Correntropy-based spectral characterization of respiratory Patterns in patients with chronic heart failure

A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized corre...

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Detalhes bibliográficos
Autores: Garde Martínez, Ainara, Sörnmo, Leif, Jané Campos, Raimon|||0000-0002-6541-8729, Giraldo Giraldo, Beatriz|||0000-0002-9910-8577
Formato: artículo
Fecha de publicación:2010
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/9961
Acesso em linha:https://hdl.handle.net/2117/9961
https://dx.doi.org/10.1109/TBME.2010.2044176
Access Level:acceso abierto
Palavra-chave:Autoregressive processes (cardiology)
Medical signal processing
Peumodynamics
Chronic heart failure (CHF)
Cor -- Malalties -- Diagnòstic
Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
Descrição
Resumo:A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropybased spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PBor nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients fromhealthy subjects with an accuracy of 94.4%.