Special Issue "Computer Methods in Bio-mechanics and Biomedical Engineering"

Deadline: 30 October 2019 (closed)
Guest Editors
Professor Lulu Wang, Hefei University of Technology
Professor Xiaoning Jiang, North Carolina State University
Professor Lindong Yu, Hefei University of Technology
Professor Linxia Gu, University of Nebraska-Lincoln


This special issue focuses on the implementation of various engineering principles in the conception, design, development, analysis and operation of biomedical and biotechnological systems and applications. The special issue aims to promote solutions of excellence for biomedical data and establishes links among engineers, researchers, and clinicians. 

This special issue offers a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to bio-mechanics and biomedical technologies, including, but not limited to:

1.Computational Modeling in Biomedical Applications
2.Computer Aided Diagnosis, Surgery, Therapy and Treatment
3.Data Processing and Analysis
4.Injury and Damage Bio-mechanics
5.Vibration and Acoustics in Biomedical Applications
6.Biomedical Imaging, Therapy and Tissue Characterization
7.Biomaterials and Tissue: Modelling, Synthesis, Fabrication and Characterization
8.Biomedical Devices
9.Dynamics and Control of Biomechanical Systems
10.Clinical Applications of Bioengineering
11.Musculoskeletal and Sports Bio-mechanics
12.Sensors and Actuators
13.Robotics, Rehabilitation
14.Data Processing and Analysis
15.Virtual Reality
16.Visual Data Mining and Knowledge Discovery
17.Software Development for Bio-mechanics and Biomedical Engineering

Biomedical imaging; Biomedical Devices; CAD; Biosensors;

Published Papers
  • Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG
  • Abstract The grading of hypoxic-ischemic encephalopathy (HIE) contributes to the clinical decision making for neonates with HIE. In this paper, an automated grading method based on electroencephalogram (EEG) data is proposed to describe the severity of HIE infants, namely mild asphyxia, moderate asphyxia and severe asphyxia. The automated grading method is based on a multi-class support vector machine (SVM) classifier, and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch… More
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