s23031496 - 29 Jan 2023

Cited by 3 | Viewed by 2321

Abstract

Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to

3.07

dB, a Signal-to-Mean Ratio of up to

20.78

dB and a Location Error of

1.58

mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI. Full article

(This article belongs to the Special Issue Advanced Sensing and Image Processing Techniques for Healthcare Applications—2nd Edition)

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8 pages, 3791 KiB

Open AccessArticle

Flexible Neural Probe Fabrication Enhanced with a Low-Temperature Cured Polyimide and Platinum Electrodeposition

by João R. Freitas, Sara Pimenta, Diogo J. Santos, Bruno Esteves, Nuno M. Gomes and José H. Correia

Sensors 2022, 22(24), 9674; https://doi.org/10.3390/s22249674 - 10 Dec 2022

Cited by 5 | Viewed by 2117

Abstract

Polyimide is an emerging and very interesting material for substrate and passivation of neural probes. However, the standard curing temperature of polyimide (350 °C) is critical for the microelectrodes and contact pads of the neural probe, due to the thermal oxidation of the metals during the passivation process of the neural probe. Here, the fabrication process of a flexible neural probe, enhanced with a photosensitive and low-temperature cured polyimide, is presented. Annealing tests were performed with metallic films deposited on polyimide, which led to the reduction of the curing temperature to 250 °C, with no significant irregularities in the metallic sample annealed at that temperature and an effective polyimide curing. The use of a lower curing temperature reduces the thermal oxidation of the metals during the polyimide curing process to passivate the neural probe. Additionally, in this fabrication process, the microelectrodes of the neural probe were coated with electrodeposited platinum (Pt), only after the passivation process, and its electrochemical performance was accessed. At 1 kHz, the impedance of the microelectrodes before Pt electrodeposition was approximately 1.2 MΩ, and after Pt electrodeposition, it was approximately 350 kΩ. Pt electrodeposition changed the equivalent circuit of the microelectrodes and reduced their impedance, which will be crucial for future in-vivo tests to acquire the electrical activity of the neurons with the fabricated neural probe. Full article

(This article belongs to the Special Issue Advanced Sensing and Image Processing Techniques for Healthcare Applications—2nd Edition)

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22 pages, 4729 KiB

Open AccessArticle

COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images

by Wei Wang, Shuxian Liu, Huan Xu and Le Deng

Sensors 2022, 22(21), 8578; https://doi.org/10.3390/s22218578 - 7 Nov 2022

Cited by 4 | Viewed by 2384

Abstract

Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods. Full article

(This article belongs to the Special Issue Advanced Sensing and Image Processing Techniques for Healthcare Applications—2nd Edition)

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26 pages, 3551 KiB

Open AccessArticle

Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network

by Sumit Kumar Singh, Vahid Abolghasemi and Mohammad Hossein Anisi

Sensors 2022, 22(16), 6261; https://doi.org/10.3390/s22166261 - 20 Aug 2022

Cited by 10 | Viewed by 2233

Abstract

Recent years evidenced an increase in the total number of skin cancer cases, and it is projected to grow exponentially. This paper proposes a computer-aided diagnosis system for the classification of a malignant lesion, where the acquired image is primarily pre-processed using novel methods. Digital artifacts such as hair follicles and blood vessels are removed, and thereafter, the image is enhanced using a novel method of histogram equalization. Henceforth, the pre-processed image undergoes the segmentation phase, where the suspected lesion is segmented using the Neutrosophic technique. The segmentation method employs a thresholding-based method along with a pentagonal neutrosophic structure to form a segmentation mask of the suspected skin lesion. The paper proposes a deep neural network base on Inception and residual blocks with softmax block after each residual block which makes the layer wider and easier to learn the key features more quickly. The proposed classifier was trained, tested, and validated over PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets. The proposed segmentation model yields an accuracy mark of 99.50%, 99.33%, 98.56% and 98.04% for these datasets, respectively. These datasets are augmented to form a total of 103,554 images for training, which make the classifier produce enhanced classification results. Our experimental results confirm that the proposed classifier yields an accuracy score of 99.50%, 99.33%, 98.56%, and 98.04% for PH2, ISIC 2017, 2018, and 2019, respectively, which is better than most of the pre-existing classifiers. Full article

(This article belongs to the Special Issue Advanced Sensing and Image Processing Techniques for Healthcare Applications—2nd Edition)

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