The analysis of heart rate variability is a tool to investigate the autonomic cardiac control and the well-functioning of the autonomic nervous system. The sympathetic and parasympathetic systems are the principle rapidly reacting systems that control heart and the methods usually employed are not sufficient alone to describe these dynamic changes. The integration of parameters derived from the non-linear techniques, like symbolic dynamic analysis, and traditional ones derived from frequency domain analysis could improve the evaluation of the complexity of cardiac regulation systems. Here, 50 ECG Holter of normal, hypertension, post-myocardial infarction, cardiac failure and transplanted subjects were examined using new indexes obtained with the symbolic dynamic analysis and more traditional parameters computed by the power spectral density of the heart variability signals. The results demonstrate that both methodologies are able of distinguishing the pathological subjects from healthy ones, in a more evident way with increasing of the disease severity.

Frequency domain and symbolic dynamics analysis for the study of cardiac pathologies

Maria Romano;
2013-01-01

Abstract

The analysis of heart rate variability is a tool to investigate the autonomic cardiac control and the well-functioning of the autonomic nervous system. The sympathetic and parasympathetic systems are the principle rapidly reacting systems that control heart and the methods usually employed are not sufficient alone to describe these dynamic changes. The integration of parameters derived from the non-linear techniques, like symbolic dynamic analysis, and traditional ones derived from frequency domain analysis could improve the evaluation of the complexity of cardiac regulation systems. Here, 50 ECG Holter of normal, hypertension, post-myocardial infarction, cardiac failure and transplanted subjects were examined using new indexes obtained with the symbolic dynamic analysis and more traditional parameters computed by the power spectral density of the heart variability signals. The results demonstrate that both methodologies are able of distinguishing the pathological subjects from healthy ones, in a more evident way with increasing of the disease severity.
2013
9781479923731
biomedical signal processing; cardiography; heart rate variability (HRV); medical signal detection; symbolic dynamics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12317/19228
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