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HEART: An Automated Beat-to-Beat

Cardiovascular Analysis Package Using Matlab®

Mark J. Schroeder1,2, Ph.D., Bill Perreault2, M.S., Daniel L. Ewert1,2, Ph.D., and Steven

C. Koenig1, Ph.D.

1Jewish Hospital Heart and Lung Institute, Department of Surgery, University of

Louisville, Louisville, KY 40202

2Cardiovascular Research Laboratory, Department of Electrical and Computer

Engineering, North Dakota State University, Fargo, ND 58105

*Funding provided by a grant from the Jewish Hospital Heart and Lung Institute

Running Title: Hemodynamic Beat-to-Beat Analysis

Word Count: 4,117

Address Correspondence To:

Primary Secondary

Mark J. Schroeder, Ph.D. Steven C. Koenig, Ph.D.Electrical and Computer Engineering Dept. Cardiovascular Research CenterNorth Dakota State University 500 South Floyd Street Fargo, ND 58105 Department of SurgeryPH: (701)-231-8049 University of LouisvilleFAX: (701)-231-8677 Louisville, KY 40202Email: [email protected] PH: (502)-852-4416

FAX: (502)-852-1795E-mail: [email protected]

mailto:[email protected]

Abstract

A computer program is described for beat-to-beat analysis of cardiovascular parameters from high-fidelity pressure and flow waveforms. The Hemodynamic Estimation and Analysis Research Tool (HEART) is a post-processing analysis software package developed in Matlab® that enables scientists and clinicians to document, load, view, calibrate, and analyze experimental data that has been digitally saved in ascii or binary format. Analysis routines include traditional hemodynamic parameter estimates as well as more sophisticated analyses to estimate lumped arterial model parameters and vascular impedance frequency spectra. Cardiovascular parameter values of all analyzed beats can be viewed and statistically analyzed. The most attractive feature of the HEART program is the ability to analyze data with visual quality assurance throughout the process establishing a framework toward which Good Laboratory Practice (GLP) compliance can be obtained. Additionally, the development of HEART on the Matlab® platform provides users with the flexibility to adapt or create study-specific analysis files according to their specific needs.

Author Key Words

Data acquisition, data analysis, beat-to-beat, hemodynamics, cardiovascular, computer software package, Matlab®

Hemodynamic Beat-to-Beat Analysis

1. Background

Scientists, engineers, and clinicians record hemodynamic data in order to investigate cardiovascular dynamics and improve treatment of cardiovascular disease. The selected method of data analysis is a critical factor in extracting the most information from these data. Unfortunately, often times little attention is paid to the details of the data analysis methods and the investigator settles for a less than ideal method for obtaining cardiovascular metrics.

Since the 1960’s, most data acquisition methods involved recording and analyzing data using strip chart recorders [1-2]. The methods used to obtain hemodynamic parameters from strip chart paper are subject to numerous measurement inaccuracies that can ultimately lead to incorrect results and conclusions. Additionally, the tedious and time intensive nature of performing analog data analysis often leads to only small samples of data or a representative beat of data being analyzed, thus creating errors due to the unsteady nature of hemodynamic data, e.g. changes caused by respiration.

Over the past several decades, there has been a migration from standard strip chart recorders toward digital data acquisition and analysis systems in which data can be streamed directly to a digital storage device. Despite the obvious time and space saving features of using such a method, many investigators hesitate to make the transition to digital acquisition systems due to the user’s inability to perform post-processing of the digital data. This, along with the realization that ever-increasing amounts of data can be stored digitally as data storage capabilities grow, signifies a need for fast and accurate digital data analysis methods.

The first data acquisition and analysis (DAQA) programs were developed on Apple [3-5] and Macintosh computers [6], microcomputers [2,7-9], and VAX systems [10-11]. As computer technology rapidly improved in the 1990’s, DAQA programs were developed for personal computers [12-17]. These earlier programs were capable of performing data acquisition, but were often limited by storage capabilities and required off-line data analysis. More recently, data acquisition with real-time data analysis has been introduced, and used successfully in support of cardiovascular research [18-21].

