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Multi-Sensor Fusion of Biomedical Data: Application to Diagnosis and Treatment

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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 (31 March 2021) | Viewed by 23529

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Special Issue Editor

Dr. Evangelia I. Zacharaki

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Guest Editor

Department of Electrical and Computer Engineering, University of Patras, Rio, Greece
Interests: medical image registration; image segmentation; multi-sensor data fusion; artificial intelligence in medicine; virtual physiological human

Special Issue Information

Dear Colleagues,

In the last decade, the use of computational and artificial intelligence has played a growing role in the exploitation of biomedical data from multiple sensors, improving medical diagnostics and enabling smarter healthcare solutions. Extensive research has been dedicated to the effective use of multisensorial information, such as physical parameters of body activity (heart rate, neural activity, blood pressure, respiration), anatomical or functional information obtained by 2D/3D/4D imaging (CT, MRI, fMRI, PET, SPECT, ultrasound), and biochemical data (ions, metabolites, proteins), aiming to support the real-time or offline personalized decision-making process. The current Special Issue seeks to present and highlight emerging biomedical applications that use advanced sensor fusion strategies to reduce decision error probability and increase reliability. We encourage submissions that focus on any of the following aspects:

Innovative data fusion strategies, such as Bayesian methods, graphical models, abductive reasoning, probabilistic data association and state estimation techniques, that promote predictive and preventive medical solutions;
State-of-the-art frameworks employing signal or medical image analysis algorithms, as well as machine/deep learning techniques for extraction of biomarkers from multiple sensors that carry prognostic information on disease or therapy;
Contributions that address the main challenges during data fusion attributed to the high dimensionality, heterogeneity, anatomical variability, noise, sparsity, and missing values in the data.

Sensor materials and technologies, hardware design for data integration, multimodal interfaces, embedded sensor fusion systems, mobile platform applications, and data management techniques for retrieval, storage, and transfer are not within the scope of this Special Issue.

Dr. Evangelia I. Zacharaki
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

Sensor fusion
Biomedical data integration
Medical image analysis
Biosignal processing
Machine/deep learning
Probabilistic label fusion
Sensors in healthcare
Decision support system
Computer-aided diagnosis

Published Papers (5 papers)

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Research

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10 pages, 524 KiB

Open AccessCommunication

Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

by Jan Ubbo van Baardewijk, Sarthak Agarwal, Alex S. Cornelissen, Marloes J. A. Joosen, Jiska Kentrop, Carolina Varon and Anne-Marie Brouwer

Sensors 2021, 21(11), 3616; https://doi.org/10.3390/s21113616 - 22 May 2021

Cited by 3 | Viewed by 2636

Abstract

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion of Biomedical Data: Application to Diagnosis and Treatment)

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19 pages, 3028 KiB

Open AccessArticle

Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning

by Georgios Giarmatzis, Evangelia I. Zacharaki and Konstantinos Moustakas

Sensors 2020, 20(23), 6933; https://doi.org/10.3390/s20236933 - 4 Dec 2020

Cited by 26 | Viewed by 6922

Abstract

Conventional biomechanical modelling approaches involve the solution of large systems of equations that encode the complex mathematical representation of human motion and skeletal structure. To improve stability and computational speed, being a common bottleneck in current approaches, we apply machine learning to train surrogate models and to predict in near real-time, previously calculated medial and lateral knee contact forces (KCFs) of 54 young and elderly participants during treadmill walking in a speed range of 3 to 7 km/h. Predictions are obtained by fusing optical motion capture and musculoskeletal modeling-derived kinematic and force variables, into regression models using artificial neural networks (ANNs) and support vector regression (SVR). Training schemes included either data from all subjects (LeaveTrialsOut) or only from a portion of them (LeaveSubjectsOut), in combination with inclusion of ground reaction forces (GRFs) in the dataset or not. Results identify ANNs as the best-performing predictor of KCFs, both in terms of Pearson R (0.89–0.98 for LeaveTrialsOut and 0.45–0.85 for LeaveSubjectsOut) and percentage normalized root mean square error (0.67–2.35 for LeaveTrialsOut and 1.6–5.39 for LeaveSubjectsOut). When GRFs were omitted from the dataset, no substantial decrease in prediction power of both models was observed. Our findings showcase the strength of ANNs to predict simultaneously multi-component KCF during walking at different speeds—even in the absence of GRFs—particularly applicable in real-time applications that make use of knee loading conditions to guide and treat patients. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion of Biomedical Data: Application to Diagnosis and Treatment)

