THE DEVELOPMENT OF A BEDSIDE DISPLAY FOR THE ICU
By
YAWEI SUN
Submitted in partial fulfillment of the requirements
For the degree of Master of Science
Thesis Advisor: Professor Kenneth A. Loparo, Ph.D.
Department of Electrical Engineering and Computer Science
CASE WESTERN RESERVE UNIVERSITY
May 2014
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of
Yawei Sun
Candidate for the
(signed)
Master of Science
degree *.
Kenneth A. Loparo, Ph.D
(Chair of the committee)
Farhad Kaffashi, Ph.D
Frank Jacono, Ph.D
(date)
5/27/2014
*We also certify that written approval has been obtained for any
proprietary material contained therein.
1
Table of Contents
1
Introduction ........................................................................................ 9
2
Data Structure Analysis of EDF file ............................................... 11
2.1
Introduction .............................................................................................................. 11
2.2
Background ............................................................................................................... 11
2.3
Data Structure for EDF file....................................................................................... 14
2.3.1
Basic Structure for EDF .................................................................................... 14
2.3.2
Data Analysis of EDF in MATLAB ................................................................. 16
3 Basic Ideas for the GUI of the ICU ................................................. 22
3.1 Introduction ................................................................................................................. 22
3.2 Components of the GUI .............................................................................................. 22
3.3 Data Selection ............................................................................................................. 23
3.4 Plot .............................................................................................................................. 24
3.5 Time setting ................................................................................................................ 25
3.6 Information ................................................................................................................. 25
3.7 Innovation Ideas .......................................................................................................... 26
3.7.1 Poincare Plot ........................................................................................................ 26
3.7.2 Histogram............................................................................................................. 28
4 Innovation GUI Development and Analytic Information
Extraction ................................................................................................ 30
4.1 Introduction ................................................................................................................. 30
4.2 Related Prior Work ..................................................................................................... 31
4.2.1 Development environment ................................................................................... 31
4.2.2 Frame Design ....................................................................................................... 31
4.2.3 Data processing .................................................................................................... 32
4.3 Realization of GUI ...................................................................................................... 36
4.3.1 Data Selection ...................................................................................................... 36
4.3.2 Plot and Time setting ........................................................................................... 38
2
4.3.3 Information .......................................................................................................... 42
4.3.4 Innovation: Poincare Plot and Histogram ............................................................ 47
5 Real Time Analysis Mode Development ......................................... 55
5.1 Introduction ................................................................................................................. 55
5.2 Realization of Real Time Analysis Mode ................................................................... 55
5.3 Discussion and Future work........................................................................................ 60
Appendix .................................................................................................. 62
Bibliography ............................................................................................ 66
3
List of Figures
Figure 2-1: Spacelabs 90396 monitor ................................................................ 12
Figure 2-2: Numeric Data from Spacelabs 90396 monitor ............................. 13
Figure 2-3: Philips Intellivue MP5 monitor ...................................................... 14
Figure 2-4: Header record structure for EDF file in MATLAB ..................... 16
Figure 2-5: The label of each channel of EDF file in MATLAB ..................... 17
Figure 2-6: The TransType of each channel of EDF file in MATLAB ........... 18
Figure 2-7: The Unit of each signal of EDF file in MATLAB ......................... 18
Figure 2-8: The Physical Min of each signal of EDF file in MATLAB .......... 19
Figure 2-9: The Physical Max of each signal of EDF file in MATLAB .......... 19
Figure 2-10: The Dimension Min of each signal of EDF file in MATLAB .... 20
Figure 2-11: The Digital Max of each signal of EDF file in MATLAB ........... 20
Figure 2-12: The number of samples in each data record of EDF file in
MATLAB ..................................................................................................... 21
Figure 3-1: Poincare plot for a normal person ................................................. 28
Figure 4-1: Simplified frame of innovation GUI .............................................. 32
Figure 4-2: Data Selection for ECGL vs. ECGR .............................................. 36
Figure 4-3: Data selection for HR vs. Cannula Flow ....................................... 37
Figure 4-4: Time vs. Cannula Flow ................................................................... 37
Figure 4-5: Regular Data Comparison.............................................................. 38
Figure 4-6: Legend of plot .................................................................................. 39
Figure 4-7: Time Gap1 and Time Gap2 to change the time interval between
each data ...................................................................................................... 40
Figure 4-8: Change the Data Length of each period by setting WindowLength
....................................................................................................................... 41
Figure 4-9: Information of Mean value and Standard Deviation of x axis and
y axis ............................................................................................................. 43
Figure 4-10: Mean value of Distance, SD of Distance, and cluster selection . 44
Figure 4-11: Detect Respiration rate by setting a threshold for Time vs.
Cannula Flow .............................................................................................. 45
Figure 4-12: Abnormal Respiration Rate, the value of Cannula flow is too low
....................................................................................................................... 46
Figure 4-13: Poincare´Plot of Heart Rate ........................................................ 48
Figure 4-14: Low Frequency Power and High Frequency Power of the Heart
Beat ............................................................................................................... 50
Figure 4-15: Poincare´plot: Respiration Rate vs. Respiration Rate (1 sample)
....................................................................................................................... 50
Figure 4-16: Histogram for SaO2 vs. HR ......................................................... 51
Figure 4-17: Conventional SaO2 vs. HR scatterplot ........................................ 52
Figure 4-18: Histogram of SaO2 vs. SaO2 ......................................................... 52
Figure 5-1: Set the Path selection for users ...................................................... 56
Figure 5-2: Real Time Display of ECG(V1) and ECG(LL) signals(Ideal) ..... 57
Figure 5-3: Real Time Display of ECG(V1) and ECG(LL) signals (with
4
Artificial Points) .......................................................................................... 57
Figure 5-4: Switch to Analytic mode, ECGR vs. ECGL .................................. 58
Figure 5-5: Analytic mode, HR vs. ECGR ........................................................ 58
Figure 5-6: Switch Back to Real Time Analysis mode ..................................... 59
Figure 5-7: Stop the program ............................................................................ 59
Figure 5-8: Saved Data ....................................................................................... 60
5
List of Tables
Table 2-1 : Physical Min and Max, Digital Min and Max ............................... 21
Table 4-1 : Physical Min and Max, Digital Min and Max ............................... 33
6
Acknowledgements:
I would especially like to thank Dr. Kenneth Loparo for all of his guidance and all the
support throughout my graduate career. Additionally, I would also like to thank the
other members of my defense committee Dr. Frank Jacono and Dr. Farhad Kaffashi
for taking their time to read and provide feedback. Finally, I must thank Dr. Farhad
Kaffashi for all his assistance in understanding the data structure, set up of the GUI
and for answering so many questions along the way.
7
The Development of a Bedside Display for the ICU
Abstract
by
YAWEI SUN
In this thesis, an innovative Graphical User Interface (GUI) for the next
generation of bedside decision-support systems for the Intensive Care Unit (ICU) is
developed. Functions of existing monitors in the ICU are integrated into a single
bedside system. The bedside monitor is capable of visualizing real-time data
streaming from a patient monitor, performing routine and novel signal analytics and
also reading archived patient waveform data in European Data File (EDF) format.
Signals such as Electrocardiograph (ECG) waveform, Heart Rate (HR), Oxygen
Saturation (SaO2), etc. are available for visualization and analysis. Selection for three
different period time data with flexible data length is also created. Novel analytics
including Poincare´and histogram plots are available to investigate the variation and
connections of different physiological signals.
8
1 Introduction
In recent years, we have witnessed a significant increase in the instrumentation
that is available in different hospital units, especially in the care of critically ill
patients in the intensive care unit [1] where a variety of devices are used to monitor
patient status. The bedside monitor developed in this work is intended as a tool
managing the collection, integration, and visualization of all types of clinical data.
