Special Issue "Biosignal Processing"

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: 30 November 2018

Special Issue Editor

Guest Editor
Prof. Dr. Yuling Yan

Department of Bioengineering, Santa Clara University, CA, USA
Website | E-Mail
Interests: Biosignal processing; Bioimaging; AI-assisted disease classification; Laryngeal dynamics and physiology; Biomedical visualization; Brain-Computer Interface

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to advance new approaches to analyze 1D~3D datasets to reveal mechanisms of biological phenomena and identify indicators of diseased states.

In particular, we want to highlight new approaches for bio-signal/bio-image analysis with an emphasis on the monitoring and early detection of diseases.

Recent advances in artificial intelligence (AI) and machine learning (ML) have the potential to transform healthcare practices from the traditional “one-size-fits-all” to targeted and personalized treatments. AI approaches to medicine require new methods to manage and analyze large biomedical datasets, for example, genomic, X-ray/CT images, and dynamic multivariate physiological readouts including electrocardiographic, electroencephalographic and fMRI data.

With this mission, we invite you to contribute original research papers or comprehensive reviews to this Special Issue on the “Biosignal Processing”. Your contributions will help to improve and advance methodologies to process and analyze libraries of biomedical data that will generate new opportunities in AI to develop approaches and solutions to important medical and biological problems.

Prof. Dr. Yuling Yan
Guest Editor

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 papers will be 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. Bioengineering is an international peer-reviewed open access quarterly 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 300 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.

Published Papers (2 papers)

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Research

Open AccessArticle Breast Cancer Estimate Modeling via PDE Thermal Analysis Algorithms
Bioengineering 2018, 5(4), 98; https://doi.org/10.3390/bioengineering5040098
Received: 11 August 2018 / Revised: 14 October 2018 / Accepted: 23 October 2018 / Published: 5 November 2018
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Abstract
The significance of this study lies in the importance of (1) nondestructive testing in defect studies and (2) securing the reliability of breast cancer prediction through thermal analysis in nondestructive testing. Most nondestructive tests have negative effects on the human body. Moreover, the
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The significance of this study lies in the importance of (1) nondestructive testing in defect studies and (2) securing the reliability of breast cancer prediction through thermal analysis in nondestructive testing. Most nondestructive tests have negative effects on the human body. Moreover, the precision and accuracy of such tests are poor. This study analyzes these drawbacks and increases the reliability of such methods. A theoretical model was constructed, by which simulated inner breast tissue was observed in a nondestructive way through thermal analysis, and the presence and extent of simulated breast cancer were estimated based on the thermal observations. Herein, we studied the medical diagnosis of breast cancer by creating a theoretical environment that simulated breast cancer in a real-world setting; the model used two-dimensional modeling and partial differential equation (PDE) thermal analysis. Our theoretical analysis, based on partial differential equations, allowed us to demonstrate that non-wounding defect detection is possible and, in many ways, preferable. The main contribution of this paper lies in studying long-term estimates. In addition, the model in this study can be extended to predict breast cancer through pure heat and can also be used for various other cancer and tumor analyses in the human body. Full article
(This article belongs to the Special Issue Biosignal Processing)
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Open AccessArticle Evaluation of a Computer-Aided Diagnosis System in the Classification of Lesions in Breast Strain Elastography Imaging
Bioengineering 2018, 5(3), 62; https://doi.org/10.3390/bioengineering5030062
Received: 12 June 2018 / Revised: 27 July 2018 / Accepted: 6 August 2018 / Published: 9 August 2018
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Abstract
Purpose: Evaluation of the performance of a computer-aided diagnosis (CAD) system based on the quantified color distribution in strain elastography imaging to evaluate the malignancy of breast tumors. Methods: The database consisted of 31 malignant and 52 benign lesions. A radiologist who was
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Purpose: Evaluation of the performance of a computer-aided diagnosis (CAD) system based on the quantified color distribution in strain elastography imaging to evaluate the malignancy of breast tumors. Methods: The database consisted of 31 malignant and 52 benign lesions. A radiologist who was blinded to the diagnosis performed the visual analysis of the lesions. After six months with no eye contact on the breast images, the same radiologist and other two radiologists manually drew the contour of the lesions in B-mode ultrasound, which was masked in the elastography image. In order to measure the amount of hard tissue in a lesion, we developed a CAD system able to identify the amount of hard tissue, represented by red color, and quantify its predominance in a lesion, allowing classification as soft, intermediate, or hard. The data obtained with the CAD system were compared with the visual analysis. We calculated the sensitivity, specificity, and area under the curve (AUC) for the classification using the CAD system from the manual delineation of the contour by each radiologist. Results: The performance of the CAD system for the most experienced radiologist achieved sensitivity of 70.97%, specificity of 88.46%, and AUC of 0.853. The system presented better performance compared with his visual diagnosis, whose sensitivity, specificity, and AUC were 61.29%, 88.46%, and 0.829, respectively. The system obtained sensitivity, specificity, and AUC of 67.70%, 84.60%, and 0.783, respectively, for images segmented by Radiologist 2, and 51.60%, 92.30%, and 0.771, respectively, for those segmented by the Resident. The intra-class correlation coefficient was 0.748. The inter-observer agreement of the CAD system with the different contours was good in all comparisons. Conclusions: The proposed CAD system can improve the radiologist performance for classifying breast masses, with excellent inter-observer agreement. It could be a promising tool for clinical use. Full article
(This article belongs to the Special Issue Biosignal Processing)
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