Application of Neural Engineering in Sleep Research and Medicine

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 2668

Special Issue Editors


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Guest Editor
Scuola IMT Alti Studi Lucca, Lucca, Italy
Interests: processing and analysis of biomedical signals; brain imaging methodologies; EEG brain activity; computational/mathematical models; optimization algorithms

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Guest Editor
Istituto di Fisiologia Clinica del CNR, Pisa, Italy
Interests: analysis of biomedical signals; artificial intelligence; neuroscience; connected health; psychophysiology; mathematical modeling; biostatistics

Special Issue Information

Dear Colleagues,

Sleep is essential for a multitude of biological processes including cognitive, immune, endocrine, metabolic, and cardiovascular functions. Uncovering the precise physiological mechanisms underlying these processes as well as their potential impairment under pathological conditions represents a fundamental step in the definition of novel risk factors, early diagnosis protocols, and strategies for maintaining, restoring, and improving our mental and physical health.

Studying and monitoring sleep requires the simultaneous acquisition of multiple bio-signals related to both the central and the peripheral nervous system. If this can limit the applicability in daily life conditions, it also implies a considerable effort in terms of signal preprocessing and advanced analysis. In the past, sleep medicine and research largely addressed this necessity using visual scoring, especially for sleep staging, and manual pattern identification.

Considering the latest achievements in the field of biomedical imaging, signal processing, and machine learning, this Special Issue aims to collect original research papers and comprehensive reviews focusing on the most recent applications and studies of neural engineering for sleep research and medicine. Topics of interest include, but are not limited to, the following:

  • Signal processing for sleep data analysis.
  • Machine learning based approaches for sleep data analysis and sleep staging.
  • Automated methods for sleep staging, event detections and pattern recognition.
  • Multimodal neuroimaging for sleep study.
  • Non-invasive systems for sleep monitoring in both clinical and daily life settings.
  • Techniques and technologies for influencing and manipulating sleep.

Dr. Monica Betta
Dr. Marco Laurino
Guest Editors

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Keywords

  • sleep
  • signal processing
  • computational neuroscience
  • machine learning
  • artificial intelligence
  • PSG
  • EEG
  • neuroimaging
  • wearable systems

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Published Papers (1 paper)

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Research

32 pages, 1966 KiB  
Article
Remote Monitoring of Sympathovagal Imbalance During Sleep and Its Implications in Cardiovascular Risk Assessment: A Systematic Review
by Valerie A. A. van Es, Ignace L. J. de Lathauwer, Hareld M. C. Kemps, Giacomo Handjaras and Monica Betta
Bioengineering 2024, 11(10), 1045; https://doi.org/10.3390/bioengineering11101045 - 19 Oct 2024
Cited by 1 | Viewed by 1948
Abstract
Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and [...] Read more.
Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography. Full article
(This article belongs to the Special Issue Application of Neural Engineering in Sleep Research and Medicine)
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