Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal
Författare
Summary, in English
This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.
Publiceringsår
2010
Språk
Engelska
Sidor
3572-3580
Publikation/Tidskrift/Serie
Annals of Biomedical Engineering
Volym
38
Issue
12
Dokumenttyp
Artikel i tidskrift
Förlag
Springer
Ämne
- Electrical Engineering, Electronic Engineering, Information Engineering
Nyckelord
- Respiratory pattern
- Discriminant
- Periodic and nonperiodic breathing
- band
- Chronic heart failure
- AR modeling
Status
Published
Forskningsgrupp
- Signal Processing
ISBN/ISSN/Övrigt
- ISSN: 1573-9686