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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
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Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks.

Carmen Camara1, Pedro Peris-Lopez2, Honorio Martín3

  • 1Department of Computer Science, University Carlos III of Madrid, 28911 Leganés, Spain. macamara@pa.uc3m.es.

Sensors (Basel, Switzerland)
|August 24, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel True Random Number Generator (TRNG) using cardiac signals for enhanced Wireless Sensor Network (WSN) security. The proposed TRNG meets rigorous statistical tests, proving suitable for securing WSNs.

Keywords:
Electrocardiogram (ECG) sensorRandom Number Generators (RNGs)Wireless Sensor Networks (WSNs)wavelet

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Biomedical Engineering

Background:

  • Wireless Sensor Networks (WSNs) are vital for applications like environmental monitoring and healthcare but face security threats due to wireless connectivity.
  • Cryptographic solutions, including Random Number Generators (RNGs), are crucial for WSN security, particularly for authentication and key generation.
  • Existing RNGs may not be optimally suited for resource-constrained WSN devices.

Purpose of the Study:

  • To propose and evaluate an avant-garde True Random Number Generator (TRNG) for securing Wireless Sensor Networks (WSNs).
  • To leverage biological signals, specifically cardiac signals, as a source for generating random bits.
  • To validate the randomness and suitability of the proposed TRNG for WSN security applications.

Main Methods:

  • Utilized electrocardiogram (ECG) signals from a large public dataset (202 subjects, 24 hours).
  • Applied multi-level decomposition via wavelet analysis for extracting random bits from cardiac signals.
  • Assessed the TRNG's output using rigorous statistical test batteries (ENT, DIEHARDER, NIST), bias, distinctiveness, and performance analysis.

Main Results:

  • The proposed TRNG, utilizing cardiac signals and wavelet decomposition, demonstrated output streams behaving as random variables.
  • Statistical tests (ENT, DIEHARDER, NIST) confirmed the high quality of randomness.
  • The TRNG exhibited suitable performance characteristics for WSN applications.

Conclusions:

  • The proposed cardiac-signal-based TRNG is a viable and effective solution for enhancing the security of Wireless Sensor Networks.
  • The method provides a novel, biologically-inspired approach to random number generation for embedded systems.
  • The TRNG's proven randomness and performance make it suitable for cryptographic protocols in WSNs.