Dr Athanasios Tsanas (‘Thanasis’) [email protected]
Transcript of Dr Athanasios Tsanas (‘Thanasis’) [email protected]
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Dr Athanasios Tsanas (‘Thanasis’) [email protected] Associate Professor in Data ScienceUsher Institute, Medical SchoolUniversity of Edinburgh
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Address unmet clinical needs
Assessment &Monitoring
▪ Frequent
▪ Unobtrusive
▪ Personalised
▪ Inexpensive
▪ Massive scale
© A. Tsanas, 2021
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Project 1
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Physical presence in the clinic
• Cumbersome for those living in remote areas
• People need to take days off work
Patient-led self-monitoring
• Recall bias
• Subjectivity
No ground truth
• Difficult to objectively assess intervention effects
• Inter-rater variability
© A. Tsanas, 2021
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Sensor-based objective monitoring
Frequent time-stamped self-reports
© A. Tsanas, 2021
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▪ Continuous personalized monitoring
▪ Objectively quantify mental health
▪ Identify characteristic patterns
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Tidal Generation: Rolls-Royce EMEC 500kW
▪ 48 Bipolar Disorder (BD)
▪ 31 Borderline Personality Disorder (BPD)
▪ 51 Healthy Controls (HC)
❖ Questionnaires: ASRM, QIDS, GAD-7 + MZ (novel)
❖ Data collected over a year for most participants
© A. Tsanas, 2021
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© A. Tsanas, 2021
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A. Tsanas et al.: Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline
personality disorder, Journal of Affective Disorders, Vol. 205, pp. 225-233, 2016
▪ Longitudinal, daily monitoring of mood
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P1 P2 P3 P4 P5 P6
Anxious 0.55 0.08 -0.47 -0.27 0.60 0.18
Elated -0.11 0.76 -0.11 -0.53 -0.33 0.01
Sad 0.52 0.04 -0.43 0.39 -0.57 -0.25
Angry 0.42 0.11 0.46 0.11 -0.21 0.74
Irritable 0.47 0.12 0.60 -0.15 0.14 -0.60
Energetic -0.13 0.62 0.020 0.67 0.38 -0.03
% Total
variance
explained
55 77 85 91 97 100
Tentative
interpretation
“Negative
feelings”
“Positive
feelings”
“Irritability”
A. Tsanas et al.: Clinical insight into latent variables of psychiatric questionnaires for mood symptom
self-assessment, JMIR Mental Health, Vol. 4, pp. e15, 2017
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A. Tsanas et al.: Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline
personality disorder, Journal of Affective Disorders, Vol. 205, pp. 225-233, 2016
A. Tsanas et al.: Clinical insight into latent variables of psychiatric questionnaires for mood symptom
self-assessment, JMIR Mental Health, Vol. 4, pp. e15, 2017
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Potentially conveys information
Identify patterns of missing data and scores
Longitudinal questionnaire data for a single participant
© A. Tsanas, 2020
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N. Palmius, A. Tsanas, K.E.A. Saunders, A.C. Bilderbeck, J.R. Geddes, G.M. Goodwin, M. De Vos:
Detecting bipolar depression from geographic location data, IEEE Transactions on Biomedical
Engineering, Vol. 64, pp. 1761-1771, 2017
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O. Carr, K.E.A. Saunders, A.C. Bilderbeck, A. Tsanas, N. Palmius, J.R. Geddes, R. Foster, M. De Vos,
G.M. Goodwin: Desynchronization of diurnal rhythms in bipolar disorder and borderline personality
