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Related Concept Videos

Discrete Fourier Transform01:15

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction.

Timibloudi Enamamu1, Abayomi Otebolaku1, Jims Marchang1

  • 1Department of Computing, Sheffield Hallam University, Sheffield, S1 1WB, UK.

Sensors (Basel, Switzerland)
|October 10, 2020
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Summary
This summary is machine-generated.

This study introduces a novel m-health data authentication framework using smartphone-collected heart rate variability (HRV) signals. The research identifies optimal HRV sub-bands for secure and reliable remote patient monitoring.

Keywords:
approximation coefficientsbioelectrical signalsbiorthogonal waveletdetail coefficientm-health monitoringsmartphonessmartwatchwavelet transform

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Area of Science:

  • Biomedical Engineering
  • Public Health Technology
  • Mobile Health (m-health)

Background:

  • The World Health Organization (WHO) defines m-health as the use of mobile wireless technologies for public health.
  • Smart devices like smartphones and smartwatches are crucial for remote patient data collection in m-health.
  • Ensuring the integrity of m-health data through continuous authentication is vital before storage.

Purpose of the Study:

  • To develop and evaluate an architectural framework for m-health data authentication.
  • To identify the most effective sub-bands of heart rate variability (HRV) signals for data authentication.
  • To assess the performance of the proposed authentication framework using real-world data.

Main Methods:

  • Extraction and decomposition of heart rate variability (HRV) signals into sub-bands (detail and approximation coefficients).
  • Feature classification and comparative analysis to select the best performing sub-bands for authentication.
  • Implementation of an m-health data authentication framework using selected HRV sub-bands with data from 30 subjects.

Main Results:

  • The study successfully decomposed HRV signals and identified optimal sub-bands for data authentication.
  • The proposed framework demonstrated effective authentication capabilities.
  • The best performing sub-band achieved an equal error rate (EER) of 12.42% in authentication.

Conclusions:

  • The developed m-health data authentication framework enhances the security and reliability of remote patient monitoring.
  • Utilizing specific HRV sub-bands offers a promising approach for robust m-health data integrity.
  • This research contributes to the advancement of secure and effective m-health systems.