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Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare

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

Deadline for manuscript submissions: closed (10 September 2023) | Viewed by 24688

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Special Issue Editors

Dr. Wenyan Jia

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Guest Editor

Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
Interests: biomedical signal and image processing; wearable electronic devices; the implementation of mobile technology in healthcare
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yi Gao

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Guest Editor

Department of Medical Information Engineering, Shenzhen University, Shenzhen 518060, China
Interests: medical image computing; artificial intelligence

Prof. Dr. Zhi-Hong Mao

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Guest Editor

Department of Electrical and Computer Engineering and Department of Bioengineering at the University of Pittsburgh, Pittsburgh, PA 15260, USA
Interests: human-in-the-loop control systems; control of networked and large-scale dynamical systems

Prof. Dr. Mingui Sun

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Guest Editor

University of Pittsburgh, Department of Neurosurgery, Pittsburgh, PA, USA
Interests: neurophysiological signals and systems; biosensor designs; brain-computer interface; bioelectronics and bioinformatics

Special Issue Information

Dear Colleagues,

Due to the wide applications of sensors in the field of healthcare, analysis of data acquired by these sensors (e.g., electronic sensor, optical sensor, biosensor, chemical sensor) has been playing an important role in disease screening, diagnosis, monitoring, and communication between sensors/systems and people. This Special Issue is focused on sensing-based biomedical communication and intelligent identification (or recognition) of information in signals/images for healthcare applications. The term intelligent identification includes both deep leaning and conventional machine learning approaches. Potentially valuable information or knowledge for healthcare can be obtained through intelligent identification, and delivered to patients/healthcare providers effectively, efficiently and securely. Topics of interests include, but are not limited to, egocentric image analysis for action/activity recognition, motion/gait analysis using wearable sensors, biometrics identification for security, innovative vital signal (e.g., temperature, blood pressure) monitoring, and human–computer interface via invasive or non-invasive sensors.

We strongly encourage submissions exploring the advances in sensor-based signal/image analysis for health monitoring, disease diagnosis, and human-computer interface. Survey papers and reviews are also welcome.

Dr. Wenyan Jia
Prof. Dr. Yi Gao
Prof. Dr. Zhi-Hong Mao
Prof. Dr. Mingui Sun
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
sensing systems
signal or image processing
machine learning/deep learning
intelligent identification or analysis
biomedical communication
disease screening, diagnosis and monitoring
health systems, healthcare, and wellness

Published Papers (12 papers)

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Editorial

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3 pages, 140 KiB

Open AccessEditorial

Editorial for the Special Issue “Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare”

by Wenyan Jia, Yi Gao, Zhi-Hong Mao and Mingui Sun

Sensors 2024, 24(5), 1403; https://doi.org/10.3390/s24051403 - 22 Feb 2024

Viewed by 491

Abstract

The integration of sensor technology in healthcare has become crucial for disease diagnosis and treatment [...] Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

Research

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17 pages, 1978 KiB

Open AccessArticle

Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices

by Ramón A. Félix, Alberto Ochoa-Brust, Walter Mata-López, Rafael Martínez-Peláez, Luis J. Mena and Laura L. Valdez-Velázquez

Sensors 2023, 23(21), 8796; https://doi.org/10.3390/s23218796 - 28 Oct 2023

Cited by 1 | Viewed by 1415

Abstract

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm’s performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal’s isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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15 pages, 1969 KiB

Open AccessArticle

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

by Atieh Khodadadi, Nima Ghanbari Bousejin, Soheila Molaei, Vinod Kumar Chauhan, Tingting Zhu and David A. Clifton

Sensors 2023, 23(14), 6571; https://doi.org/10.3390/s23146571 - 21 Jul 2023

Cited by 2 | Viewed by 1514

Abstract

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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16 pages, 1542 KiB