The data originates from four major sources, (1) bedside medical devices, (2) local
area network (LAN) of the ICU, (3) hospital information system (HIS) and (4) manual
input [2]. All these sources are remarkably different in both the quality and quantity of
data. However, the issue of data overload is the main problem for health care
professionals making it difficult to make a decision. The massive clinical data from
bedside medical devices with limited processing and analytics not only contributes to
the data overload problem, but also increase the possibility of medical errors. In this
thesis, we develop a bedside monitor that can get data in real-time, provide data
visualization and analytics go support clinical decision-making at the bedside.
Maintaining a balance between useful data and the limited space available for
data display on a bedside monitor is a challenging question for improving the
efficiency of health care. Further, providing a rich database and custom analytics to
different users requires new concepts for data presentation and manipulation to
support better clinical decisions [3]. The conventional patient monitor used in the ICU
displays real-time signal plots, e.g. ECG, SaO2 and Non-Invasive Blood Pressure
(NIBP) waveforms, as well as numeric such as HR and ABP. Raw data is not acquired
at high sampling rates or archived to facilitate either real-time or off line analysis.
This, traditional patient monitors interface only provide limited information, with no
capability for even the simplest data analytics.
In this thesis, we develop a bedside monitor that includes two panels for users to
select different types of data displays, including both time and scatter plots, and also a
variety of signal analytic methods that can be used for trend analysis to determine
9
when a change in patient state may have occurred. Users can select three separate
1-minute (default) data segments for comparative purposes and to facilitate further
analysis, raw ECG data is to compute heart rate by detecting the R to R intervals and
respiratory rate is determined from Cannula flow.
The bedside monitor also includes some novel analytics, including Poincare´and
Histogram plots. The Poincare´plot is a method to analyze the variability of a time
series, usually between consecutive data samples. When we select the same signal for
both the x-axis and y-axis data, the Poincare´plot will be activated. Using a single
sample delay, the traditional Poincare´plot of the data is generated. We can visualize
the changes in signal in consecutive or nonconsecutive time points. To quantify the
variability, we compute SD1C (short term) and SD2C (long term) measures of
variability by estimating the minor and major axes of a hypothetical ellipse that
contains the scatter plot. We also include another measure of heart rate variability
using the Lomb Periodogram for unevenly sampled time series data to calculate the
Low Frequency Power (LPF) and High Frequency Power (HPF). We use the
histogram to investigate the (distributional) connection between two different signals,
instead of the cross-correlation hat is only intended to quantify the degree of linear
dependence. The corresponding distribution of a signal is investigated by setting the
high-low limits of the other signal. Specifically, when we set the limit for HR from 85
to 120, the corresponding distribution of SaO2 in this one-minute when the HR is
from 85 to 120 will be shown in the figure.
The real-time analysis tools allow the user to evaluate the current status of the
patient. For example, two leads of ECG signals can be displayed and the detected
heartbeats can be shown directly on the ECG signals. Simultaneously, we compute the
instantaneous HR and Standard Deviation of the HR along with other relevant
parameters from the Poincare´analysis such as SD1C, SD2C, as well as LPF, HPF
and LPF to HPF ratio. All this information is updated every 10 seconds, and users are
able to switch the mode by selecting a corresponding checkbox as well as save parsed
data into a file for later review and analysis.
10
2 EDF file format
2.1
Introduction
Chapter 1 presented the basic concepts behind the bedside patient monitor GUI,
next we will introduce a variety of different formats and data structures for clinical
data such as .xls, .m, .txt, and .edf files. Research groups commonly use .xls and .m
file formats for statistical analysis and importing data to Matlab for algorithm
development and testing, respectively. Several companies provide output files in .txt
format, for example, numeric data from the Spacelabs 90396/90496 monitor. The
EDF file is a common file format for physiological data. The information in an EDF
file is included in two separate parts, the header record and the data records. The
header record consists of the version number, local patient ID, start date, start time,
number of records, and number of signals. For the data records, it includes Labels,
TransType, PhyDim, PhyMin, PhyMax, DiMin, DiMax, PreFiltering, nr, and
Reserved. All this information is used to properly interpret the data that is included
with the EDF file.
2.2
Background
In the last few years, a large variety of commercial and specializedl
computer-based systems have been used for automatic analysis and archiving of ECG
and EEG (electroencephalograhpy) recordings. These systems are based on different
protocols and different formats to store a large amount of polygraphic signal data. The
large variety of different formats slows down the improvement of the ECG signal
analysis algorithms because of the difficulty in translating data formats across
different computational platforms. For instance, some engineers save the ECG data
in .xls format, while others store complete polygraphic data as .m files, a file format
for MATLAB. These formats for the storage of polygraphic signals are seldom used in
11
a clinical setting because the data structure is too complicated. More often, medical
companies such as Spacelabs Healthcare save the data into a .txt file. Figure 2-1
shows the Spacelabs 90396 monitor. Figure 2-2 shows the numeric data we obtain
using Hyperterminal from the Spacelabs 90396 monitor.
Figure 2-1: Spacelabs 90396 monitor
12
Figure 2-2: Numeric Data from Spacelabs 90396 monitor
The European Data Format (EDF) file format is often used to store digital
polygraphic recordings. The EDF is a flexible format for exchange and storage of
multichannel biological and physical signals [4]. A few European ‘medical’ engineers
who met at the 1987 international Sleep Congress in Copenhagen developed the EDF
format, which simplifies the complication of using a variety of formats to store and
exchange digital polygraphic recordings. In our efforts to develop the Integrated
Medical Environment (tIME) we using the EDF to archive all data from ICU monitors.
Specifically, we are acquiring hing resolution physiological signal data from the
Philips Intellivue MP5 patient monitor through a custom acquisition system that
communicates with the monitor through the serial port. The signal data is then
archived in EDF. Figure 2-3 shows the Philips Intellivue MP5 monitor.
13
Figure 2-3: Philips Intellivue MP5 monitor
2.3
Data Structure for EDF file
2.3.1 Basic Structure
An EDF file consists of two parts, a header record and data records. The length
of the header record is dependent on the number of signals and identifies the patient
and specifies basic information such as StartDate, StartTime, NumberDataRecord,
and SignalNumbers. The data records includes the details of different signals.
2.3.1.1 Header Record
The header record can be separated into two parts, the first 256 bytes contain the
version number, local patient ID, local record ID, date and time information, number
of data records (nr), and the number of signals (ns). The second part includes 256
14
bytes for the detail of each signal such as number of records in a fixed time duration,
which can be used for computing the sampling rate. The following shows the identity
attributes of the header record:
First part: Basic information of the file
8 ascii: version of this data format (0)
80 ascii: local patient identification
80 ascii: local recording identification
8 ascii: start date of recording (dd.mm.yy)
8 ascii : start time of recording (hh.mm.ss)
8 ascii : number of bytes in header record
44 ascii : reserved
8 ascii : number of data records (-1 if unknown)
8 ascii : duration of a data record, in seconds
4 ascii : number of signals (ns) in data record
The identifying information included in the first part shows the Patient ID which used
for identify the patient. The data and time information can be used in the bedside
monitor to set the time point. The duration of a data record shows the time of each
record. After the file attributes part, the header record contains the detailed
information of each channel signal.
Second part: Detail of each signal channel
ns * 16 ascii : ns * label (e.g. EEG FpzCz or Body temp)
ns * 80 ascii : ns * transducer type (e.g. AgAgCl electrode)
ns * 8 ascii : ns * physical dimension (e.g. uV or degreeC)
ns * 8 ascii : ns * physical minimum (e.g. -500 or 34)
ns * 8 ascii : ns * physical maximum (e.g. 500 or 40)
ns * 8 ascii : ns * digital minimum (e.g. -2048)
15
ns * 8 ascii : ns * digital maximum (e.g. 2047)
ns * 80 ascii : ns * prefiltering (e.g. HP:0.1Hz LP:75Hz)
ns * 8 ascii : ns * nr of samples in each data record
ns * 32 ascii : ns * reserved
2.3.1.2 Data records
The data records contain continuous fixed length data for each of the signals, the
length of each period depends on the total the number of samples in each data record.
