Detail publikace

Application of Advanced Spectral Analysis for Rehabilitation Progress Estimation Concerning Patients After Ischemic Brain Stroke

Originální název

Application of Advanced Spectral Analysis for Rehabilitation Progress Estimation Concerning Patients After Ischemic Brain Stroke

Anglický název

Application of Advanced Spectral Analysis for Rehabilitation Progress Estimation Concerning Patients After Ischemic Brain Stroke

Jazyk

en

Originální abstrakt

Biomedical signals are in most cases highly complex and fortunately understandably nonlinear. It concerns i.e. firing of neurons, beating of the heart or breathing. These signals arise out of a multitude of interconnected elements comprising of the human body. Mentioned elements are bounded and the connections are rather weakly coupled across all elements. Signal propagation processes vary in time scales ranging from nanoseconds for molecular motion to gross observable behavior in term of days and years. This is in fact the reason why biomedical systems are highly nonstationary. The fundamental nature of the brains electrical activity remains unknown. Therefore a linear stochastic model of the brain as well as pure spectral analysis of its activity seems to appear nonsufficient as signals recorded are usually associated with some noise and artifacts, which in most cases highly contaminate obtained results of measurements and further analysis. In this paper we have ecided to present an indirect approach, less stress-causing for the individual patient and on the other hand much common and simpler in comparison to the electroencephalographic (EEG) recordings. This concerns application of Heart RateVariability (HRV) recording and analysis extracted from electrocardiographic (ECG) signals. Having created a suitable database with ECG recordings performed with the help of Holter method registration we started to apply a bit more sophisticated analysis such as calculation of bispectra and bicoherence.

Anglický abstrakt

Biomedical signals are in most cases highly complex and fortunately understandably nonlinear. It concerns i.e. firing of neurons, beating of the heart or breathing. These signals arise out of a multitude of interconnected elements comprising of the human body. Mentioned elements are bounded and the connections are rather weakly coupled across all elements. Signal propagation processes vary in time scales ranging from nanoseconds for molecular motion to gross observable behavior in term of days and years. This is in fact the reason why biomedical systems are highly nonstationary. The fundamental nature of the brains electrical activity remains unknown. Therefore a linear stochastic model of the brain as well as pure spectral analysis of its activity seems to appear nonsufficient as signals recorded are usually associated with some noise and artifacts, which in most cases highly contaminate obtained results of measurements and further analysis. In this paper we have ecided to present an indirect approach, less stress-causing for the individual patient and on the other hand much common and simpler in comparison to the electroencephalographic (EEG) recordings. This concerns application of Heart RateVariability (HRV) recording and analysis extracted from electrocardiographic (ECG) signals. Having created a suitable database with ECG recordings performed with the help of Holter method registration we started to apply a bit more sophisticated analysis such as calculation of bispectra and bicoherence.

BibTex


@article{BUT110403,
  author="Ewaryst {Tkacz} and Ivo {Provazník} and Paweł {Kostka}",
  title="Application of Advanced Spectral Analysis for Rehabilitation Progress Estimation Concerning Patients After Ischemic Brain Stroke",
  annote="Biomedical signals are in most cases highly complex and fortunately understandably nonlinear. It concerns i.e. firing of neurons, beating of the heart or breathing. These signals arise out of a multitude of interconnected elements comprising of the human body. Mentioned elements are bounded and the connections are rather weakly coupled across all elements. Signal propagation processes vary in time scales ranging from nanoseconds for molecular motion to gross observable behavior in term of days and years. This is in fact the reason why biomedical systems are highly nonstationary. The fundamental nature of the brains electrical activity remains unknown. Therefore a linear stochastic model of the brain as well as pure spectral analysis of its activity seems to appear nonsufficient as signals recorded are usually associated with some noise and artifacts, which in most cases highly contaminate obtained results of measurements and further analysis. In this paper we have ecided to present an indirect approach, less stress-causing for the individual patient and on the other hand much common and simpler in comparison to the electroencephalographic (EEG) recordings. This concerns application of Heart RateVariability (HRV) recording and analysis extracted from electrocardiographic (ECG) signals. Having created a suitable database with ECG recordings performed with the help of Holter method registration we started to apply a bit more sophisticated analysis such as calculation of bispectra and bicoherence.",
  chapter="110403",
  doi="10.7763/IJBBB.2014.V4.315",
  number="4",
  volume="2014",
  year="2014",
  month="march",
  pages="82--85",
  type="journal article"
}