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Cited by 22Wireless Body Area NetworksECG Monitoring and AnalysisSparse and Compressive Sensing Techniques

A neural algorithm for the non-uniform and adaptive sampling of biomedical data

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Mesin, Luca·2016·Computers in Biology and Medicine
DOI 10.1016/j.compbiomed.2016.02.004
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Abstract

BACKGROUND AND OBJECTIVE Body sensors are finding increasing applications in the self-monitoring for health-care and in the remote surveillance of sensitive people. The physiological data to be sampled can be non-stationary, with bursts of high amplitude and frequency content providing most information. Such data could be sampled efficiently with a non-uniform schedule that increases the sampling rate only during activity bursts. METHODS A real time and adaptive algorithm is proposed to select the sampling rate, in order to reduce the number of measured samples, but still recording the main information. The algorithm is based on a neural network which predicts the subsequent samples and their uncertainties, requiring a measurement only when the risk of the prediction is larger than a selectable threshold. RESULTS Four examples of application to biomedical data are discussed: electromyogram, electrocardiogram, electroencephalogram, and body acceleration. Sampling rates are reduced under the Nyquist limit, still preserving an accurate representation of the data and of their power spectral densities (PSD). For example, sampling at 60% of the Nyquist frequency, the percentage average rectified errors in estimating the signals are on the order of 10% and the PSD is fairly represented, until the highest frequencies. The method outperforms both uniform sampling and compressive sensing applied to the same data. CONCLUSION The discussed method allows to go beyond Nyquist limit, still preserving the information content of non-stationary biomedical signals. It could find applications in body sensor networks to lower the number of wireless communications (saving sensor power) and to reduce the occupation of memory.

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  • Wearable biosensors face many fundamental challenges and technological gaps related to the scope, validation, stability and accuracy, along with power, communication and security/privacy issues.

    “Wearable biosensors face many fundamental challenges and technological gaps related to the scope, validation, stability and accuracy, along with power (Box 1)1,42,157-167, communication (Box 2)1,168 and security/privacy issues (Box 3)1,169-172”

    From Wearable biosensors for healthcare monitoring (2019) · cites this paper as [161]

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Authors
Mesin, L.
Affiliations
Politecnico di Torino (IT)
Corresponding author
Mesin, Luca
ORCID
Mesin, Luca
Venue
Computers in Biology and Medicine
Publisher
Elsevier BV
Published
2016-04
Volume
71
Pages
223-230
ISSN
0010-4825 (ISSN-L)
DOI
10.1016/j.compbiomed.2016.02.004
OpenAlex
W2259945320
Semantic Scholar
acdfc71314caccdb935ac24175374c1ddd5ea563
Cited by
22
References
45
Citations by year
2026: 1 · 2025: 1 · 2024: 4 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 2 · 2018: 1 · 2017: 2 · 2016: 1
FWCI
0.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers in Biology and Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • PORTO@Iris (submittedVersion, cc-by-nc-nd)
  • PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion, cc-by-nc-nd)
Funders
  • Department of Neurorehabilitation Engineering, University Medical Center, Goettingen
  • Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino
  • Epilepsy Service, Department of Neuroscience, University of Torino
  • EMG, ECG and EEG
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
Keywords
adaptive sampling, non-uniform sampling, neural network prediction, uncertainty estimation, physiological signals, non-stationary signals, wireless body area networks, sampling rate reduction, spectral estimation, electromyography, electrocardiogram, EEG, body acceleration
MeSH terms
Algorithms, Electronic Data Processing, Electrocardiography, Electroencephalography, Models, Theoretical, Accelerometry

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