Monitoring

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14 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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Lecture 13 Slide #
1

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Healthcare Decision Support Systems



Lecture 13: Monitoring


Lecturer: Prof Jim Warren

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Lecture 13 Slide #
2

Monitoring


A few different domains


Critical care monitoring


reporting back to humans
who will respond quickly


‘Ubiquitous’ monitoring


getting data (probably over
a long period of time) without being too obvious about
it


Participatory monitoring


patients get a sense of
engagement by participating in the medical record


‘Coaching’


the interaction is mostly about
encouraging healthy behaviour

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Lecture 13 Slide #
3

Critical care systems


Classic app is ECG monitoring

See http://www.nda.ox.ac.uk/wfsa/html/u11/u1105_01.htm

P
-

R interval



QRS complex
duration


Q
-

T interval
corrected for heart
rate (QTc) QTc =
QT/ RR interval

0.12
-

0.2 seconds
(3
-
5 small squares
of standard ECG
paper)

less than or equal
to 0.1 seconds (2.5
small squares)

less than or equal
to 0.44 seconds


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Lecture 13 Slide #
4

Another view of the ECG


One

heart
-

beat

Particularly want to look out for lengthening Q
-
T

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Lecture 13 Slide #
5

Amplitude, Frequency, Phase


Amplitude is ‘displacement’ (a
distance) in a physical
vibration and then is usually
transformed to an electric
current and is measured in
voltage

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Lecture 13 Slide #
6

AM / FM


Can encode signals by changing (“modulating”)
amplitude or frequency (or phase) of a carrier signal

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Lecture 13 Slide #
7

Basics of signal processing


Sampling frequency


Must take samples frequently enough


The
Nyquist

rate

is

twice the

frequency of

the highest

frequency

component

of the signal


If there’s something higher frequency, then you’ll get
aliasing



an incorrect interpretation of the signal

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Lecture 13 Slide #
8

Sampling in ECG


In ECG we have a lot of concern with interval lengths


Equipment commonly samples at 100Hz (mobile devices) to
1000Hz (high resolution)


At 100Hz, due to the Nyquist rate, you miss any high
-
frequency
features with a period of less than 0.02s (i.e., 20ms)
(Period = 1
/ frequency)


Moreover, at 100Hz, you can be up to 10ms late in seeing a rise
or fall, and thus up to 20ms inaccurate in estimate of an interval


Sampling requirements (now talking ECG or other apps)
put demands on


the speed of your equipment to process


the bandwidth of your transmission (esp. in telemonitoring)


the size of your database (esp. for long
-
term monitoring)

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Lecture 13 Slide #
9

Signal classification


Algorithms can classify signals based on
features

of the
signal


Might be straightforward (e.g., time between lowest and highest
amplitude


but keep in mind all those sampling errors!)


Signal can be mathematically transformed


Fourier transform


transforms from amplitude over time
-
>
amplitude over frequency


We can then extract features from the transformed signal


Classifiers can then use whatever machine learning
methods


Multiple regression, artificial neural networks, induced decision
trees, etc.


Can classify the ‘system’ (e.g., the patient’s heart) as being in
any of a variety of states


And you can layer symbolic reasoning (production rules) and
fuzzy logic on top of the signal
-
feature
-
based classifiers

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Lecture 13 Slide #
10

Fourier transform results


A sine wave is the pure ‘spike’ once Fourier
transformed


Square waves

and pulses

make more

complex

patterns

Time domain Frequency domain

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Lecture 13 Slide #
11

Markov model


Based on the ‘memoryless’ (or Markov) property
(“M” either way!)


Your previous states say nothing; only need to think
about current state and probability/rate of progression
to other states from there

e.g., P(B
t+1

| A
t
) = 0.9

Can describe the system with a square
matrix, NxN, where N is the number of
states

Again, only accurate if the system is
memoryless with respect to those states

Can use a series of low probability
transitions to indicate that the system
has changed (and throw an alert)

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Lecture 13 Slide #
12

Applications


ICU (esp. PICU) monitoring


Respiration, blood glucose, etc.


classify and
alert on changes


Worn heart monitors


http://www.nlm.nih.gov/medlineplus/news/fullstory_64123.html


Also, worn accelerometers for falls detection


‘Smart’ homes


Monitor usage patterns of lights, water,
refrigerator etc. and also track motion

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Lecture 13 Slide #
13

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Lecture 13 Slide #
14

Discussion


Have you experienced any good (or not so
good) automated monitors?

