SLIDE 1
Personalized Depression Tuan D. Pham Saudi Aramco Center for - - PowerPoint PPT Presentation
Personalized Depression Tuan D. Pham Saudi Aramco Center for - - PowerPoint PPT Presentation
The Recurrence Dynamics of Personalized Depression Tuan D. Pham Saudi Aramco Center for Artifcial Intelligence Prince Mohammad bin Fahd University University Khobar, Saudi Arabia 04 - 06 February 2020 Melbourne, Vic, Australia Outline
SLIDE 2
SLIDE 3
Depression and Nonlinear Dynamics
◼ Major depression (MD) is associated with
morbidity and risk for suicide.
◼ Response rates of antidepressant treatments
are relatively low.
◼ In addition to the heterogeneous causes of
MD, the disorder shows complex transitions between several disease states.
◼ Hypotheses trying to explain the dynamics of
depression have certain limitations, so our understanding what causes depression is still incomplete (Demic & Cheng, PloS One, 2014).
SLIDE 4
Data of Personalized Depression
◼ The participant completed 1478
measurements over the course of 239 consecutive days in 2012 and 2013.
◼ Five phases: 1 (base line), 2 (double-blind,
no antidepressant reduction), 3 (double- blind, under antidepressant reduction), 4 (post assessment), and 5 (follow-up).
SLIDE 5
Data of Personalized Depression
◼ Twelve affective items: 1) restless, 2)
agitated, 3) irritated, 4) anxious, 5) lonely, 6) guilty, 7) enthusiastic, 8) cheerful, 9) content, 10) strong, 11) worrying, and 12) suspicious.
◼ The five mental states: 1) unrest, 2)
negative, 3) positive, 4) worrying, and 5) suspicious.
◼ Measurement: 7-point Likert scale: -3 (not)
to 3 (very), or 1 (not) to 7 (very).
SLIDE 6
Analysis of Depression with Nonlinear Dynamics
◼ Fuzzy recurrence plots ◼ Fuzzy joint recurrence plots ◼ Fuzzy weighted recurrence networks ◼ Tensor decomposition of mental-state
dynamics
SLIDE 7
SLIDE 8
Recurrence Plots A recurrence plot (RP) enables us to investigate certain aspects
- f the m-dimensional phase
space trajectory through a 2-D representation.
SLIDE 9
SLIDE 10
What can an RP tell?
◼ An RP is characterized by typical patterns,
helpful for understanding the underlying dynamics of the system investigated.
◼ A homogeneous distribution of points:
associated with stationary stochastic processes; e.g., Gaussian white noise.
◼ Long diagonal lines: periodic behaviors ◼ White areas or bands: non-stationarity and
abrupt changes in the dynamics.
SLIDE 11
(a) (b)
(a) RP of a logistic map that consists of single dots and line structures (Marwan et al., Physics Letters A 360 (2007) 545–551. (b) RPs for a sinusoidal signal: 2 Hz (left) and 25 Hz (right) (Llop et al,
- Int. J. Multiphase Flow, 73 (2015) 43-56.
SLIDE 12
SLIDE 13
SLIDE 14
SLIDE 15
RPs vs. FRPs
◼ RPs are displays of binary texture. ◼ FRPs are displays of gray-scale texture. ◼ RPs are sensitive to the threshold for
similarity.
◼ FRPs are visible with selection of various
numbers of fuzzy clusters.
SLIDE 16
SLIDE 17
SLIDE 18
SLIDE 19
Fuzzy Relation
where θ and ψ are cluster centers, and x is a data point. The use inference of relation between cluster centers allow scalability of the network.
SLIDE 20
SLIDE 21
SLIDE 22
SLIDE 23
EMG signals: healthy (left), myopathy (center), and neuropathy (right). Hierarchical clustering of characteristic path lengths (PN: pink noise, WN: white noise).
SLIDE 24
Published January 2020
SLIDE 25
Tensor Decomposition
Subjects x Mental States x Recurrence Dynamics
SLIDE 26
Results & Discussion
SLIDE 27
Results & Discussion
SLIDE 28
Results & Discussion
Fuzzy joint recurrence plot of time series of the unrest state (e) in experimental phase 1 (baseline).
SLIDE 29
Results & Discussion
SLIDE 30
Results & Discussion
SLIDE 31
Results & Discussion
SLIDE 32
Results & Discussion
SLIDE 33
Results & Discussion
SLIDE 34
Results & Discussion
SLIDE 35
Results & Discussion
SLIDE 36
Conclusion
◼ Both complex network analysis and
tensor-decomposition of the recurrence dynamics indicate that the participant was vulnerable to develop a new episode of depression when the anti-depressant medication was reduced and stopped.
◼ Such a detection in the recurrence
dynamics of the data van be considered as a personalized warning signal for depression.
SLIDE 37