Objectives

"Data are the direct record of an event, such as a natural phenomenon, a rocket launch, or engineering processes. The record can be taken by our eyes, ears, electronic sensors, or mechanical devices. We analyze the data, detect signals and make decisions. Thus, data provide connections between reality and us, and data analysis enables us to understand reality and to discover its underlying driving mechanisms. In this sense, data analysis is very different from data processing. The former emphasizes detailed decomposition and examination of data to extract underlying or hidden regularities ultimately leading to understanding of physical phenomena, while the latter often relies on transforming the data by established algorithms and machines to extract predefined feature of interest, remove the noise or output values of mathematical parameters. Adaptive data analysis takes a further step in interpretation of reality, by being driven by the content and properties of the data themselves, potentially providing deeper insights into the mechanisms underlying the phenomena under study." http://www.worldscientific.com/page/adsaa/aims-scope

In this special issue of the journal Advances in Data Science and Adaptive Analysis (ADSAA-BIOMED2017), we invite authors to submit original research articles or reviews disseminating the use of adaptive analysis applied to biomedical data (e.g., biological signals, such as EMG, ECG, EEG, neural activity, medical images, biomechanical data). We also encourage the submission of work dealing with the use of adaptive analysis in thematic areas such as burgeoning datasets (Big Data) emerging as a result of wearable wireless systems, etc.

We have the following objectives:

• to identify and illustrate new applications of adaptive analysis of distinct types of biomedical data.
• to publish recent experimental, theoretical, and numerical results related to the use of adaptive analysis in the context of biomedical data, as well as comprehensive review papers.

Empirical Mode Decomposition (EMD), Bayesian methods, Kalman filtering, and machine learning techniques may be considered adaptive analysis.

There is no page charge for this journal.

Potential audience

Researchers who work in the field of Biomedical Engineering, specifically in applied biomedical signal analysis.

Important dates

Submission deadline

October 31, 2017

Expected review completion date

February 2018

Expected publication date

April 2018

Submission instructions

Complete instructions for submission are available here.

Papers should be submitted in PDF format through the EasyChair for ADSAA-BIOMED2017 Platform.

Contact

Enquiries should be addressed to Prof. Adriano Andrade (leading guest editor): adsaabiomed2017@easychair.org

Accepted and published papers will be indexed in

• Academic OneFile
• Baidu
• CNKI
• CnpLINKer
• Compendex
• Computer & Information Systems Abstracts
• DBLP Computer Science Bibliography
• Ebsco Discovery Service
• EBSCO Electronic Journal Service (EJS)
• Emerging Sources Citation Index (ESCI)
• ExLibris Primo Central
• Google Scholar
• J-Gate
• Mathematical Reviews^® (MR)
• Naver
• National Science and Technology Libraries (NSTL)
• OCLC WorldCat^®
• Scopus
• The Summon^® Service

See also

SJR impact history

RG impact history