2.3.2 Exploring the EDF in MATLAB
To more clearly understand the meaning of each of the attributes, we use the
following example in MATLAB:
Figure 2-4: Header record structure for EDF file in MATLAB
In this example, the information we could get from this figure is that the patient
16
ID is ‘BI0313’. The start date and time are 18/05/04 and 19:33:03, and the number of
records is 38610. Duration is 1 second, and signal numbers is 22. Additionally, the
HeaderNumBytes is computed by the first part of header record 256 bytes plus 22
channels multiplied by 256 bytes for each channel, 5888 = 256+22 x 256.
The detailed information for the channels is shown below.
Figure 2-5: The label of each channel of EDF file in MATLAB
The Labels contains the label of each signal in each channel, in this EDF file it
contains 2 ECG signals, HR, SaO2, Cannula Flow, Airflow, 7 EEG signals.
17
Figure 2-6: The TransType of each channel of EDF file in MATLAB
The Info.ChInfo.transtype contains the source of each channel. For instance, the
ECG L signal and ECG R signal are coming from ECG.
Figure 2-7: The Unit of each signal of EDF file in MATLAB
18
The Info.ChInfo.PhyDim includes the physical units of all signals. Combined
with Physical Min and Max, Dimension Min and Max, we could compute the actual
value of each corresponding signal.
Figure 2-8: The Physical Min of each signal of EDF file in MATLAB
Figure 2-9: The Physical Max of each signal of EDF file in MATLAB
19
Figure 2-10: The Dimension Min of each signal of EDF file in MATLAB
Figure 2-11: The Digital Max of each signal of EDF file in MATLAB
The physical min and max provides information on the range of the real values
for each signal. For example, for the ECG signal, the unit is mV, and the physical
minimum and maximum are -1.000 and 1.000. The digital range is from -32767 to
32768, so the actual ECG data (mV) is related according to: ActualData =
20
EDFData/2^15.
Table 2-1 : Physical Min and Max, Digital Min and Max
Digital Min
Digital Max
Physical Min
Physical Max
Unit
ECGL
-32768
32767
-1.00000
1.0
uV
ECGR
-32768
32767
-1.00000
1.0
uV
HR
-32768
32767
0
200.0000
BPM
Airflow
-32768
32767
-1.000000
1.00
uV
SaO2
-32768
32767
0
100.00
%
Cannula Flow
-32768
32767
-0.50000
0.5000
V
Figure 2-12: The number of samples in each data record of EDF file in MATLAB
The number of samples (ns) in each data record along with the signal duration
(e.g. 1 second) provides information for computing the sampling rate of each signal.
21
3
The Bedside Patient Monitor
3.1
Introduction
The bedside monitor can operate in two modes, using archived or real-time data.
In this chapter, we discuss operation of the bedside monitor using data archived in
EDF. We begin by discussing the major components of the GUI. Specifically, the
three main components consist of Patient Identification, Time information, data
information. The data information includes data input, data selection, data display.
There are a variety of different ways to select data for visualization and analysis
including edit boxes, pop-up menus, radio buttons, and button groups. After
comparison among these choices, button groups and pop-up menus were chosen.
Next we need to decide on the format for data visualization including waveform
(time series) and numeric data, as well as how the data is annotated to make it easier
to understand and interpret by the user.
A key feature of the bedside monitor is to support analytic computations that
transform the data to actionable information for patient care at the bedside.
Conventional numeric information is displayed, along with other statistical and trend
information like mean and standard deviation over selectable time windows.
Statistical computations such as the mean value and standard deviation can help
physicians have a better understanding of variations in patient data.
Finally, we will discuss some innovative ideas for the display, including the
Poincare´plot, and of histograms that provide a way to visualize the relationship
between two different data sources.
3.2
Components of the GUI
The GUI components of ICU bedside monitor includes three parts: Patient
Identification, Time Information (actual time), and Data Information including data
22
selection, plotting, setting time, data values, etc. The patient ID and time information
are very basic and important components of the ICU bedside system and the data
information is essential for visualization and analytic applications. The data
information can also be grouped into three parts: data input, data selection, and data
display. For archived applications the data input is the source file, (EDF) and for
real-time applications the data input is the data packets that are received from the
patient monitor and devices in the ICU. For data selection, there are many ways in
MATLAB for users to select the data they want. However, it is not easy to find the
best way for users to choose data. We will discuss this in 3.3. In addition, for the GUI
of the ICU, the basic idea is to efficiently show as much data and useful information
as possible to the users. How to display the data in a more comprehensive way is a
challenge for the next generation ICU bedside monitors. Basically, we separate the
data display component into 4 parts, plotting, setting time, information display, and
advanced features. All of these will be discussed in the remainder of this chapter.
3.3
Data Selection
There are many ways in the MATLAB for users to select data. Mainly, edit text,
checkbox, radio button, pop-up menu, list box, table, and button group are all possible
options. The method for data selection should be convenient for all clinical personnel
who will be interacting with the ICU patient at the bedside. We also need to consider
the range of possible information that may useful to physicians for patient care, so we
should also provide a comprehensive selection for all types of data with supporting
analytics.
Although the radio button, checkbox, button group are similar, the button group is
designed for a group of radio buttons and it is more convenient to the use button
group for comprehensive data selection. On the other hand, we need also need to find
the most convenient way to the select data we want, so we choose the pop-up menu.
Finally, we decided to use both the button group and pop-up menu for data selection.
23
3.4
Plotting
Plotting is one of the main components of any interface, and is the most direct
way to show data and information to users. Due to the large amount of numeric and
waveform data, it is difficult to find a way to present all the data that is available. So,
our goal is to find a suitable method to display useful data and analytic information to
users. Waveform signals such as ECG, EEG consume large amounts of storage,
several hours data could be hundreds of megabytes, so we need to consider every
possibility to accelerate the speed of accessing and plotting archived data. MATLAB,
provides both scatter and plot functions. But if we look into both of them, we find that
plot is usually to plot lines that slow down the speed, so in this situation we decide to
use the scatter plot option. To present more details about the data, we add ‘legend’
function into our code to show the color and time range of the data. Another problem
is that the ‘legend’ function is time consuming. The average time for each call of the
‘legend’ function is almost two seconds. To solve this problem, we directly change the
attributes of the GUI so that we only need to call the legend function at initialization.
In addition, the ‘linspace’ function has been used to generate linearly spaced
vectors that can define gradual changes of color. Gradual color changes of data points
can be used to represent different time windows in which the data was collected,
thereby providing a visually intuitive way of comparing different data characteristics
during distinct time periods. In this way, users could see the trend of data through the
changes in color, which should have a profound influence on bedside data
interpretation and analysis.
Also, in order to increase the utility of the bedside display for clinical personnel
for the analysis of patient status, we provide a comparison window where 3
one-minute epochs of data can be visualized simultaneously. This will assist in
decision support at the bedside where significant changes data epochs over time, can
be more easily quantified.
24
3.5
Setting time
From Chapter 2, the EDF header contains information on start date and start time
of the data recording. With these information, we could set the time period of the file,
and then the start and end times could be computed directly (add the number of
records multiplied by the duration of each record to the start time). Another problem
for setting the time is that it may be necessary to determine the exact time associated
with a given data point, or different sets of patient data. We use a slider to the user in
selecting the time points of interest. The slider can directly be moved to see trends in
the data, and if there are specific features of interest, they can be identified quickly
and easily to support clinical decision-making. Also, add an edit text tool for users to
input an exact time point that may be of interest. The combination of slider and edit
box improves the flexibility and usability of the interface.
3.6
Information extraction and display
After we setup the data selection, time setting, and plotting, the analytic
information to be extracted and displayed needs to be determined. Conventional
patient monitors show waveforms and numerics, and these are also incorporated as a
basic feature of our bedside display. As new innovative features of a bedside display,
we also need to display patient information extracted from univariate and multivariate
data that support clinical decision-making.
