Relationship between ventilatory oscillations and fractal dimension of the EEG during daytime periodic breathing in heart failure patients.

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Abstract

In this study we investigated the existence and the nature of rhythmic changes in EEG associated with ventilatory oscillations in heart failure (HF) patients with periodic breathing (PB). Since nonlinear mechanisms are thought to be involved in the generation of EEG, we hypothesized that a mathematical approach based on nonlinear methods would provide relevant information on the association between EEG and ventilatory oscillations. We studied five patients who developed a sustained non-obstructive PB pattern during a 20 min laboratory recording. The time course of the fractal dimension of the EEG signal (HFD) was estimated dividing this signal into 2 s segments, with a 1.5 s overlap and computing for each EEG segment the fractal dimension using the Higuchi's algorithm. From the lung volume signal, an instantaneous minute ventilation (IMV) signal was also computed. The relationship between IMV and HFD was assessed by bivariate spectral analysis, computing the magnitude square coherence function (MSC). In four patients the value of the MSC was very high, ranging from 0.75 to 0.91, while in one patient the value was only 0.29. Our results suggest that in patients with PB, rhythmic changes in the EEG signal are very common and, when present, they are associated with ventilatory oscillations. We have also demonstrated that such oscillations can be detected very effectively by a technique based on nonlinear methods.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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