A Bayesian classification of heart rate variability data
We propose a simple Bayesian method for the classification of time series signals originating from mutually exclusive sources. In particular, the method is used to address the question of whether a 24-h recording of human heart rate data is produced by a normally functioning heart or by one exhibiting symptoms of congestive heart failure. Our method correctly classifies 18 of 18 normal heart data sets, and 38 of 44 congestive failure data sets.
Year of publication: |
2004
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Authors: | Muirhead, R.J ; Puff, R.D |
Published in: |
Physica A: Statistical Mechanics and its Applications. - Elsevier, ISSN 0378-4371. - Vol. 336.2004, 3, p. 503-513
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Publisher: |
Elsevier |
Subject: | Bayesian inference | Time series analysis | Heart rate variability | Signal classification | Biological signal processing and instrumentation |
Saved in:
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