The work in this paper provides evidence for temporal brain dynamics during resting state through dynamic multi-layer community detection which enables us to better understand the behavior of different subnetworks.
Asthma is not very easy to be correctly diagnosed by physicians where asthma is considered a common chronic inflammatory disease. Distinguishing of cough sound can be used to diagnose asthma. The use of signal processing techniques of cough sound for detecting asthma will be addressed in this paper to help the diagnosis of asthma by physicians. Since cough sounds are non-stationary and are stochastic signals inherently, time-frequency transform techniques are used to deal with such signals. Time-frequency analyses are performed to show in a comprehensive approach the characteristics of the cough sound signal. Time-frequency analysis techniques, specifically Wigner distribution in addition to wavelet transform to analyse cough signals, are used in this paper. The features extracted from the time-frequency domain of the cough sound are used as inputs to the asthma and non-asthma classifier. The results of the proposed algorithm are competitive to the best existing algorithms in the literature.
<span>The latest advances and trends in information technology and communication have a vital role in healthcare industries. </span><span>Theses advancements led to the Internet of Medical Things (IoMT) which provides a continuous, remote and real-time monitoring of patients. The IoMT </span><span>architectures still face many challenges related to the bandwidth, communication protocols, big data and data volume, flexibility, reliability, data management, data acquisition, data processing and analytics availability, cost effectiveness, data security and privacy, and energy efficiency. The goal of this paper is to find </span><span>feasible </span><span>solutions to enhance the healthcare living facilities using remote health monitoring (RHM) and IoMT. In addition, the enhancement of the prevention, prognosis, diagnosis and treatment abilities using IoMT and RHM is also discussed. </span><span>A case study of monitoring the vital signs of diabetic patients using real-time data processing and IoMT is also presented</span><span>. </span>
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