136
H. Schmid-Schönbein
Fig. 7.10 Prototypical demonstration of a time –frequency plot in a subject exposing himself to
“comfortable” physical (bicycle ergometer) exercise of 120 W. The subject was asked to adjust the
rotational speed so that he felt physically comfortable. Note the stable frequencies of skeletomuscular activity, respiratory activity and cardiovascular activity (recorded by FINAPRESS ® signal)
an the integer number (n:m) correlation of ca. 0.6 Hz, ca 0.12 Hz and ca 2.4 Hz in ventilation,
skeletomuscular activity and cardiac 1/f dynamics
features underlying fractal dimensionality of complex physiological time series). In
order to understand these in a more fundamental context, it will be necessary to
relate PMA patterns observable in non-invasive techniques to the concepts we have
concerning the underlying TMI evasion mechanisms and their direct and indirect
sequelae: this will likewise be the topic of a communication to the forthcoming
conference on computer-based non-invasive diagnostics.
H. Schmid-Schönbein
Fig. 7.10 Prototypical demonstration of a time –frequency plot in a subject exposing himself to
“comfortable” physical (bicycle ergometer) exercise of 120 W. The subject was asked to adjust the
rotational speed so that he felt physically comfortable. Note the stable frequencies of skeletomuscular activity, respiratory activity and cardiovascular activity (recorded by FINAPRESS ® signal)
an the integer number (n:m) correlation of ca. 0.6 Hz, ca 0.12 Hz and ca 2.4 Hz in ventilation,
skeletomuscular activity and cardiac 1/f dynamics
features underlying fractal dimensionality of complex physiological time series). In
order to understand these in a more fundamental context, it will be necessary to
relate PMA patterns observable in non-invasive techniques to the concepts we have
concerning the underlying TMI evasion mechanisms and their direct and indirect
sequelae: this will likewise be the topic of a communication to the forthcoming
conference on computer-based non-invasive diagnostics.