RESUMO
The spatial distribution of the shape of the electrocardiography (ECG) waves obtained by body surface potential mapping (BSPM) is studied, using a 64-channel high-resolution ECG system. The index associated to each lead is the shape difference between its ECG wave and a reference computed taking into account all the leads on the same column. The reference is either a selected real wave or a synthetic signal computed by integral shape averaging (ISA). Better results are obtained with the ISA signal using the distribution function method (DFM) for computing the shape difference. The spatial dispersion of ECG waves is showed to allow the separation of patients after myocardial infarction (MI) from healthy subjects. In addition, the reference signal position for each column is computed. The path linking these positions appears as an invariant, i.e., it is independent of the subject and the ECG wave.
Assuntos
Algoritmos , Mapeamento Potencial de Superfície Corporal/métodos , Diagnóstico por Computador/métodos , Eletrocardiografia/métodos , Modelos Cardiovasculares , Infarto do Miocárdio/diagnóstico , Infarto do Miocárdio/fisiopatologia , Simulação por Computador , Humanos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
The analysis of heart period series is a difficult task especially under graded exercise conditions. From all the information present in these series, we are the most interested in the coupling between respiratory and cardiac systems, known as respiratory sinus arrythmia. In this paper, we show that precise patterns concerning the respiratory frequency can be extracted from the heart period series. An evolutive model is introduced in order to achieve tracking of the main respiratory-related frequencies and their time-varying amplitudes. Since respiration acts to modulate the sinus rhythm, we relate the frequencies and amplitudes to this modulation by analyzing in detail its nonlinear transformation giving the heart period signal. This analysis is performed assuming stationary conditions but also in the realistic case where the mean heart period, the amplitude, and the frequency of the respiration are time-varying. Since this paper is devoted to the theoretical and complete presentation of the method used in a physiological study published elsewhere, the capabilities of our method will be illustrated in a realistic simulated case.