Simple statistics, such as the mean value of data, can be very useful for a first
glimpse of tends in data. Widely used ICU monitors such as Philips Intellivue and
Spacelabs, are not able to provide the mean value of any data stream to a user. This is
a significant disadvantage of these monitors. The mean value can be useful both for
clinical decision-making as well as alarm processing. Additional computations, such
as the standard deviation (SD) can also provide useful clinical information as the
variability in a physiological variable (compared to its mean trend) over given periods
of time. A low SD indicates that the data points tend to be very close to the mean
25
value, and a high SD indicate that the data points are spread over a large range of
values. Specifically, if the mean value of patient heart rate is 110 bpm, but the SD is
20, that potentially is an indicator of an unstable situation. Note, it is also important
the ability to visualize a histogram of the data to understand the context in which to
interpret the SD. The SD of a time series over a window of N points is calculated by
the formula:

1
[(x1   )2  (x 2   )2    (x N   )2 ]
N
Where,  is the standard deviation,  is the mean value of data
The bedside monitor includes computations of the mean and standard deviation
for x-axis and y-axis data.
3.7
Advanced features
The ideas and improvements we discussed above are basic improvements, and the
next generation bedside monitors needs to have options for extracting more analytic
information from the available data. As an initial starting point for our developments,
we integrate the calculations and display for Poincare´and Histogram analysis.
3.7.1
Poincare´analysis
The Poincare´plot, as an approach to measure Heart Rate Variability (HRV), is a
scatter plot of the current R-R interval plotted against the preceding R-R interval.
Specifically, it is the plot of Heart Beat vs. Heart Beat + 1 sample delay. The
Poincare´plot is also referred to as a scattergram, return or phase delay map, and
Lorenz plot. Poincare´plot analysis is a simple quantitative technique compared to
more conventional computations such as the fast Fourier transform (FFT) [5], [6]. The
Poincare´plot for HRV presents both summary information of the heart rate and
details of the behavior of instantaneous beat-to-beat variability [7]. Points above the
line of identity are R-R intervals that are longer than the preceding R-R interval. On
26
the contrast, the points below the line of identity indicate R-R intervals shorter than
the previous. Usually, the Poincare´plot looks like an ellipse. Thus, the dispersion of
point’s perpendicular to the line of identity reflects the level of variability along the
minor axis, and the points along the line of identity represents the variability along the
major axis [8].
To quantify the geometry of the Poincare´plot, we use model in Tulppo et al [9]
by defining two standard descriptors of the plot, SD1C and SD2C, that are measures
of the minor axis and major axis of the ellipse. The description of SD1C and SD2C
given by Brennan et al [10] shows that these standard descriptors guide the visual
inspection of the distribution. The Poincare´plot represents useful information of R-R
interval data by presenting the variations over both short and long time intervals [9]
[10]. SD1C stands for the Standard Deviation of the short-term variability of the data
and SD2C represents the Standard Deviation of long-term variability. The point where
both axes intersect is the total mean of the R-R intervals. Figure 3-1 shows the
Poincare´plot for HRV of a normal person.
27
Figure 3-1: Poincare plot for a normal person
In conclusion, the Poincare´plot of HR is a scatter plot that represents the current
R-R interval against the next R-R interval (+ 1 sample), and can be easily extended to
other times series data, e.g. as respiratory data. This will be illustrated in Chapter 4.
3.7.2
Histogram
The Histogram is not a new idea for visualizing the distributional characteristics
of univariate time series data. We provide this functionality along with the ability to
investigate the interrelationship between two time series using a “conditional”
histogram.
The basic idea for the conditional histogram is to define a range of data for one
28
time series data, and then according to the time windows that are associated with this
range find the relevant data of another time series at these corresponding time points.
Then build a histogram that combines the information of the two different time series
data. For example, if we set the limits for Heart Rate from 80 – 120 bpm, then in the
time period (1-minute by default), we could find all the heart rate (R-points) that
correspond to HR from 80-120. According to the index of the associated R-R intervals,
we could also find the corresponding values of SaO2 when HR is in the indicated
range. The histogram will show the range of SaO2 when the HR is from 80-120 during
this period. The heights of the bins of the histogram indicate the distribution of SaO2.
In conclusion, we extend the histogram concept to combine two different timer
series data for display and analysis. This could be a very useful tool for determining
the interrelationship between physiological data streams, and providing “correlative”
information that would be very difficult to extract in any other way. In our current
implementation, the conditional histogram is limited to instantaneous heartbeat and
SaO2 because these data have the same sampling rate. Future work will develop
algorithms that can be used for data streams with different sampling rate. The
sampling rate of different signals is listed in appendix.
29
4 GUI development
4.1
Introduction
In Chapter 2 and Chapter 3, we discussed the structure of the data source file and
the basic idea of the GUI design for the ICU. In this Chapter, we will build a fully
functional GUI for the ICU in the MATLAB.
To begin the design, we will first build a basic frame for the GUI. The prior work
about the data processing will be done before we build the GUI. Details about the file
loading and seeking corresponding data points will be illustrated. EDF file attributes
such as Physical Min, Physical Max, Digital Min, Digital Max, are needed to properly
display and analyze the data in the bedside monitor GUI.
The data selection part is created by adding two panels to fit the radio button
group for the data selection by users. The signals include Airflow, Cannula flow,
ECGL, ECGR, HR, and SaO2. Others can be easily added in the future. The time
selection for x-axis is added to provide a convenient method for visualizing trend data.
Also, two static text boxes are added next to the button group to more directly show
the label of selected signals.
Next, plot and time setting will be discussed together. The legend function will be
used to show the details of each signal. Slider and time point input have been
developed for users to both conveniently change the time and choose an exact desired
time point. In addition, GAP1 and GAP2 parameters are introduced so that users can
change the time interval between each period of data. Now, users could choose three
separate time period of data to show on the plot. Also, users are able to change the
length of the time period by changing the Window Length. This provides a more
flexible method for users to choose the best time period for different data.
Additionally, the “data cursor” function is implemented to show the time of an event
and exact value for a certain point by clicking on the figure.
Then, we add the information part for various analytic computations. Average
30
value of x-axis and y-axis data, and standard deviation of each axis data are calculated.
Furthermore, we compute the average distance of each point to the central point of
each cluster and calculate the standard deviation of these distances. Thus, we could
provide measures of variability for different types of data. The respiration rate is
detected by setting a threshold for the cannula waveform flow. Some abnormal
situations are discussed for low respiration rates.
Finally, we add advanced data analytics and visualization capability to the GUI.
The Poincare´ plot is developed to provide one approach to visualizing and
quantifying variability in time series data. SD1C and SD2C measuring the standard
deviation along the minor axis and major axis of the data are calculated. Moreover,
low frequency power and high frequency power for R-R interval time series data are
also computed. The histogram and conditional histogram are used to show the
distribution of signals and combinations of signals. The synchronization of data
sources will also be discussed.
4.2
Related Prior Work
4.2.1
Development environment
The innovation GUI discussed in this chapter is developed with MATLAB,
version R2012b. The environment of the computer for this project is on Windows 7
Premier Service Pack 1 with Interl(R) Core(TM) i5-2520M CPU @2.50G Hz and 16
GB memory, 64 bit OS.
4.2.2
Frame Design
Based on the concept indicated in Chapter 3, we design a simplified frame to
guide our design. and we test the functionality with basic .xls files that only contain
several hundred Kb of data. Figure 4-1 shows the basic frame design.
31
Figure 4-1: Simplified frame of innovation GUI
In this frame, we display the patient name on the top left for identification, and
we use a static text box to show the current time on the top right of the display. There
are two panels at the bottom and left hand side of the interface for the button group
for data selection for both x-axis data and y-axis data. Also, when users choose one
kind of data in the panel, the data they choose will be shown in the static text next to
the panel. The plot display includes three different colors of data to show three one
-minute epochs of data simultaneously for comparison. We also add time point
selection and a slider to both conveniently and exactly change the time to show the
difference between signals. Finally, we add regular data comparison part different
combinations of signals can be chosen, this will be the very convenient for users.
