Multiscale Analysis of Intensive Longitudinal Biomedical Signals and Its Clinical Applications

Recent advances in wearable and/or biomedical sensing technologies have made it possible to record very long-term, continuous biomedical signals, referred to as biomedical intensive longitudinal data (ILD). To link ILD to clinical applications, such as personalized healthcare and disease prevention,...

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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 104; no. 2; pp. 242 - 261
Main Authors: Nakamura, Toru, Kiyono, Ken, Wendt, Herwig, Abry, Patrice, Yamamoto, Yoshiharu
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Artificial Intelligence
Biomedical monitoring
Biomedical signal processing
Clinical diagnosis
Complex systems
Computer Science
Computer Vision and Pattern Recognition
Diseases
Graphics
Heart rate
Heart rate variability
Image Processing
Intermittency
Links
Multiscale analysis
Patients
Personalized
Physiology
Signal and Image Processing
Spontaneous
complex biosignals
multiscale fluctuations
psychiatric disorder
heart failure
Autonomic nervous system
heart rate variability
spontaneous physical activity
dynamical disease
Physiology
Clinical diagnosis
Biomedical monitoring
Heart rate variability
Biomedical signal processing
Diseases
ISSN:0018-9219, 1558-2256
Online Access:Get full text
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Summary:Recent advances in wearable and/or biomedical sensing technologies have made it possible to record very long-term, continuous biomedical signals, referred to as biomedical intensive longitudinal data (ILD). To link ILD to clinical applications, such as personalized healthcare and disease prevention, the development of robust and reliable data analysis techniques is considered important. In this review, we introduce multiscale analysis methods for and the applications to two types of intensive longitudinal biomedical signals, heart rate variability (HRV) and spontaneous physical activity (SPA) time series. It has been shown that these ILD have robust characteristics unique to various multiscale complex systems, and some parameters characterizing the multiscale complexity are in fact altered in pathological states, showing potential usability as a new type of ambient diagnostic and/or prognostic tools. For example, parameters characterizing increased intermittency of HRV are found to be potentially useful in detecting abnormality in the state of the autonomic nervous system, in particular the sympathetic hyperactivity, and intermittency parameters of SPA might also be useful in evaluating symptoms of psychiatric patients with depressive as well as manic episodes, all in the daily settings. Therefore, multiscale analysis might be a useful tool to extract information on clinical events occurring at multiple time scales during daily life and the underlying physiological control mechanisms from biomedical ILD.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2015.2491979