disorder, Translational Psychiatry, Vol. 8:79, 2018
O. Carr, K.E.A. Saunders, A. Tsanas, A.C. Bilderbeck, N. Palmius, J.R. Geddes, R. Foster, G.M.
Goodwin, M. De Vos: Variability in different diurnal rhythms is related to variation of mood in bipolar and
borderline personality disorder, Scientific Reports, Vol. 8:1649, 2018
AMoSS-Proteus
dataset
➢ “High intensity”
monitoring
➢ Mapping circadian
variability to mood
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Raw data Processed patterns
Features
Extract data Pre-process Characterise
© A. Tsanas, 2021
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Database with all participants (used to blank out participant id)
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Sleep (estimate)
Non-wear (estimate)
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Tidal Generation: Rolls-Royce EMEC 500kW
Feature Subject id
IS 0.66 ± 0.07
IV 0.01 ± 0.00
L5 0.00 ± 0.00
M10 0.16 ± 0.01
RA 0.95 ± 0.02
(results in form: median±iqr)
IS = interdaily stabilityIV = intradaily variabilityRA = relative amplitude = (M10-L5)/(M10+L5)
High IS: good zeitgeber sync☺High IV: fragmentationHigh RA: good rhythmicity☺
© A. Tsanas, 2021
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Feature 14047
IS 0.65 ± 0.09
IV 0.01 ± 0.00
L5 0.00 ± 0.00
M10 0.17 ± 0.00
RA 0.95 ± 0.04
Feature 14047
IS 0.72 ± 0.08
IV 0.01 ± 0.00
L5 0.00 ± 0.00
M10 0.17 ± 0.02
RA 0.97 ± 0.03
median±iqr median±iqr© A. Tsanas, 2021
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Psyche system (t-shirt based)Valenza et al. Wearable
monitoring for mood recognition
in bipolar disorder based on
history dependent long-term
heart rate variability analysis,
IEEE Journal of Biomedical
and Health Informatics, Vol.
18, pp. 1625-1635, 2014
Lifelogging (cameras)
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Project 2
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Physical presence in the clinic
• Cumbersome for those living in remote areas
• People need to take days off work
Patient-led self-monitoring
• Recall bias
• Subjectivity
No ground truth
• Difficult to objectively to assess intervention effects
• Inter-rater variability
© A. Tsanas, 2021
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Tidal Generation: Rolls-Royce EMEC 500kW
▪ 42 PTSD participants
▪ 43 traumatized controls
▪ 51 Healthy Controls (HC)
❖ Smartwatch (~1month)
❖ Sleep diaries + PSQI
❖ Collected before + after treatment© A. Tsanas, 2021
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© A. Tsanas, 2021
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van Hees et al. (2015) sleep
detection algorithm
Proposed sleep detection
algorithm in this study
Sleep onset Sleep offset Sleep onset Sleep offset
Dev
iati
on
fro
m g
ran
d t
ruth
(m
inu
tes)
Non-traumatisedcontrols -56±112 22.5±106 -12.5±51 2±30.25
Traumatisedcontrols -81±147 35.5±95.5 -18±50 10±46.75
PTSDparticipants -78±131.25 41.5±122.5 -34±78.25 10±45.25
A. Tsanas, E. Woodward, A. Ehlers: Objective characterization of activity, sleep, and circadian rhythm
patterns using a wrist-worn sensor: insights into post-traumatic stress disorder, JMIR mHealth and
uHealth, Vol. 8(5), e16452, 2020
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BEFORE
AFTER
© A. Tsanas, 2021
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Project 3
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Tidal Generation: Rolls-Royce EMEC 500kW
▪ 27 participants
▪ No normative data (no HC)
❖ Smartwatch (~1 month)
❖ Questionnaires (anxiety, fear)
❖ Collected before + after different treatment© A. Tsanas, 2021
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Tidal Generation: Rolls-Royce EMEC 500kW
H-Y. Chun, A.J. Carson, A. Tsanas, M.S. Dennis, G.E. Mead, W. Whiteley: Cognitive behavioural therapy
for treating anxiety after stroke (TASK): proof of concept in a streamlined randomized controlled trial,
Stroke, Vol. 51, pp. 2297-2306, 2020
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Wearables
• New algorithms
• New insights
App-based monitoring
• PROMs
• Passive data
The “invisibles”
• Electro-magnetic monitoring
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Easy to collect data often convey clinically useful information
Moving towards passively collected data
Fertile field to be developing algorithmic tools
Need for interdisciplinary collaboration to make breakthroughs
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Data from different projects
Toolboxes (Matlab code)
➢Time-series analysis
➢Actigraphy
➢ML methods
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US, Spain, Greece, Australia, UK