Additional advanced features will be discussed in the next chapter
4.2.3
Data processing
Archived clinical data is stored in EDF format and each file will contain 24 hours
of data, and will be hundreds of megabytes in size. In previous tests, our algorithm
transformed the .xls file into .mat file before the data was loaded to increase the speed.
The .mat format is a binary file format for storing variables in MATLAB. Converting
data to this format can improve the speed of accessing the data in the MATLAB
32
environment. For large data files, like the EDF of interest in this work, this speed
improvement may not be realized because of the time it would take to convert EDF
to .mat format. Therefore, we read the EDF data directly by opening the file and
reading the relevant data corresponding to the selected time and signal specifications
by the user. Header information for the EDF is obtained using the algorithm
developed Dr. Farhad Kaffashi. According to Figure 2-4, the number of signals is 22,
and the number of bytes in header record is 5888. Also, figure 2-4 indicates that the
number of data records is 38610 and the duration of the data record is 1 second. Based
on these two parameters, the total time range of the data is 38610 seconds = 10.725
hours = 10 hours 43 minutes 30 seconds. Since the start time is 18:05:04, the end time
is 04:48:34. For the data records, with the duration of each record equal to 1 second,
the sampling rate of each signal can be obtained from nr, see table 4-1.
Table 4-2 : Physical Min and Max, Digital Min and Max
Signals
Sampling Rate
Position
4
Leg L
64
Leg R
64
C3
256
C4
256
A1
256
A2
256
ROC
256
LOC
256
ECG L
512
ECG R
512
L Chin
256
R Chin
256
Airflow
16
SUM
16
Thoracic
16
Abdominal
16
STAT
1
HR
1
SaO2
1
Cannula Flow
64
DHR
512
33
From Figure 2-4, we know the number of samples in each record is 3847 and
since all of them are double format, we need to multiply 2 to compute the number of
bytes for the data records. To compute the number of byte we need to skip to find the
right point for each signal. Here Skipbyte = Bytes of header record + samples of each
data record * 2 * (Seconds). In this way, we can find the corresponding data in the file.
The Skipbyte algorithm is given below:
The loop runs 5 times, first, we define the pointer pointing to the address of the
file, then we set different skip bytes for every time, finally, we use fseek to find the
address of the file and then skip the relevant bytes we want to find the data of relevant
signals. The ‘bof’ means to set the beginning point of a file as the reference point.
Specifically, there are 5 different skip bytes in this loop, all of them are for data
preparation for the GUI. The Info.FileInfo.HeaderNumBytes and sum(Info.ChInfo.nr)
stand for the bytes of the header record and each data record, respectively. The first
three iterations of the loop are used to store the position of current time point data and
2 consecutive one-minute data epochs after the current time. With these data, we can
display 3 one-minute data in a row for comparison. The fourth iteration stores the
position of ‘delay’ seconds after the current time. This provides the information for
34
evaluating signal trend changes. The last iteration stores the position of one byte after
the current data. The Poincare´plot needs the current data and preceding data for
computations.
Data needs to be extracted properly from the EDF to be useful, and this requires
using information like the physical limits and digital limits from the EDF header. For
example, for the ECGL, the physical limits and digital limits are -1 to 1, and -32768
to 32767. So, the real data of ECGL should be ECGL (from EDF) * 2 /2^16. The
following algorithm does the conversion to properly interpret the data:
Where, Index defines the position of each channel in each data record, and
fread(fid,[sum(Info.ChInfo.nr) WindowLen], ’int16’ means to read data with the size
of the matrix: ‘sum(Info.ChInfo.nr)’ rows and ‘WindowLen’ columns.
After all this processing, DataECGL{i} is the data we need. All the other signals
are the same except the Heart Rate. The heart rate from the EDF file is the
consecutive heart rate that has been averaged over a fixed data window, e.g. one
minute. Our goal for bedside ICU applications is to show more accurate and useful
data, so we would like to use the instantaneous heart rate that is determined by the
R-R intervals extracted from the raw ECG signals, instead of the low resolution
consecutive heart rate from EDF file. Specifically, the algorithm is shown below:
In the algorithm, first, we detect heartbeats (R-points) from the ECG signal and
save the index of each event with the function DetectR developed by Dr. Farhad
35
Kaffashi. Then we compute the total time and time interval between each event by
dividing by the sampling rate. Finally we calculate the instantaneous heart rate by
60/time interval between each event. The HR_R{i} is the final data.
4.3
Realization of the GUI
4.3.1
Data Selection
As discussed in Chapter 3, the GUI has two panels to fit the radio button group
for channel selection. In this way, users could select the data they need from the radio
buttons. Simultaneously, static text next to the panels will show the label of the
signals that are chosen. Thus, users could more directly and clearly see which
channels they have selected.
In the GUI, we block the selection for EEG signals and some other channels that
are not important, this can be easily modified in future versions. In the current version
of the GUI for illustrative purposes, the available channels are: Airflow, Cannula Flow,
ECG L, ECG R, Heart Rate, and SaO2. Users could select any two of them to display
simultaneously. Two examples are shown in Figure 4-2 and Figure 4-3:
Figure 4-2: Data Selection for ECGL vs. ECGR
36
Figure 4-3: Data selection for HR vs. Cannula Flow
Figure 4-2 and Figure 4-3 show 2 combinations of data selection: ECGL vs.
ECGR, and HR vs. Cannula Flow. We could clearly see the data selection in the
panels and static text boxes.
Time selection for the x-axis has been added so users can choose Time selection
for the x-axis to display Time vs. data. The x-axis will be 0 to 60 seconds and the
y-axis indicates the value of data at the relevant time points. Figure 4-4 shows the
combination of Time vs. Cannula Flow.
Figure 4-4: Time vs. Cannula Flow
37
From Figure 4-4, we see there is variability in the signals and the range of
variability is increasing with increasing time.
We also include a pop-up menu for users to directly choose the most common
data comparisons, particular combination include ECG L vs. ECG R, ECG R vs. ECG
L, HR vs. SaO2, and SaO2 vs. HR. These choices will help users quickly select the
data they want, Figure 4-5 illustrates this aspect of the GUI.
Figure 4-5: Regular Data Comparison
In conclusion, for data selection we provided radio button group for choosing two
channels based on user preference and a pop-up menu for preselected comparisons.
Time selection for the x-axis is included for trend data.
4.3.2
Data plotting and time selection
Time series data display through plotting is the core of the GUI. Users are able to
clearly get information from the data plots, users can choose two channels for x- axis
data and y-axis data, and can select the exact time point for the figure. The plot
creation plot is shown in Figure 4-6.
38
Figure 4-6: Legend of plot
In Figure 4-6, we could clearly see there are three different colors of data, green
points, yellow points, and red points. Each color stands for different period of time for
different data. On the top right corner, we use the legend function to present a form
that includes the detail time range for each color data. The green data is data from the
current time window, with yellow and red from two consecutive 1-minute epochs
after the current time period as the default.
Users can set the time interval between green and yellow and yellow and red data
as shown in Figure 4-7 for ECGL vs. HR. Apparently in Figure 4-7, we could see the
time range for each signal has been changed due to the settings of Time Gap1 and
Time Gap2. The green data remains the same time but for yellow one, it’s from
19:14:25 to 19:15:25. And for red one, it’s from 19:24:25 to 19:25:25. There do exist
a possibility that a patient bear a sudden heart attack in the second data period, but
totally normal in period 1 and 3. So, with the help of this method, physicians could
analyze the difference among 3 separate time data.
39
Figure 4-7: Time Gap1 and Time Gap2 to change the time interval between each
data
In addition, we also allow users to change the data length of each period by
changing the value of WindowLen. This change offers a convenient way for users to
analyze different data due to different data length requirements. Specifically, 60
seconds maybe a suitable time period for the SaO2 signal since the sampling rate of
SaO2 is 1 per second and the signal itself is usually very steady. However, for the
heart rate, sometimes 60 seconds of data is not enough to clarify the status of a patient.
