Special Issue "Biosignal Processing"

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

Deadline for manuscript submissions: closed (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 550 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 (4 papers)

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Research

Open AccessArticle Feature Extraction of Shoulder Joint’s Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance
Bioengineering 2019, 6(1), 2; https://doi.org/10.3390/bioengineering6010002
Received: 16 November 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 24 December 2018
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Abstract
Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals’ daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, [...] Read more.
Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals’ daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals’ features of the shoulder joint, which is the most complex joint in the human’s body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300–400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities continuously. Full article
(This article belongs to the Special Issue Biosignal Processing)
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Open AccessArticle Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning
Bioengineering 2018, 5(4), 107; https://doi.org/10.3390/bioengineering5040107
Received: 8 November 2018 / Revised: 26 November 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
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Abstract
Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of [...] Read more.
Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of the bone condition. Therefore, the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient’s tibia and an electronic stethoscope to acquire the impulse responses. The signals are processed as mel frequency cepstrum coefficients and passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses. Following some discussions of the mechanism and procedure, this paper details the signal acquisition using the stethoscope and the subsequent signal processing and the statistical machine learning algorithm. Pilot testing with 12 patients achieved over 80% sensitivity with a false positive rate below 30% and accuracies in the region of 70%. An extended dataset of 110 patients achieved an error rate of 30% with some room for improvement in the algorithm. By using common clinical apparatus and strategic machine learning, this method might be suitable as a large population screening test for the early diagnosis of osteoporosis, thus avoiding secondary complications. Full article
(This article belongs to the Special Issue Biosignal Processing)
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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 [...] Read more.
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 [...] Read more.
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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Graphical abstract

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