# Continuous Monitoring of Physiological Signals

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4 Οκτ 2013 (πριν από 4 χρόνια και 7 μήνες)

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Critical Care Bioinformatics Workshop

Sept 26th, 2009

Continuous Monitoring of Physiological
Signals

Christopher G. Wilson, Ph.D
.

Departments of Pediatrics and

Neurosciences

Disclosures….

Outline

Continuous sampling as a logistical problem

Nuts and bolts of sampling

Data takes up space!

On
-
line versus off
-
line analysis

Organizing multiple data files from the same
patient

Datafarming

Why collect all that data?

Changes in physiological signals indicate patient
state (duh!)

Without a sufficient “window” of data, you will
miss changes in patient state

Currently, staff only “acquires” charting data once
every hour or so…

Retaining a “superset” of patient data allows for
more comprehensive post
-
hoc data mining for
pathophysiologies

Potential for improved standard of care

Nyquist
-
Shannon “Criterion”

The
Nyquist

Shannon sampling theorem
is a fundamental result in the
field of information theory, in particular telecommunications and signal
processing. Sampling is the process of converting a signal (for example, a
function of continuous time or space) into a numeric sequence (a function
of discrete time or space). The theorem states:

If a function
x(t
) contains no frequencies higher than B hertz, it is completely determined
by giving its ordinates at a series of points spaced 1/(2B) seconds apart
.

This means that a
bandlimited

analog signal that has been digitally
sampled can be
perfectly

reconstructed from a sequence of samples if the
sampling rate exceeds 2
×
B samples per second, where B is the highest
frequency of interest contained in the original signal.

Analog signals are continuous…

And sampled at 2x their highest frequency…

But it’s better to sample more!

Storage space required = (# of channels)
×

(sampling rate)
×

(recording time)

If we record respiration, ECG, and Pulse
-
Ox at a
very slow sampling rate (50 samples per second).

And four channels of EEG (1000 samples per
second).

Over 12 hours of continuous monitoring we
would collect
~
200 Megabytes

of data for a
single patient!

Long
-
term Data Storage

Luckily disk storage is now very cheap
(approximately \$100/Terabyte).

However, with 100s of patients in the hospital
per year, even with only a few hours of limited
recording per patient, the data will become
prohibitive to manage locally.

Computer operating systems that can handle
large datasets in memory have only recently
become more common (32 bit versus 64 bit).

Example of Long
-
term Acquisition

Neonatal
Desaturation

Dataset

“High
-
res” pulse
-
oximetry

data: 2 second average, 0.5
samples/sec.

Desaturation

events must < 80% and be ≥ 10 seconds in
duration.

We only use 24 hour days that have < 2 hours of missing
data.

Missing SaO
2

data points are flagged with a “non
-
event”
value.

Values that are clearly “unrealistic” (equipment
malfunction, removal of pulse
-
ox) are flagged and ignored
through scripted data filtering.

We use multiple analysis algorithms on the same set of
data to extract both linear and non
-
linear information.

Artifact sources

Patient moves, dislodging the finger cuff

Patient is moved by transport to another
location

Equipment malfunction

Movement artifact

These sources of artifact can happen with any
signal source!

Patterning of
Desats

Across Patients

Data Collection

Integrating the data (II)

Integrating the data (II)

n

n

+
D

“On
-
line” versus “Off
-
line”

Things we can do on
-
line

Time
-
series plots which can include:

Raw data over time

Averaged data (“trending”)

Qualitative dynamics

Poincaré

return maps

“Windowed”
FFTs

Things we will need to do off
-
line

ApEn
, DFA, etc.

Organizing Multiple Data Sources: Our Database

Integrates all data records obtained for each
subject/patient.

The backend is
MySQL

based (
Open Source
but very
well supported with commercial options for “high
-
level” support).

Available at
mysql.org

Using an ODBC (open database connectivity)
compatible client (
MS Access
), we have developed a
graphical front
-
end for data access and management.

The database is easily extended using graphical
development tools.

Form Development

Data Collection

Data flow

Data flow

Data flow

Data Centers

Summary

Long
-
term patient data acquisition can be done
now
.

This is possible due to
relatively

inexpensive data
storage and acquisition hardware.

Currently, the majority of our data “digestion” and
analysis is done off
-
line,
post
-
hoc
.

Management of collected data using widely available
database software allows integration of patient records
and high
-
resolution waveform and imaging data.

A remaining challenge is long
-
term off
-
site storage of
patient data in secure data centers and “open
-
access”
standards across health care institutions.

Acknowledgements

Kenneth
Loparo
, PhD

Ryan
Foglyano
, BME

Kaffashi
, PhD

Julie
DiFiore
, BME

Jordan Holton, BME (major)

Bryan Kehoe, Nihon
Kohden
, USA

Our website:
http://www.case.edu/med/bioinformatics/