Evaluation of Time-Frequency Domain Features for Heart Rate Variability Signal Classification.
(2024) In Bachelor's Thesis in Mathematical Sciences MASK11 20241Mathematical 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:
http://lup.lub.lu.se/student-papers/record/9169299
- author
- Stark, Joanna LU
- supervisor
-
- Maria Sandsten LU
- Maria Ã…kesson LU
- organization
- course
- MASK11 20241
- year
- 2024
- 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}}, }