Special Issue "Advanced signal acquisition and processing for Internet of Medical Things"

Submission Deadline: 30 June 2021 (closed)
Guest Editors
Dr. Junxin Chen, University of Macau, Macau.
Dr. Mohammad Tanveer, Indian Institute of Technology Indore, India.
Dr. Shancang Li, University of the West of England, UK.
Dr. Pengfei Hu, UC Davis, CA, USA.


The Internet of Medical Things (IoMT) is an amalgamation that represents the medical devices and software applications connected to a health care provider through networking technologies. With the power of collecting, transmitting and analyzing multi-modal health data, IoMT applications are playing increasing important roles for early detection and continuous monitoring of chronic illnesses. It can provide improved healthcare in terms of patient experience, diagnosis and treatments, disease management, management of pharmaceuticals, and so on. The IoMT is written as “transforming healthcare” technique, with the goal to make healthcare more convenient for patients and efficient for providers. With the continuing progress in information technologies, the applications of IoMT are expected to expand.


While offering enormous healthcare benefits, the IoMT is facing various kinds of challenges. On the one hand, continuous and real-time monitoring of physiological parameter is the basis task of IoMT, the energy restriction of the sensors prompts the requirement of lightweight signal acquisition and data compression method. On the other hand, how to extract medical/healthy knowledge from the collected physiological data is another challenge. The data analysis may be performed in the wearable device and also may be implemented in the remote data center of the hospital. Different kinds of applications require distinct data analysis approaches, for example, data mining technique directly from the compressed physiological signals without signal reconstruction attracts increasing attention in recent years. In addition, considering the privacy concerns, the collected data of IoMT should be acquired, transmitted and analyzed in a secure manner.


This special issue aims to collect advanced signal acquisition and processing techniques for IoMT.

IoT system architectures for healthcare
Lightweight signal acquisition for IoMT
Intelligent sensing of IoMT
Information fusion in IoMT
Medical data transmission, cleaning and integration
Artificial intelligence for medical data mining
Artificial intelligence for medical image processing
Multi-modal medical data processing
Information encryption and security in IoMT
Trustable signal processing for IoMT

Published Papers
  • Modeling of Heart Rate Variability Using Time-Frequency Representations
  • Abstract The heart rate variability signal is highly correlated with the respiration even at high workload exercise. It is also known that this phenomenon still exists during increasing exercise. In the current study, we managed to model this correlation during increasing exercise using the time varying integral pulse frequency modulation (TVIPFM) model that relates the mechanical modulation (MM) to the respiration and the cardiac rhythm. This modulation of the autonomic nervous system (ANS) is able to simultaneously decrease sympathetic and increase parasympathetic activity. The TVIPFM model takes into consideration the effect of the increasing exercise test, where the effect of a… More
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  • Reversible Data Hiding Based on Varying Radix Numeral System
  • Abstract A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article. Using varying radix, variable sum of data may be embedded in various pixels of images. This scheme is made adaptive using the correlation of the neighboring pixels. Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image. A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image. The size of the embedded data depends on the statistics of the pixel distribution in… More
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  • Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study
  • Abstract Left ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based… More
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