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Classification of non-stationary Heart Rate Variability using AR-model parameters

Risberg, Marcus LU (2015) FMS820 20151
Mathematical Statistics
Abstract
This thesis explores the connection between the heart rate variability
and both stress and age. Two methods are used to classify the heart rate
variability data, the autoregressive model and the Markov chain model.
The autoregressive model is further expanded to become an autoregressive
model with extraneous input using the respiratory signal as input signal.
The Markov chain models are compared with their stationary distribu-
tion using the Kolmogorov-Smirnov test. Autoregressive parameters are
compared using condence intervals. The results indicate statistically sig-
nicant deviations between age groups for both the autoregressive models
and the Markov model. The stress related results were not as clear as
the age related... (More)
This thesis explores the connection between the heart rate variability
and both stress and age. Two methods are used to classify the heart rate
variability data, the autoregressive model and the Markov chain model.
The autoregressive model is further expanded to become an autoregressive
model with extraneous input using the respiratory signal as input signal.
The Markov chain models are compared with their stationary distribu-
tion using the Kolmogorov-Smirnov test. Autoregressive parameters are
compared using condence intervals. The results indicate statistically sig-
nicant deviations between age groups for both the autoregressive models
and the Markov model. The stress related results were not as clear as
the age related results, however some deviations were obtained for both
models, indicating some stress related in
uence on the HRV. (Less)
Please use this url to cite or link to this publication:
author
Risberg, Marcus LU
supervisor
organization
course
FMS820 20151
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
7359634
date added to LUP
2015-06-16 15:13:53
date last changed
2015-06-18 12:21:44
@misc{7359634,
  abstract     = {This thesis explores the connection between the heart rate variability
and both stress and age. Two methods are used to classify the heart rate
variability data, the autoregressive model and the Markov chain model.
The autoregressive model is further expanded to become an autoregressive
model with extraneous input using the respiratory signal as input signal.
The Markov chain models are compared with their stationary distribu-
tion using the Kolmogorov-Smirnov test. Autoregressive parameters are
compared using condence intervals. The results indicate statistically sig-
nicant deviations between age groups for both the autoregressive models
and the Markov model. The stress related results were not as clear as
the age related results, however some deviations were obtained for both
models, indicating some stress related in
uence on the HRV.},
  author       = {Risberg, Marcus},
  language     = {eng},
  note         = {Student Paper},
  title        = {Classification of non-stationary Heart Rate Variability using AR-model parameters},
  year         = {2015},
}