Open AccessArticle

TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation

by Zidi Shi, Yu Li, Hua Zou and Xuedong Zhang

Sensors 2023, 23(10), 4897; https://doi.org/10.3390/s23104897 - 19 May 2023

Cited by 3 | Viewed by 1481

Abstract

Optical coherence tomography angiography (OCTA) provides a detailed visualization of the vascular system to aid in the detection and diagnosis of ophthalmic disease. However, accurately extracting microvascular details from OCTA images remains a challenging task due to the limitations of pure convolutional networks. We propose a novel end-to-end transformer-based network architecture called TCU-Net for OCTA retinal vessel segmentation tasks. To address the loss of vascular features of convolutional operations, an efficient cross-fusion transformer module is introduced to replace the original skip connection of U-Net. The transformer module interacts with the encoder’s multiscale vascular features to enrich vascular information and achieve linear computational complexity. Additionally, we design an efficient channel-wise cross attention module to fuse the multiscale features and fine-grained details from the decoding stages, resolving the semantic bias between them and enhancing effective vascular information. This model has been evaluated on the dedicated Retinal OCTA Segmentation (ROSE) dataset. The accuracy values of TCU-Net tested on the ROSE-1 dataset with SVC, DVC, and SVC+DVC are 0.9230, 0.9912, and 0.9042, respectively, and the corresponding AUC values are 0.9512, 0.9823, and 0.9170. For the ROSE-2 dataset, the accuracy and AUC are 0.9454 and 0.8623, respectively. The experiments demonstrate that TCU-Net outperforms state-of-the-art approaches regarding vessel segmentation performance and robustness. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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25 pages, 2227 KiB

Open AccessArticle

Electrical Bioimpedance Analysis for Evaluating the Effect of Pelotherapy on the Human Skin: Methodology and Experiments

by Margus Metshein, Varje-Riin Tuulik, Viiu Tuulik, Monika Kumm, Mart Min and Paul Annus

Sensors 2023, 23(9), 4251; https://doi.org/10.3390/s23094251 - 25 Apr 2023

Cited by 2 | Viewed by 1556

Abstract

Background: Pelotherapy is the traditional procedure of applying curative muds on the skin’s surface—shown to have a positive effect on the human body and cure illnesses. The effect of pelotherapy is complex, functioning through several mechanisms, and depends on the skin’s functional condition. The current research objective was to develop a methodology and electrodes to assess the passage of the chemical and biologically active compounds of curative mud through human skin by performing electrical bioimpedance (EBI) analysis. Methods: The methodology included local area mud pack and simultaneous tap water compress application on the forearms with the comparison to the measurements of the dry skin. A custom-designed small-area gold-plated electrode on a rigid printed circuit board, in a tetrapolar configuration, was designed. A pilot study experiment with ten volunteers was performed. Results: Our results indicated the presence of an effect of pelotherapy, manifested by the varying electrical properties of the skin. Distinguishable difference in the measured real part of impedance (R) emerged, showing a very strong correlation between the dry and tap-water-treated skin (r = 0.941), while a poor correlation between the dry and mud-pack-treated skin (r = 0.166) appeared. The findings emerged exclusively in the frequency interval of 10 kHz …1 MHz and only for R. Conclusions: EBI provides a promising tool for monitoring the variations in the electrical properties of the skin, including the skin barrier. We foresee developing smart devices for promoting the exploitation of spa therapies. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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18 pages, 11131 KiB

Open AccessArticle

Application of Feedforward and Recurrent Neural Networks for Fusion of Data from Radar and Depth Sensors Applied for Healthcare-Oriented Characterisation of Persons’ Gait

by Paweł Mazurek

Sensors 2023, 23(3), 1457; https://doi.org/10.3390/s23031457 - 28 Jan 2023

Cited by 1 | Viewed by 1216

Abstract

In this paper, the useability of feedforward and recurrent neural networks for fusion of data from impulse-radar sensors and depth sensors, in the context of healthcare-oriented monitoring of elderly persons, is investigated. Two methods of data fusion are considered, viz., one based on a multilayer perceptron and one based on a nonlinear autoregressive network with exogenous inputs. These two methods are compared with a reference method with respect to their capacity for decreasing the uncertainty of estimation of a monitored person’s position and uncertainty of estimation of several parameters enabling medical personnel to make useful inferences on the health condition of that person, viz., the number of turns made during walking, the travelled distance, and the mean walking speed. Both artificial neural networks were trained on the synthetic data. The numerical experiments show the superiority of the method based on a nonlinear autoregressive network with exogenous inputs. This may be explained by the fact that for this type of network, the prediction of the person’s position at each time instant is based on the position of that person at the previous time instants. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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17 pages, 11586 KiB

Open AccessArticle

A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue

by Tian Mou, Jianwen Liang, Trung Nghia Vu, Mu Tian and Yi Gao

Sensors 2023, 23(3), 1432; https://doi.org/10.3390/s23031432 - 28 Jan 2023

Cited by 2 | Viewed by 1745

Abstract

The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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16 pages, 803 KiB