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Lecture 13 Slide #
15

Participatory Home Telemedcare


Home ECG, lung function, blood
oxygen saturation, glucose, weight, BP


All with feedback so patient sees their
state and their progress


Can, for instance, learn to deal with an
asthma attack (possibly on phone to
nurse) without called ambulance

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Lecture 13 Slide #
16

Reminders, life coaches


STOMP


txt messaging to quite smoking


“chewing gum for the fingers”


automated ‘friend’ to

txt when

craving


Plus staged

supportive

messages

and

monitoring


Significant

quit effect

(Maori and

non
-
Maori

at 6 months


Other obvious apps are exercise coaches, drug administration
reminders and (esp. w. video phones) guides (e.g., for insulin dosing
or nebulizer spacer technique)


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Lecture 13 Slide #
17

What is a ‘care plan’ anyway?


Fundamental to monitoring or health
promotion should be the notion of the
care
plan

for a patient


What are our objectives (specified as goals
and target values)
?


What interventions do we have in place to
achieve those objectives?


How often do we monitor status?


When do we plan to re
-
plan?

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Lecture 13 Slide #
18

Care plan model


We’ve created an information model for
care plans
(Khambati, Warren, Grundy and Hosking)

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Lecture 13 Slide #
19

Model (contd.)


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Lecture 13 Slide #
20

Designing a care plan in the
model


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Lecture 13 Slide #
21

Care plan in the model (contd.)


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Lecture 13 Slide #
22

Automated interface
generation


We’ve prototyped a
process for generating
multiple user interface
implementations for an
individual care plan
around the care plan
model

Model a care plan
using the care plan
visual language
Guideline Implementer
Instantiate the care plan
template for a patient
Provider (e.g., GP)
Care Plan Template
Care Plan Instance
Model a suitable visual
-
isation
for representing a
care plan on a specific
device
User Interface
Programmer
Care Plan Visual
-
isation
Definition
Generate an application
for a user to
visualise
a
care plan instance
Visualisation
Generator
OpenLaszlo
script
representing end
-
user application
Create
runnable
application
OpenLaszlo
Compiler
Shockwave Flash
Objects
DHTML
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Lecture 13 Slide #
23

Example interfaces


Part of a diabetes monitoring care plan being tailored in
our care plan instantiation application

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Lecture 13 Slide #
24

Example interfaces


End
-
user Flash application compiled from OpenLaszlo

Auto
-
generated interfaces are still a bit
basic, but better than nothing

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Lecture 13 Slide #
25

“Your plastic pal that’s fun to be with”


Healthcare robots (or healthbots) are being
considered to supplement human personnel


Particularly in low
-
intensity monitoring situations such
as aged care


‘Robot’ is from a Czech word for ‘to work’


But many practical robots are actually more focused on being
mobile sensor platforms and computer terminals


Real work robots are possible when fixed to an automotive
assembly line, but not yet practical for dealing with people


Which doesn’t mean the Japanese aren’t trying…

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Lecture 13 Slide #
26

Robots that can lift and carry


Japanese

RI
-
MAN
(incidentally,
that’s a doll
it’s lifting)


still highly
experimental

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Lecture 13 Slide #
27

Tele
-
presence healthbot


Much more common

… and further along
toward real
-
world use

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Lecture 13 Slide #
28

Robots for companionship


Gladys Moore, a resident at the
NHC Healthcare assisted
-
living
facility in Maryland Heights,
Missouri, plays with AIBO, a
robotic dog, in this undated
handout photo. Researchers
found that the robot dog was
about as good as a real dog at
easing the loneliness of
nursing home residents in a
study.

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Lecture 13 Slide #
29

UoA Health Robotics Centre


Working with ETRI (Korean Robotics Institute)


Looking at adapting an inexpensive

robot for elder care


Combination of companion
-

ship and monitoring

capabilities


Strong emphasis on speech

interaction


More autonomous adjunct to

human healthcare workers, rather

than for tele
-
presence


Possibly supplement other

smart home equipment

Ultrasonic sensors to avoid bumping into
things

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Lecture 13 Slide #
30

Summary


Monitoring is a major class of health IT activity


It leads to the embedding of sometimes non
-
trivial
artificial intelligence in devices (often with reliance
on traditional signal processing)


Monitors may be overt or ubiquitous


They may engage the consumer


In fact, engaging the consumer may be the main point!


Monitoring implies the knowledge engineering of
guidelines