A number of turn-key computer software packages are also now commercially available, including BioBench (National Instruments, Austin, TX), PowerLab (ADInstruments, Grand Junction, CO), ARIA-1 (Millar Instruments, Houston, TX), DADiSP (DSP Development Corp, Newton, MA), and PO-NE-MAH (Gould Instrument Systems, Valley View, OH ). Although they have offered some solace to investigators, these commercial ‘what-you-see-is-what-you-get’ (WYSIWYG) packages provide a limited number of analysis options and often lack the flexibility necessary to meet the evolving study-specific demands of some investigators.

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In sharp contrast to the recent trend toward real-time data acquisition (DAQ) and real-time data analysis (DA), our group has made a conscious decision to keep these procedures separate. The rationale for this is two-fold: (1) it enables a high-degree of control for quality assurance throughout data acquisition and data analysis processes, thereby establishing a framework that will meet Good Laboratory Practice (GLP) guidelines regulated by the Food and Drug Administration (FDA) in testing medical devices, and (2) it exploits industry-standard software packages that were designed explicitly for carrying out data acquisition (LabVIEW, National Instruments, Austin, TX) and data analysis (Matlab®, MathWorks, Natick, MA) procedures.

Over the past ten years, our group has been developing PC-based data acquisition and analysis programs. Our experience in handling large amounts of digital data has led us to develop an efficient and accurate data analysis program that allows a user to easily document, view, calibrate, analyze, and execute data reduction of cardiovascular data on a beat-to-beat basis. The program was created within the Matlab® environment (Mathworks, Natick MA), a commonly used mathematical tool used by many mathematicians, engineers, and scientists. Equipped with myriad mathematical capabilities, this analysis package and platform provide tremendous flexibility and power to meet the user’s present and future needs. We describe in detail the design and functionality of this program called HEART (Hemodynamic Estimation and Analysis Research Tool).

2. Design

The HEART program was developed with the goals of ease of use, time efficiency, accuracy, flexibility, and quality assurance for every step of the data analysis process. These were accomplished by implementing the program using windows-based graphical user interfaces (GUIs) and a menu-driven format to make navigation of the system easy and user-friendly. All programming was performed using Matlab® version 6.0, a matrix based mathematical package capable of handling large amounts of data and carrying out time-efficient handling of mathematical manipulations. HEART can either be run as a stand-alone data analysis tool or within the Matlab® programming environment (requires purchase of Matlab® software), thereby allowing users to modify or add additional routines to the main HEART program tailored to investigator study-specific needs.

The complete HEART program uses a total of seventeen Matlab® m-files. As illustrated in the flowchart below (Figure 1), HEART was designed to carry out numerous functions to help investigators analyze their data. The flowchart outlines the order in which these generalized functions are performed. The first step in this process is accessing or loading the data by using the “Profile” window. This was designed to load either binary or ascii data from files

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containing numerous variables stored in columnar format. Data of various word sizes can be loaded, as can data containing header information at the beginning of the data file. The “Data Viewer” can be used to plot and/or save any or all data variables over any desired length of time. The data can be viewed versus data point index or time, and, if desired, with pre-selected beat indices. Separate windows exist for calibrating data, automatically selecting beat indices, and viewing mathematically manipulated data. A “Data Analysis” window allows the user to select from a variety of automated data analysis routines. Finally, windows exist for viewing the analysis results and exporting the results and other information to an ascii file for data reduction and/or statistical analysis. The following sections will describe each function in greater detail.

[ Figure 1 ]

2.1. Profile

The first step in using HEART is to start the Matlab® program and then type “heart” at the command line prompt. Assuming the HEART program is located in a Matlab-accessible directory, the program will open to the “Profile” window (Figure 2). This window allows the user to either create or load a previously saved “profile”. A profile is an information set linked to a unique set of data. It is initially created to provide necessary information for loading and analyzing the data, but will ultimately include beat indices, calibration factors, and analysis results. To create a profile, one must first select the “Create Profile” button. Then a data file can be selected using the browse feature. If a calibration file exists (to be discussed later), the file and path can again be located using the browse feature. The names of the signals and corresponding signal units that are in the data file must be selected and entered in the order they appear in the data file by using the signal name and signal unit popup menus. Additional signal names can be created by using the “Edit: Preferences” option (to be discussed later).