Open AccessArticle

Identifying Biomarkers for Accurate Detection of Stress

by Kiran Jambhale, Smridhi Mahajan, Benjamin Rieland, Nilanjan Banerjee, Abhijit Dutt, Sai Praveen Kadiyala and Ramana Vinjamuri

Sensors 2022, 22(22), 8703; https://doi.org/10.3390/s22228703 - 11 Nov 2022

Cited by 4 | Viewed by 1547

Abstract

Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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13 pages, 5819 KiB

Open AccessArticle

Control of Brushless Direct-Current Motors Using Bioelectric EMG Signals

by Sebastian Glowinski, Sebastian Pecolt, Andrzej Błażejewski and Bartłomiej Młyński

Sensors 2022, 22(18), 6829; https://doi.org/10.3390/s22186829 - 9 Sep 2022

Cited by 5 | Viewed by 1859

Abstract

(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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10 pages, 913 KiB

Open AccessArticle

Intra-Rater Reliability of Shear Wave Elastography for the Quantification of Respiratory Muscles in Adolescent Athletes

by Małgorzata Pałac and Paweł Linek

Sensors 2022, 22(17), 6622; https://doi.org/10.3390/s22176622 - 1 Sep 2022

Cited by 4 | Viewed by 1512

Abstract

The aim of this study was to assess the intra-rater reliability and agreement of diaphragm and intercostal muscle elasticity and thickness during tidal breathing. The diaphragm and intercostal muscle parameters were measured using shear wave elastography in adolescent athletes. To calculate intra-rater reliability, intraclass correlation coefficient (ICC) and Bland–Altman statistics were used. The reliability/agreement for one-day both muscle measurements (regardless of probe orientation) were at least moderate. During the seven-day interval between measurements, the reliability of a single measurement depended on the measured parameter, transducer orientation, respiratory phase, and muscle. Excellent reliability was found for diaphragm shear modulus at the peak of tidal expiration in transverse probe position (ICC_3.1 = 0.91–0.96; ICC₃.₂ = 0.95), and from poor to excellent reliability for the intercostal muscle thickness at the peak of tidal inspiration with the longitudinal probe position (ICC_3.1 = 0.26–0.95; ICC₃.₂ = 0.15). The overall reliability/agreement of the analysed data was higher for the diaphragm measurements (than the intercostal muscles) regardless of the respiratory phase and probe position. It is difficult to identify a more appropriate probe position to examine these muscles. The shear modulus/thickness of the diaphragm and intercostal muscles demonstrated good reliability/agreement so this appears to be a promising technique for their examination in athletes. Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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Review

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18 pages, 983 KiB

Open AccessReview

Intelligent Health: Progress and Benefit of Artificial Intelligence in Sensing-Based Monitoring and Disease Diagnosis

by Gabriela Palavicini

Sensors 2023, 23(22), 9053; https://doi.org/10.3390/s23229053 - 8 Nov 2023

Cited by 1 | Viewed by 1839

Abstract

Technology has progressed and allows people to go further in multiple fields related to social issues. Medicine cannot be the exception, especially nowadays, when the COVID-19 pandemic has accelerated the use of technology to continue living meaningfully, but mainly in giving consideration to people who remain confined at home with health issues. Our research question is: how can artificial intelligence (AI) translated into technological devices be used to identify health issues, improve people’s health, or prevent severe patient damage? Our work hypothesis is that technology has improved so much during the last decades that Medicine cannot remain apart from this progress. It must integrate technology into treatments so proper communication between intelligent devices and human bodies could better prevent health issues and even correct those already manifested. Consequently, we will answer: what has been the progress of Medicine using intelligent sensor-based devices? Which of those devices are the most used in medical practices? Which is the most benefited population, and what do physicians currently use this technology for? Could sensor-based monitoring and disease diagnosis represent a difference in how the medical praxis takes place nowadays, favouring prevention as opposed to healing? Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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21 pages, 1757 KiB

Open AccessReview

A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

by Qi An, Saifur Rahman, Jingwen Zhou and James Jin Kang

Sensors 2023, 23(9), 4178; https://doi.org/10.3390/s23094178 - 22 Apr 2023

Cited by 30 | Viewed by 7233

Abstract

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets). Full article

(This article belongs to the Special Issue Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare)

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