300 seconds of data may be a better choice to get a better picture of patient status, so
WindowLen can be used to change the data length of each period. Figure 4-8
illustrates this feature in the GUI.
From Figure 4-8, we observe sthat by setting the Window Length to 300-seconds,
the data details shown in legend have also changed. The range of each data has
increased from 1 minute to 5 minutes. Also, we have already finished the
development of time point selection using both the slider and time input functionality
that has been included in the GUI, allowing users to select any time point they are
interested in to see different signals form the patient.
40
Figure 4-8: Change the Data Length of each period by setting WindowLength
As we discussed in Chapter 3, the scatter plot is faster than plot, however, in
order to change the linestyle of the figure to show Time vs. data plot, we have to use
plot instead of scatter. Also because the size of data is too large, we do not use
linspace to gradually change the color. The large variety of color changes blur the
figure which is opposite to our original goal to more clearly show accurate and useful
data to the users.
Finally, we add a function similar to the data cursor. We can find the exact time of
each event by directly clicking on a certain point in the plot, the event time will
display next to the point. This function is a significant improvement for the study of
abnormal points. This is particularly useful in the ICU setting, where patient stability
if an important consideration. There will be many abnormal points in ta given figure,
so it is important that each abnormal event can be identified and associated with a
given point in time. Our goal is to develop a flexible interface that can be used to
capture every possible sign that may help clinical personnel make decisions.
The functionality would like to add is finding the nearest data point to the current
point in a given time series. The approach is to find the smallest distance between the
selected point and our data, the detail of algorithm is shown below:
41
First, we need to find the x value and y value of current point by Clickpoint =
get(handles.axes1,’CurrentPoint’). Then, we initialize the Minimum distance, and try
to find the nearest point in each color data. Next, we make the decision among the
three nearest points in each color by [~,I] = min(Mmin). Finally, we set the time of the
point according to where we find the nearest point and display it.
In conclusion, we developed the plot and time selection functionality for the GUI.
The legend function has been added to show the detail of each color data. The Time
gap and Window Length provide users with the ability to change the time interval
between each color data and the data length of each period. Slider and time inputs are
also created for users to choose exact time points of interest. Finally, displaying data
and the exact time of certain events by clicking any point of the figure has also been
included in the GUI.
4.3.3
Additional features
After creating the basic data display for the GUI, the analytic information is the
next step. In Chapter 3, we discussed that most widely used monitors such as Philips
and Spacelabs don’t even have the ability to calculate and display simple statistics
such as the mean value of data time series over a given period of time. Figure 4-9
42
shows the detail about the statistical information available in the GUI.
Figure 4-9: Information of Mean value and Standard Deviation of x axis and y
axis
From Figure 4-9, we observe that there are 6 columns and 3 rows for the table.
Each row stands for one period of data. For the first column and third column
represent the mean value of x-axis and y-axis data, fox example we see the mean
values of SaO2 are 91, 90, and 91 and the mean values of Heart Rate are 113, 121, and
122. However, the mean values alone are insufficient since the variability of the
signals could be clinically meaningful. Thus, we also compute the standard deviation
of the x-axis data and y-axis data. The standard deviation of SaO2 is 0.63, 1.3, and 0.9,
and the standard deviation for Heart Rate is 12.1, 18.8, and 22.6.
In addition to the ideas we raised in Chapter 3, we also calculate the mean
distance of each point to the central point of each cluster. First, we get the value of the
central point of each cluster c(xcentral,ycentral), then we compute the distance of each
point to the central point and calculate the mean value of these distances. The standard
deviation of the distances is also computed to show the variability of the data. To
illustrate this idea more clearly, we add three check box to control the display of each
cluster. In this way, we could present three clusters of data separately. Figure 4-10
shows an example if we only choose the red cluster.
43
Figure 4-10: Mean value of Distance, SD of Distance, and cluster selection
From Figure 4-10, if we only choose the red cluster in the check box, only display
the red period data is displayed and the SD of distances indicates the variability of
data. The algorithm is shown below:
HH and VV are the mean values of the x-axis and y-axis data. Then we compute the
distance of each point to the central point DD(j) and get the mean value of these
44
distances DavgDavg = mean(DD), finally we calculate the standard deviation of the
distances.
Data selection for Time vs. data could be used to represent the data trend. Next
we discuss to detect the respiration rate by using Cannula Flow time series data. The
Time vs. Cannula Flow is shown in Figure 4-11:
Figure 4-11: Detect Respiration rate by setting a threshold for Time vs. Cannula
Flow
The signal oscillates with each breath. Thus, we set a threshold at 0.012 for the
Cannula Flow to identify individual breathes for a patient. However, one limit is not
enough to precisely find the respiration rate, small variations in the data may cause
many artificial breathes to be detected. So, in our algorithm, we first find the points
over 0.012 and also confirm it is a candidate breath by comparing to the next point
with 0.012 as well, if it is also larger than 0.012, it’s a real breath, if not is rejected.
The detail of the algorithm is shown below:
45
We need to count the how many times the patient breathed in the period, store the
indices where the events happen, save the time intervals, and compute the Respiration
Rate.
Sometimes the respiratory signal is very noisy and it is not possible to calculate
the respiration rate, figure 4-12 is an example of this situation.
Figure 4-12: Abnormal Respiration Rate, the value of Cannula flow is too low
46
From Figure 4-12, the Respiration Rate at the bottom right corner is 0 because it
was not possible to detect any respiratory events. If we examine the data more
carefully, we could find that the value of Cannula flow is less than 10 x 10-4 and we
the so-called “signal” in the figure is simply noise because the Cannula Flow is too
low to be meaningful.
In conclusion, we compute the mean value of x-axis and y-axis data, we calculate
the mean distance of each point to the central point of data clusters, the standard
deviation is used to measure the variability of the data computed, and we detect the
respiration rate directly from the Cannula Flow.
4.3.4
Poincare´Plot and Histogram
Additional information that might be clinically meaningful and useful can be
extracted from patient time series. For example, the Poincare´can be used to quantify
heart rate variability, and (conditional) histogram is a new idea to quantify the
relationship between two different kinds of data.
4.3.4.1 Poincare´Analysis
The Poincare´plot is a scatterplot of current R-R interval against the preceding
R-R interval, and Figure 4-13, shows the scatterplot of heart rate.
47
Figure 4-13: Poincare´Plot of Heart Rate
At the bottom left corner, we provide a check box for Poincare´plot selection,
and an edit text box for the user to input the samples of delay. The delay is 1 sample
by default and users can change the delay by selecting a different value. The Poincare´
plot presents both short- and long-term variability of the signal. A normal HRV
Poincare´plot looks like an ellipse (Figure 3-1), unlike Figure 4-13. The significant
short-term variability in heart rate as shown in Figure 4-13, is an indicator of
abnormal behavior. Computing SD1C and SD2C, short-term and long-term variability
respectively, owe have SD1C=15.72 and SD2C=15.90.
Additional measures of interest include frequency domain features such as
spectral density, coherence, etc. Because the R-beat time series is not uniformly
sampled, we cannot calculate the FFT directly using MATLAB. Instead, we use the
Lomb-Scargle method [11] that performs spectral analysis on unevenly sampled data.
With the Lomb-Scargle algorithm, we calculate the normalized magnitude and
frequency of R-R intervals in frequency domain. There are three main HRV spectral
components distinguished in a spectrum computed from 2 to 5 minutes (short-term)
recordings [12] [13]: very low frequency (VLF) , low frequency (LF), and high
frequency (HF) components. The distribution of the power and the central frequency
of LF and HF are not fixed but may vary according to the change in autonomic
modulations of the heart period [14]. The VLF component lacks persuasive
48
physiological explanations and the existence of a specific physiological process
supporting these heart period changes may not be reliable. Thus we do not consider
the VLF as one of the components in our analysis. Measurement of VLF, LF and HF
power components is usually made in absolute values of power, but LF and HF can
also be normalized to represent the relative value of each power component in
proportion to the total power minus VLF component [15]. The representation of LF
and HF in normalized units stresses the controlled and balanced behavior of the two
branches of the autonomic nervous system. Furthermore, normalization would
minimize the effect on the values of the LF and HF components to changes in total
power. However, normalized units should always be quoted with absolute values of
LF and HF power in order to describe the total distribution of power in the spectral
components. The range of LF is from 0.04 to 0.15 Hz, and HF is from 0.15 to 0.4 Hz.
