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Evaluation of Time-Frequency Domain Features for Heart Rate Variability Signal Classification.

Stark, Joanna LU (2024) In Bachelor's Thesis in Mathematical Sciences MASK11 20241
Mathematical Statistics
Abstract (Swedish)
The search for non-invasive tools to monitor stress has gained significant research attention. Heart rate variability is one such measure commonly analyzed in either the time domain or the frequency domain. This project explores the use of time-frequency analysis, specifically the spectrogram, for this purpose. The study utilizes real data obtained from a cold pressor test and a control session. One of the primary aims was to identify features in the time-frequency domain that could be used to classify the two different sessions and compare various frequency bands. The results suggest that there are potential features in the time-frequency domain that could be further explored for the classification of the signals.
Please use this url to cite or link to this publication:
author
Stark, Joanna LU
supervisor
organization
course
MASK11 20241
year
type
M2 - Bachelor Degree
subject
publication/series
Bachelor's Thesis in Mathematical Sciences
report number
LUNFMS-4076-2024
ISSN
1654-6229
other publication id
2024:K17
language
English
id
9169299
date added to LUP
2024-07-01 09:52:47
date last changed
2024-07-01 09:52:47
@misc{9169299,
  abstract     = {{The search for non-invasive tools to monitor stress has gained significant research attention. Heart rate variability is one such measure commonly analyzed in either the time domain or the frequency domain. This project explores the use of time-frequency analysis, specifically the spectrogram, for this purpose. The study utilizes real data obtained from a cold pressor test and a control session. One of the primary aims was to identify features in the time-frequency domain that could be used to classify the two different sessions and compare various frequency bands. The results suggest that there are potential features in the time-frequency domain that could be further explored for the classification of the signals.}},
  author       = {{Stark, Joanna}},
  issn         = {{1654-6229}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Bachelor's Thesis in Mathematical Sciences}},
  title        = {{Evaluation of Time-Frequency Domain Features for Heart Rate Variability Signal Classification.}},
  year         = {{2024}},
}