Journal of Healthcare Engineering

Big Data Intelligence in Healthcare Applications Based on Physiological Signals


Publishing date
01 Feb 2020
Status
Closed
Submission deadline
11 Oct 2019

Lead Editor
Guest Editors

1The University of British Columbia, Vancouver, Canada

2Nanyang Technological University, Singapore

3Dalian University of Technology, Dalian, China

4Stanford University, Stanford, USA

This issue is now closed for submissions.
More articles will be published in the near future.

Big Data Intelligence in Healthcare Applications Based on Physiological Signals

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Medical data is projected to double in volume every two years. This rapid increase in the generation of physiological data, alongside the development of big data intelligence, has enabled us to extract new insights from massive physiological signals. These include bioelectrical signals (e.g., EEG and ECG), biomagnetic signals (e.g., MRI and CT), biochemical signals (e.g., pressure of oxygen and carbon dioxide in respiration), and bioacoustic signals (e.g., speech and ultrasound).

Applications that utilize big data intelligence in healthcare have the potential to help reduce treatment costs, avoid preventable diseases, and improve quality of life. However, when compared with other types of sensor-based big data, physiological data contains numerous artefacts and large variances in signal quality. Intraindividual variability, data inconsistency, and inhomogeneity also impede its usage in healthcare applications. In addition, how to integrate big data intelligence into clinical practice remains a problem open for discussion. These challenges must be overcome to realize the full potential and benefits that big data intelligence can bring to the realm of healthcare.

The purpose of this special issue is to collect original research articles and review articles that present state-of-the-art research on the latest developments within big data intelligence in healthcare applications, including reviews, algorithms, platforms, and applications.

Potential topics include but are not limited to the following:

  • Machine learning for physiological signal analysis
  • Ensemble learning (including classification and clustering) for physiological signals
  • Transfer learning for physiological signals
  • Reinforcement learning for physiological signals
  • Multimodal data fusion for physiological signals
  • Deep learning for physiological signal processing
  • Design and modelling of intelligent medical systems
  • Cloud computing for physiological signals
  • Signal processing for biomedical big data
  • Machine learning for health informatics and bioinformatics

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 7963497
  • - Research Article

BMIVPOT, a Fully Automated Version of the Intravenous Pole: Simulation, Design, and Evaluation

Abbas Sayed-Kassem | Nancy Kozah | ... | Amira J. Zaylaa
  • Special Issue
  • - Volume 2020
  • - Article ID 2452683
  • - Research Article

Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals

Xiuqing Chen | Hong Zhu | ... | Shoudao Li
  • Special Issue
  • - Volume 2020
  • - Article ID 2389527
  • - Research Article

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

Haigang Li | Qian Zhang | Ming Li
  • Special Issue
  • - Volume 2020
  • - Article ID 3264801
  • - Research Article

Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection

Zhuo Liu | Gerui Zhang | ... | Hong Yuan
  • Special Issue
  • - Volume 2020
  • - Article ID 1051394
  • - Research Article

Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

Jing Yu | Hang Li | Desheng Liu
  • Special Issue
  • - Volume 2020
  • - Article ID 3965961
  • - Research Article

Analysis of Anesthesia Methods in Percutaneous Kyphoplasty for Treatment of Vertebral Compression Fractures

Jie Liu | Lin Wang | ... | Yanjun Zhang
  • Special Issue
  • - Volume 2020
  • - Article ID 6484129
  • - Research Article

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

G. Ramos | J. R. Vaz | ... | H. Gamboa
  • Special Issue
  • - Volume 2019
  • - Article ID 4159676
  • - Research Article

Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis

Qingze Liu | Aiping Liu | ... | Xun Chen
  • Special Issue
  • - Volume 2019
  • - Article ID 8597606
  • - Research Article

DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

Lin Teng | Hang Li | Shahid Karim
  • Special Issue
  • - Volume 2019
  • - Article ID 6320651
  • - Research Article

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Junli Gao | Hongpo Zhang | ... | Zongmin Wang
Journal of Healthcare Engineering
 Journal metrics
Acceptance rate37%
Submission to final decision99 days
Acceptance to publication48 days
CiteScore2.600
Impact Factor1.803
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