The following algorithm shows how we calculate the LF and HF powers:
We compute the low frequency power (LFP) and high frequency power (HFP)
with the FASPER algorithm. The WK1 and WK2 stores the frequency of each heart
beat in the frequency domain and the magnitude of the corresponding frequency. We
49
need to find all the heart beats in the low frequency range and the high frequency
range first. Then compute the mean value of their magnitude and those are the LFP
and HFP we need. In Figure 4-14, it shows we compute LFP =3.26, HF=3.29.
Figure 4-14: Low Frequency Power and High Frequency Power of the Heart
Beat
All these attributes help us quantify the variation and connection between R-R
intervals. Additionally, we use the Poincare´plot to investigate respiratory variability
in Figure 4-15.
Figure 4-15: Poincare´plot: Respiration Rate vs. Respiration Rate (1 sample)
50
Users can select Cannula Flow for the x- and y-axis channels, and the Poincare´
plot for Respiration Rate will display.
4.3.4.2 Histogram
As we discussed in Chapter 3, the conditional histogram shows the distribution of
one signal corresponding to a certain range of another in a given Window Length
period. Specifically, Figure 4-16 shows the histogram of SaO2 vs. Heart Rate:
Figure 4-16: Histogram for SaO2 vs. HR
In this figure, users input the high limit 95 and low limit 80 for the SaO2.
Correspondingly, the figure shows the distribution of the HR when the SaO2 is from
80 to 95 in the time period 9:09:25 to 19:10:25. With SaO2 in the range 90 to 95, the
heart rate varies from 66 to 142. The height of each bin represents the number of
times a given heart rate occurs in the corresponding range. Specifically, in this figure,
we see that when the SaO2 is from 90 to 95, heart rate is mainly between 100 ~ 120
bpm. From 100 to 120, it happens 15+13+12=40 times. This method combines setting
a range for one type data and then showing the corresponding distribution of another
type of data. This provides an effective way to see connections and explore
51
interactions between two relevant data sets. Compared with Figure 4-17 which shows
the original figure of SaO2 vs. HR, the conditional histogram additional,
complementary information:
Figure 4-17: Conventional SaO2 vs. HR scatterplot
Also, if we choose the same signal on both sides, it will display the histogram of
one signal. Figure 4-18 gives an example of the histogram of SaO2 vs. SaO2, which
represents the histogram of one signal:
Figure 4-18: Histogram of SaO2 vs. SaO2
52
The realization for the histogram algorithm is not complicated, but there is one
point we need to emphasize. In the histogram computation, we seek the corresponding
data by searching the relevant index of each signal. This is straightforward if the
sampling rate of the two signals is the same. However, the heart rate we use here is
the instantaneous R-R interval we detect from ECG signals. Not only the time interval
of each beat is not normalized, but also the sampling rate is not fixed since the time
interval between each beat is different. The sampling rate of consecutive heart rate
from the EDF file is 1 per second which is the same as SaO2. Nevertheless, to avoid a
decrease in resolution, we develop our own algorithm to synchronize two different
data. The basic idea of the algorithm is, although the time interval is different for each
heartbeat, we still could average the heart rate in each second. We separate the heart
beats second by second and then compute the mean value of each second. Finally we
obtain synchronized heart rate data that is used to plot the conditional histogram of
HR vs. SaO2.
In conclusion, in this Chapter, we developed the Poincare´ and Conditional
Histogram plots for the ICU display.
For the Poincare´plot, we described the role of SD1C and SD2C in quantifying
the standard deviation of short-term and long-term variability of the data. We also
added an edit text box for users to input the sample delay based on their preference.
In addition, we implemented the Lomb-Scargle method to perform spectral
analysis for unevenly sampled time series data, and used this approach to compute
Low frequency power and high frequency power for HRV analysis.
For the histogram, we added two edit text boxes for users to choose the limits of
one signal. And when users select the check box of histogram, it will display the
histogram instead of conventional plot. The data source of heart rate is discussed, and
the function to show the distribution of one signal corresponding to the relevant range
of the other signal is implemented. Due to the limitation of sampling rate, this
function is currently available for only HR and SaO2. The synchronization work will
be extended so that the analysis can be applied to additional time series data.
53
54
5
Real-Time Analysis Mode
5.1
Introduction
In Chapter 4, designed and implemented an ICU bedside display that includes
analytical computations and visualization using archived EDF data. In this Chapter,
we design the real-time display mode for the system.
To illustrate the basic concepts of the Real-Time Analysis mode, we will limit our
work to the display of two ECG signals. The figure will refresh every 10 seconds to
show the most recent 10 seconds of data from the patient monitor. Correspondingly,
Instantaneously Heart Rate will be detected and the Standard Deviation of Heart Rate
will be computed. Simultaneously, important parameters for Poincare´plot analysis
such as SD1C, SD2C, Low Frequency Power, and High Frequency Power will also be
updated.
In addition, users are able to save the parsed data into a file. We also add the path
selection for users to choose the path where they want to save the data. When users
want to stop the real-time display, they can press the stop button.
Finally, we add a checkbox to realize the switch between Analytic mode and
Real-Time Analysis mode, in this way, users are able to monitor the current status of
the patient, but also able to analyze the status of the patient us archived data. At last,
future work about this project will be discussed.
5.2
Realization of Real-Time Analysis Mode
As we discussed in the introduction, we are going to show two different ECG
signals in the plot and update all the analytic information every 10 seconds with the
figure. Also, we need to offer the option for users to choose the path and name of the
file which is going to save the data. Finally, we need to be able to switch between
Analytic mode and Real-Time Analysis mode.
55
According to these requirements, we add two push buttons, ‘start’, and ‘Set the
Path’ for users to choose their path and start the program. We also add two
checkboxes, ‘Stop’, and ‘Analytic mode’, for users to stop the data loading and switch
between the two modes.
To start the program, first, users need to press the ‘Set the Path’ button to input a
name and select the path for the file they are going to save. Figure 5-1 shows the
window when users click the button ‘Set the Path’:
Figure 5-1: Set the Path selection for users
As Figure 5-1 shows, when users click ‘Set the Path’, they are able to select the
path of the file they want to save the data to. Also, users could input the name of the
file. The data which is saved in the file is the parsed data from the raw data file. It
only records the value of ECG signals. Later, we could use the data in the file to
compute instantaneous HR, Standard Deviation, SD1C, SD2C, LFP, HFP.
After selecting the path for the file, users could start the program by clicking the
‘Start’. Then, in Figure 5-2, it shows ECG(V1), and ECG(LL) in the plot:
56
Figure 5-2: Real Time Display of ECG(V1) and ECG(LL) signals(Ideal)
From Figure 5-2, we could clearly see the two different ECG signals displaying
in the plot. And the figure will refresh every 10 seconds. For illustrative purposes, the
raw data we use is from the demo mode of the Philips Intellivue MP5 monitor, so
there is essentially no change in heart rate. There are some artificial points from the
demo mode, so we could illustrate the functionality of the standard deviation, SD1C,
SD2C, LFP, HFP for the data shown in Figure 5-3:
Figure 5-3: Real Time Display of ECG(V1) and ECG(LL) signals (with Artificial
Points)
The real-time analysis mode shows the current status of the patient, and we need
57
to be able to switch to Analytic mode to select other patient data for further study and
analysis. In this situation, we add a checkbox ‘Analytic mode’ for users to select
switching between modes as shown in Figure 5-4 below:
Figure 5-4: Switch to Analytic mode, ECGR vs. ECGL
Also, another example of Analytic mode is shown in Figure 5-5, HR vs. ECGR:
Figure 5-5: Analytic mode, HR vs. ECGR
Also, users should be able to switch back to the Real-Time Analysis mode. By
clicking the ‘Analytic mode’ again, it will switch back to the Real-Time Analysis
58
mode as shown in Figure 5-6:
Figure 5-6: Switch Back to Real Time Analysis mode
Finally, users could stop the program by clicking ‘Stop’. The program will stop
loading the data and close the file as shown in Figure 5-7:
Figure 5-7: Stop the program
Figure 5-8 shows that we have successfully saved the data into a file.
