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Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal
Garde Martínez, Ainara; Sörnmo, Leif; Jané Campos, Raimon; Giraldo Giraldo, Beatriz
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Institut de Bioenginyeria de Catalunya; Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
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.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
Chronic heart failure
Respiratory pattern
Cor -- Malalties -- Diagnòstic
info:eu-repo/semantics/publishedVersion
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