7 Synergetic Interpretation of Patterned Vasomotion Activity …
135
Not surprisingly, a number of amiable layman in command of powerful computers
went on to “portray” the dynamics what they mistook as “the activity of the heart”:
this then led to the horrendous misconception that chaoticity were a sign of normal
function, whereas “order” were a sign of pathology. Without such prejudices, it has
now become possible to solve clinical problems.
Most importantly, this is achieved by minimally invasive techniques and the utilisation of the plethora of data compression techniques put at the disposal of medical
practitioners. As we are beginning to develop problem adapted semantics on the one
hand, and powerful problem adapted means of “feature extraction” from prolonged
time series on the other the time has come to shoulder tasks that cannot be mastered
in any other fashion. As shown in a recent publication concerning hypercomplex
data gathered in animal experiments, this set of algorithms can in fact be utilised in
comprehensive analysis of far more complicated and extended processes recorded
invasively (which are more complex than those occurring in clinical settings (see
Lambert et al.).
However, the same technique is also capable of demonstrating order under load
in human subjects by non-invasive means. As a demonstration of the most important potentials of such a routine, which in the future will allow to display the highly
informative frequency chromatograms as a series of “time –frequency plots” , the
periodically fluctuating activities are being displayed in an intuitively comprehensible fashion. As shown in Fig. 7.10, the data obtained from ergometer exercise in
a task where the volunteer was asked to strain himself in such a fashion that he
felt “subjectively comfortable” while working at about 120 W (displayed to him as
feed-back of rotational speed of the bicycle), a remarkably regular activity of the
respiration, the cardiac activity and the skeletomuscular was seen, in which there
was a remarkable integer number relationship between their periodicities.
In other words, the apparent chaoticity (or the emergence and submergence of
quasi-attractors) at rest had not just made room to a most regular configuration of
powerful stationary attractors, but tended to so-called n:m synchronisation: under
physical load, there is 1 to 4 relation of respiration and cardiac frequency, and 1–
2 correlation between skeletomuscular activity and ventilation. While at present it
has to remain unsettled weather this remarkable entrainment reflects just frequency
synchronisation or phase synchronisation, this simple experimental paradigm can be
accepted as a typical case of load dependent plasticity (or drive dependent “movement
consensualisation”), see Tass and Varela et al. This is obviously a situation, where
with the help of non-invasive computer-aided diagnostic tools a most remarkable and
highly stable “dynamic order” can be documented in an easily intuit able fashion.
Moreover, many of the parameters representing the diagnostic instrumentarium
developed in nonlinear dynamics can be applied directly or indirectly in the future
application of the “double plot” concepts, i.e. the simultaneous display of the original
time series (and thence the projection of the amplitude dynamics) and the time –
frequency plots (i.e. projection into the frequency space). Most importantly, these
easily comprehensible plots reflect the appearance of gliding coordination in the sense
of Erich von HOLST, the divergence of trajectories (or the occurrence of positive
Lyapunov exponents and, most interestingly, the fine-grained or the coarse-grained
135
Not surprisingly, a number of amiable layman in command of powerful computers
went on to “portray” the dynamics what they mistook as “the activity of the heart”:
this then led to the horrendous misconception that chaoticity were a sign of normal
function, whereas “order” were a sign of pathology. Without such prejudices, it has
now become possible to solve clinical problems.
Most importantly, this is achieved by minimally invasive techniques and the utilisation of the plethora of data compression techniques put at the disposal of medical
practitioners. As we are beginning to develop problem adapted semantics on the one
hand, and powerful problem adapted means of “feature extraction” from prolonged
time series on the other the time has come to shoulder tasks that cannot be mastered
in any other fashion. As shown in a recent publication concerning hypercomplex
data gathered in animal experiments, this set of algorithms can in fact be utilised in
comprehensive analysis of far more complicated and extended processes recorded
invasively (which are more complex than those occurring in clinical settings (see
Lambert et al.).
However, the same technique is also capable of demonstrating order under load
in human subjects by non-invasive means. As a demonstration of the most important potentials of such a routine, which in the future will allow to display the highly
informative frequency chromatograms as a series of “time –frequency plots” , the
periodically fluctuating activities are being displayed in an intuitively comprehensible fashion. As shown in Fig. 7.10, the data obtained from ergometer exercise in
a task where the volunteer was asked to strain himself in such a fashion that he
felt “subjectively comfortable” while working at about 120 W (displayed to him as
feed-back of rotational speed of the bicycle), a remarkably regular activity of the
respiration, the cardiac activity and the skeletomuscular was seen, in which there
was a remarkable integer number relationship between their periodicities.
In other words, the apparent chaoticity (or the emergence and submergence of
quasi-attractors) at rest had not just made room to a most regular configuration of
powerful stationary attractors, but tended to so-called n:m synchronisation: under
physical load, there is 1 to 4 relation of respiration and cardiac frequency, and 1–
2 correlation between skeletomuscular activity and ventilation. While at present it
has to remain unsettled weather this remarkable entrainment reflects just frequency
synchronisation or phase synchronisation, this simple experimental paradigm can be
accepted as a typical case of load dependent plasticity (or drive dependent “movement
consensualisation”), see Tass and Varela et al. This is obviously a situation, where
with the help of non-invasive computer-aided diagnostic tools a most remarkable and
highly stable “dynamic order” can be documented in an easily intuit able fashion.
Moreover, many of the parameters representing the diagnostic instrumentarium
developed in nonlinear dynamics can be applied directly or indirectly in the future
application of the “double plot” concepts, i.e. the simultaneous display of the original
time series (and thence the projection of the amplitude dynamics) and the time –
frequency plots (i.e. projection into the frequency space). Most importantly, these
easily comprehensible plots reflect the appearance of gliding coordination in the sense
of Erich von HOLST, the divergence of trajectories (or the occurrence of positive
Lyapunov exponents and, most interestingly, the fine-grained or the coarse-grained