59
Figure 5-8: Saved Data
5.3
Discussion and Future work
In this Chapter, we developed the Real-Time Analysis mode for the ICU bedside
display and illustrated its functionality using real-time ECG signals to represent the
current status of the patient. Also, the analytic information such as instantaneous heart
rate, standard deviation, and important parameters for Poincare´ analysis such as
SD1C, SD2C, LF Power, HF Power will be updated every 10 seconds with the plot.
In addition, combined with the Analytic mode designed in Chapter 4, we realize
the switching functionality between Real-Time Analysis mode and Analytic mode.
Also, the parsed data of ECG signals will be saved in a file which could be decided by
the user.
These two modes offer options for ICU personnel to monitor the current status of
the patient, and to also have a full access to archived data to improve knowledge of
the past status of the patient. All the analytic information and visual presentation of
data and analytic results will provide significant information to support clinical
decision-making and improve patient care.
However, further work needs to be done in the future. The data we get from the
monitor is currently limited waveform data, although waveform, numeric and alarm
60
data tare combined in the data packets. We need to extend the capability of the display
we designed and implemented in this thesis to accept the numeric and alarm data for
both visualization and analysis. We also need to extend the data inputs to include
other ICU devices not directly connected to a patient monitor and to extend the
Real-Time Analysis mode to include other signals such as Non-invasive blood
pressure (NIBP), Oxygen Saturation (SaO2), Respiration, and many others.
Additionally, it would be interesting to further explore what additional analytical
and visualization techniques can help support clinical decision-making in the ICU,
and how would the bedside GUI developed in this work be integrated into the ICU
workflow to improve both efficiency and patient care?
61
Appendix
Table of Sampling Rate and TransType of Different Signals
Signals
Sampling Rate
TransType
Position
4
DC
Leg L
64
Leg
Leg R
64
Leg
C3
256
EEG C3
C4
256
EEG C4
A1
256
EEG A1
A2
256
EEG A2
ROC
256
EEG ROC
LOC
256
EEG LOC
ECG L
512
ECG
ECG R
512
ECG
L Chin
256
EEG L Chin
R Chin
256
EEG R Chin
Airflow
16
HP
SUM
16
HP
Thoracic
16
HP
Abdominal
16
HP
STAT
1
Oximeter
HR
1
Oximeter
SaO2
1
Oximeter
Cannula Flow
64
Cannula
DHR
512
Off
Algorithm of FASPER.m to compute LFP and HFP
Thanks to Reza Jamasebi and Ravishankar Balaji who developed this algorithm
% This function is to compute the Lomb-Scargle normalized periodogram for
% unevenly sampled time series. The algorithm adapted from "Fast algorithm
for special analysis of unevenly sampled data" by william H. Press.
% Inputs:
%
X: abscissas or time points
%
Y: Ordinates or signal values at each data point
% Output:
%
WK1: sequnce of frequncy up to HIFAC times of average Nyquist
62
%
Freq. (The defualt values of HIFAC is 1).
%
Wk2: Values of Lomb Periodogram at each frequency
%
JMAX: the index of maximum value of WK2
%
PROB: a samll value of PROB indicates that a significant
%
periodic signal is present
% Developed By: Reza Jamasebi and Ravishankar Balaji
% Last Modified: Feb 26, 2008
function [WK1 WK2 JMAX PROB]=FASPER(X,Y)
%times the
Average Nyquist Freq.
HIFAC=1;
%Over Sampling Factor (Typically 4)
OFAC=4;
% The number of Data points
N=length(Y);
% Number of Interpolation points per 1/4 cycle of highest frequncy
MACC=2;
% Size the FFT as next power of 2 above Nyquist Freq.
NOUT=.5*OFAC*HIFAC*N;
NFREQT=OFAC*HIFAC*N*MACC;
NFREQ=64;
while NFREQ<NFREQT
NFREQ=2*NFREQ;
end
NDIM=2*NFREQ;
AVE_Y=mean(Y);
VAR_Y=var(Y);
XMIN=min(X);
XMAX=max(X);
XDIF=range(X);
WK1=zeros(1,NDIM);
WK2=zeros(1,NDIM);
FAC1=NDIM/(XDIF*OFAC);
FNDIM=NDIM;
YAVE=Y-mean(Y);
NFAC=[1 1 2 6 24 120 720 5040 40320 362880];
for j=1:N
CK=1+mod((X(j)-XMIN)*FAC1,FNDIM);
CKK=1+mod(2*(CK-1),FNDIM);
if round(CK)==CK
WK1(CK)=WK1(CK)+YAVE(j);
else
IL0=min(max(floor(CK-.5*MACC+1),1),NDIM-MACC+1);
63
IHI=IL0+MACC-1;
NDEN=NFAC(MACC);
FAC=CK-IL0;
for jj=IL0+1:IHI;
FAC=FAC*(CK-jj);
end
WK1(IHI)= WK1(IHI)+YAVE(j)*FAC/(NDEN*(CK-IHI));
for jj=IHI-1:-1:IL0
NDEN=(NDEN/(jj+1-IL0))*(jj-IHI);
WK1(jj)=WK1(jj)+YAVE(j)*FAC/(NDEN*(CK-jj));
end
end
if round(CKK)==CKK
WK2(CKK)=WK2(CKK)+1;
else
IL0=min(max(floor(CKK-.5*MACC+1),1),NDIM-MACC+1);
IHI=IL0+MACC-1;
NDEN=NFAC(MACC);
FAC=CKK-IL0;
for jj=IL0+1:IHI;
FAC=FAC*(CKK-jj);
end
WK2(IHI)= WK2(IHI)+1*FAC/(NDEN*(CKK-IHI));
for jj=IHI-1:-1:IL0
NDEN=(NDEN/(jj+1-IL0))*(jj-IHI);
WK2(jj)=WK2(jj)+1*FAC/(NDEN*(CKK-jj));
end
end
end
WK1=abs(fftshift(fft(WK1)));
WK2=abs(fftshift(fft(WK2)));
WK1=WK1(NFREQ+1:2*NFREQ);
WK2=WK2(NFREQ+1:2*NFREQ);
DF=1/(XDIF*OFAC);
K=3;
PMAX=-1;
for j=1:NOUT
HYPO=sqrt(WK2(K)^2+WK2(K+1)^2);
HC2WT=.5*WK2(K)/HYPO;
HS2WT=.5*WK2(K+1)/HYPO;
CWT=sqrt(.5+HC2WT);
SWT=sqrt(.5-HC2WT)*sign(HS2WT);
DEN=.5*N+HC2WT*WK2(K)+HS2WT*WK2(K+1);
CTERM=(CWT*WK1(K)+SWT*WK1(K+1))^2/DEN;
64
STERM=(CWT*WK1(K)+SWT*WK1(K))^2/(N-DEN);
WK1(j)=j*DF;
WK2(j)=(CTERM+STERM)/(2*VAR_Y);
if (WK2(j)>PMAX)
PMAX=WK2(j);
JMAX=j;
end
K=K+2;
end
EXPY=exp(-PMAX);
EFFM=2*NOUT/OFAC;
PROB=EFFM*EXPY;
if PROB>.01
PROB =1-(1-EXPY)^EFFM;
end
WK1=2*WK1;
WK1=WK1(1:NOUT/2);
WK2=WK2(1:NOUT/2);
end
65
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