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15 pages, 11518 KiB

Open AccessArticle

Groupwise Image Alignment via Self Quotient Images

by Nefeli Lamprinou, Nikolaos Nikolikos and Emmanouil Z. Psarakis

Sensors 2020, 20(8), 2325; https://doi.org/10.3390/s20082325 - 19 Apr 2020

Cited by 1 | Viewed by 2131

Abstract

Compared with pairwise registration, the groupwise one is capable of handling a large-scale population of images simultaneously in an unbiased way. In this work we improve upon the state-of-the-art pixel-level, Least-Squares (LS)-based groupwise image registration methods. Specifically, the registration technique is properly adapted by the use of Self Quotient Images (SQI) in order to become capable for solving the groupwise registration of photometrically distorted, partially occluded as well as unimodal and multimodal images. Moreover, the proposed groupwise technique is linear to the cardinality of the image set and thus it can be used for the successful solution of the problem on large image sets with low complexity. From the application of the proposed technique on a series of experiments for the groupwise registration of photometrically and geometrically distorted, partially occluded faces as well as unimodal and multimodal magnetic resonance image sets and its comparison with the Lucas–Kanade Entropy (LKE) algorithm, the obtained results look very promising, in terms of alignment quality, using as figures of merit the mean Peak Signal to Noise Ratio (

m_{P S N R}

) and mean Structural Similarity (

m_{S S I M}

), and computational cost. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion of Biomedical Data: Application to Diagnosis and Treatment)

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17 pages, 2533 KiB

Open AccessArticle

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

by Luis de Santiago, M. Ortiz del Castillo, Elena Garcia-Martin, María Jesús Rodrigo, Eva M. Sánchez Morla, Carlo Cavaliere, Beatriz Cordón, Juan Manuel Miguel, Almudena López and Luciano Boquete

Sensors 2020, 20(1), 7; https://doi.org/10.3390/s20010007 - 18 Dec 2019

Cited by 7 | Viewed by 3378

Abstract

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion of Biomedical Data: Application to Diagnosis and Treatment)

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Review

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21 pages, 3688 KiB

Open AccessFeature PaperReview

Application of Modern Multi-Sensor Holter in Diagnosis and Treatment

by Erik Vavrinsky, Jan Subjak, Martin Donoval, Alexandra Wagner, Tomas Zavodnik and Helena Svobodova

Sensors 2020, 20(9), 2663; https://doi.org/10.3390/s20092663 - 7 May 2020

Cited by 24 | Viewed by 6797

Abstract

Modern Holter devices are very trendy tools used in medicine, research, or sport. They monitor a variety of human physiological or pathophysiological signals. Nowadays, Holter devices have been developing very fast. New innovative products come to the market every day. They have become smaller, smarter, cheaper, have ultra-low power consumption, do not limit everyday life, and allow comfortable measurements of humans to be accomplished in a familiar and natural environment, without extreme fear from doctors. People can be informed about their health and 24/7 monitoring can sometimes easily detect specific diseases, which are normally passed during routine ambulance operation. However, there is a problem with the reliability, quality, and quantity of the collected data. In normal life, there may be a loss of signal recording, abnormal growth of artifacts, etc. At this point, there is a need for multiple sensors capturing single variables in parallel by different sensing methods to complement these methods and diminish the level of artifacts. We can also sense multiple different signals that are complementary and give us a coherent picture. In this article, we describe actual interesting multi-sensor principles on the grounds of our own long-year experiences and many experiments. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion of Biomedical Data: Application to Diagnosis and Treatment)

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