# Monitoring

Τεχνίτη Νοημοσύνη και Ρομποτική

14 Νοε 2013 (πριν από 4 χρόνια και 5 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 #
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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’

encouraging healthy behaviour

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Lecture 13 Slide #
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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 #
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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 #
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AM / FM

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

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Lecture 13 Slide #
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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 #
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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 #
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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 #
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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 #
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Applications

ICU (esp. PICU) monitoring

Respiration, blood glucose, etc.

classify and

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 #
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Lecture 13 Slide #
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Discussion

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

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Lecture 13 Slide #
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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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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 #
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Care plan model

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

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Model (contd.)

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Lecture 13 Slide #
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Designing a care plan in the
model

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Lecture 13 Slide #
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Care plan in the model (contd.)

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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 #
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Example interfaces

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

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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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“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 #
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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 #
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Tele
-
presence healthbot

Much more common

… and further along
toward real
-
world use

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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 #
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UoA Health Robotics Centre

Working with ETRI (Korean Robotics Institute)

robot for elder care

Combination of companion
-

ship and monitoring

capabilities

Strong emphasis on speech

interaction

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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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