The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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Biomedical signals have been widely used in the clinical setting for diagnostics, guiding therapy, patient monitoring, disease prevention, and risk assessment. Processing and interpreting these data provides a multifaceted set of challenges, including coping with nonstationary behaviors ubiquitously present in the time course of biomedical variables, separating sources from a mixture of signals typically observed from the body surface, detecting the often weak coupling between physiological processes in noisy measurement environments, extracting and classifying significant dynamical features, modeling the underlying physiological systems, understanding the cause–effect relation between interacting subsystems, and converting methodological parameters into relevant information able to drive the clinical process and produce a measurable impact on the health care system.
The special issue of Proceedings of the IEEE in Feb. 2016 comprises 12 reviews that address the aforementioned challenges in applicative biomedical contexts by emphasizing the relevance of the methodological problem, the clinical importance of extracted information, and the possibility for future technological developments.
The first three contributions deal explicitly with a fundamental issue in the analysis and interpretation of biomedical signals: the inherently nonstationary nature of biological processes especially when recorded over a long time.
Given the multitude and complexity of processes involved in and governing the human body, integrated analysis of multidimensional data is of utmost importance to advance our understanding of integrated human physiology and pathological variations. Porta and Faes review the Wiener–Granger causality paradigm with the specific aim of assessing causal interactions among components forming a network and working according to the principles of integration and segregation.
Identifying independent sources from recordings of activity made with devices sensing the collective behavior is a major challenge in interpreting multidimensional data. Zhou et al. discuss component analysis approaches to tackle this matter.
In terms of signal processing, feature extraction and classification are the main tasks involved in brain–computer interfaces. Li et al. review multimodal approaches to improve target detection and control of these systems.
Aside from brain signals, understanding and interpreting signals from end-organs, i.e., skeletal muscles (called electromyograms) enables the design of prosthetic devices that can be controlled by amputees. Recent advances in the characterization of human motor units from surface electromyograms, using blind source separation techniques to identify the discharge times of individual motor units, are summarized by Farina and Holobar. Technologies and signal processing algorithms for recording and decoding for neural prostheses that exploit peripheral nerve signals and electrocorticograms (ECoG) to interpret human intent and control prosthetic arms are reviewed by Warren et al.
Laguna et al. discuss more recent signal processing advances in capturing subtle variations in the ECG that have been associated with cardiac death. Baumert et al. review quantitative electrogram-based methods for guiding catheter ablation in atrial fibrillation with a special focus on how signal processing can be fruitfully exploited to improve practical clinically relevant procedures. Hosokawa and Sunagawa have developed a closed-loop neuromodulation technology for baroreflex regulation that mimics natural blood pressure control.
Dealing with the issue of “big data” in the clinical environment and its support to the decision making process, machine learning, and decision support in critical care is reviewed by Johnson et al., focusing on issues of data corruption, compartmentalization, and complexity in regard to preprocessing of large volumes of biomedical signals from critically ill patients.
The editors hope that the topics covered in this issue highlight relevant contemporary challenges in the biomedical field that, when successfully tacked, might considerably advance our understanding of physiological processes, our capability of treating pathological states, and our potential for interacting with human beings and the external environment.
Mathias Baumert, Alberto Porta, Andrzej Cichocki. Proceedings of the IEEE, Vol. 104, No. 2, February 2016, 220-222
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