W1&2 Applications of Variability and Complexity Analyses W2 kinda UNFINISHED

Question 1

fractal

Answer 1

geometric shape that has the same appearance when you zoom in - self-similar

Question 2

example of fractal shape and self-similarity in humans

Answer 2

lungs - have higher surface area than volume should allow

Question 3

key idea of fractals

Answer 3

really simple rule can produce a really complex output/apperance

Question 4

fractal dimension

Answer 4

a measure of how complex a self-similar figure is

Question 5

two types of self-similarity

Answer 5

spatial and temporal

Question 6

lungs - spatial or temporal self-similarity?

Answer 6

spatial self-similarity

Question 7

spatial self-similarity

Answer 7

if you zoom in on a shape, you find the same shape at a more zoomed in level

Question 8

temporal self-similarity

Answer 8

• see similar repeating structures at different levels of inspection in time series
• time series looks the same over 30 minutes as it does over 300 minutes
• nothing systematically changing in what we see at different levels of inspection

Question 9

white noise

Answer 9

• completely random
• can take on any value at any point
• flat spectrum
• most complex

Question 10

Brownian noise

Answer 10

• random walk noise
• e.g. staggering home drunk, constrained by where you were the last step but next step can be in any direction
• least complex

Question 11

1/f noise

Answer 11

• between white and brown noise
• pink noise
• in this time series we see self-similar behaviour
• common in healthy biological systems
• moderately complex

Question 12

double pendulum

Answer 12

• simple system just behaving based on gravitational laws
• but very quickly you cannot predict it
• simple system with complex and chaotic output

Question 13

What is a chaotic system?

Answer 13

A system which is seemingly deterministic, but you cannot actually predict where the end point will be.

Question 14

What is an Improbable system?

Answer 14

A highly ordered system with a structure

Question 15

Explain the graph from Macklem, 2008?

Answer 15

It highlights how life exists between a very ordered levely of complexity and a very chaotic system. It is chaotic enough to allow for adaptability but we can put energy in to maintain some structure

Question 16

What is the problem with this balance between stability and adaptability? Give an example

Answer 16

• Small changes in a system can cause such vast changes in the whole system.
• One seemingly minor thing can deviate the whole system from its original purpose or function.
• E.g. a small change in the width of a tube within the lungs can cause a change in the overall respiratory system.

Question 17

Two types of ways of measuring complexity

Answer 17

• Magnitude - standard deviation/coefficient of variation
  -> Takes no account of temporal data order - quantify “variability” or “steadiness”
• Temporal pattern: this is what “complexity” usually refers to

Question 18

How does Approximate Entropy (ApEn) measure complexity?

Answer 18

• Measures the regularity of a time series by searching for patters which repeats themselves.
• It considers noise in the system so matching of repeated patterns in quite loose.
• higher ApEn = more info, less predictable
• ranges from 0 to 2
• 0 = completely predictable
• 2 = white noise (unpredictable)

Question 19

What is Sample Entropy (SampEn)

Answer 19

• Evaluates the probability that similar sequences will remain similar over subsequent points.
• Unlike ApEN, SampEn incoporates a “self-matching” function that avoids bias introduced by self-matching in the ApEn calculation

Question 20

How does Detrended fluctuation analysis (DFA) measure complexity?

Answer 20

• You detrend the data by separating it into different boxes with different scales.
• You then fit a line of best fit to each box and subtract the mean associated with that line.
• You can then look at the residual fluctuation.
• You can then complete that process for smaller box sizes.
• You then plot the log of the root mean square error against the log of the corresponding box size.
• You fit a line and that line gives you your alpha (α) exponent.
• An alpha exponent value of 0.5 indicates random, white noise (high complexity).
• An alpha value of 1 indicates pink noise (moderate complexity),
• a value of 1.5 indicates Brownian noise (lower complexity).

Question 21

How is entropy used in information theory?

Answer 21

entropy increases as freedom of choice increases and decreases as uncertainty decreases

Question 22

Give an example of ApEn being applied to a biological time series

Answer 22

(Lipsitz & Goldberger, 1992)
* heart rate series from a young and old individual
* If you calculate the mean and SD, they seem to be fairly similiar but the structure is obviously different
* ApEn for the older subject is much lower, while the younger subject has a lot of self-similarity and entropy
* suggests that, as you get older, your system loses complexity and becomes much more structured and predictable
* When you grow older, you lose capacity to adapt

Question 23

ApEn applied to SIDS (sudden infant death)

Answer 23

(Pincus et al., 1993)
* normal infant vs infant that experience an aborted SIDS event
* SIDS infant had much lower ApEn
* showing a loss of complexity with disease

Question 24

Give three reasons for why there are fluctuations in Isometric Force Production

Answer 24

• Motor Unit Recruitment
• Motor Unit Firing
• Muscle-Tendon Intercations

Question 25

Why do we lose complexity with age?

Answer 25

• this process of denervation and reinnervation
• As you age, you lose some of the innervation into your type 2 muscle fibres and they get reinnervated (reattached to) the motor nerve that supply type 1 fibres
• have fewer motor units over time that are larger and slower
• That loss of complexity in the bits making up the motor system leads to reduction in complexity in the motor output

Question 26

define fatigue

Answer 26

Any exercise-induced decrease in maximal voluntary force of power produced by a muscle or muscle group (Taylor & Gandevia, 2008)

Question 27

How does isometric muscle force change with fatigue?

Answer 27

• isometric muscle force is complex at rest and loses complexity at task failure

Question 28

Describe a study that explains how fatigue and entropy relates

Answer 28

(Pethick et al., 2015)
* Participants were required to do a duty cycle; 6 seconds on 4 seconds off. Their ApEn, SampEn, and DFA were measured in the first minute and in the last minute.
* First min: SampEn, ApEn, and DFA were all high, indicating self similarity and 1/F noise.
* Last min: Low ApEn, SampEn, suggesting fatigue causes the system to become more predicable.
* This could be due to their muscles losing oxygen so the participants have to do non-complex movements.
* Complexity is sacrificed to prevent fatigue.

Question 29

What is critical power?

Answer 29

(Monod & Scherrer, 1965)
* Researchers found an asymptotic point, where you can reporduce the power for a longer time, the critical power.
* It is a maximum rate a muscle can keep up for a very long time without fatigue.

Question 30

Does the fatigue-induced loss of complexity occur below the critical torque (power)?

Answer 30

(Pethick et al., 2016)
* critical torque is a range not a neat cut-off
* below CP no major loss of compelxity
* above CP loss of complexity over time