[ Figure 2 ]

A section exists in the profile window for specifying information concerning the data file. The four specifications include the data file format, sampling rate, data word size, and number of lines of the file header. The file format can be either ascii or binary. The sampling frequency must be entered in units of samples per second. The data word size, or type and length of each data value, can be selected from a popup menu that provides six different word lengths and types including integer and floating point numbers. If the data file has a header, the number of lines can be specified so that header lines are skipped when loading the data. A “View Header” button opens a separate window showing the specified number of lines of the data set. The entered number of header lines can then be adjusted if it was inaccurate. To load the data, the user selects the “Load Data File” button. A small window will appear indicating that data is being

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loaded, which is followed by a window that indicates whether or not the data was successfully loaded.

A description of the data and pertinent notes can be entered and displayed in the ‘Profile Description and Notes’ panel by clicking on the “Edit Description” button. Additionally, the profile name and path and the original and last saved dates information is provided in the “Other Profile Information” panel.

Once the profile has been created, it can be saved as a “*.pro” file. The default profile name is the name of the specified data file but with an extension of “.pro”. Of course, this name can be altered when saving the profile. Multiple profile handling was incorporated so that several profiles can be opened and data loaded before proceeding to other program options. Each HEART subroutine provides the user with an option of which profile/data set to use. This eliminates the need to repeatedly access the profile window or close profiles so that another one can be opened.

2.2 Data Preparation: View Data

Once a profile has been called or created and the experimental data have been loaded, one will generally want to inspect the waveforms to insure the data loaded properly. This can be accomplished using the data viewer (Figure 3) that contains a window allowing selected signals to be plotted over any selected range. If desired, a user can save the raw data in the plot window, data file name, data range, sampling rate and beat indices to a Matlab® or ascii file by clicking on the “Save Plotted Data to a File” button. Additionally, a “Mark Point” button can be used to display the values of all plotted signals at a selected x-value. Several features that are in the “View Data” window are also utilized in other windows that involve plotting. These include: zoom, a “VCR Player Control” that scrolls through the data at adjustable speeds, and options to view the data in its entirety, calibrated or uncalibrated, normalized, or versus time or index. If the beats have already been picked, one can also view picked beat boundaries for the desired signal of choice.

[ Figure 3 ]

2.3. Data Preparation: Data Calibration

The HEART program offers a simple means for calibrating data and storing calibration factors. Calibration of data against a known standard is necessary to convert binary bit counts to physiologic units and insure the accuracy of the acquired data prior to analysis. The calibration process begins by loading data collected at various known measurement values; for instance, data collected from a pressure catheter subjected to known pressures of 0 mmHg and 100 mmHg. The data can then be viewed in the “Data Calibration Factors

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Extrapolation” window found under the “Data Preparation” pull-down menu, as shown in Figure 4. Data calibration zones are selected at each desired measurement level by clicking on the “Pick Calibration Zone” button and then choosing the left and right sides of a data section using a cursor. The user is prompted to enter the actual known value of each zone, for instance, 0 mmHg (minimum) and 100 mmHg (maximum). This information is stored in memory and must be performed for at least two zones of differing values. Incorrectly selected zones can be deleted by selecting the “Delete a Zone” button.

[ Figure 4 ]

The user then selects whether a first or second order fit is desired. A first order fit will determine the coefficients for the equation of a straight line (y = Ax + B) that best fits the average values of the calibration zones to the entered known values in a least-squares sense. A second order fit requires at least three calibration zones and determines the coefficients for a non-linear fit of the form: y = Ax2 + Bx + C. The first order fit is generally the method of choice, as transducers should be linear. The calibration procedure must be repeated for all other signals that require calibration.

Upon calculation of the calibration factors for a signal, the coefficients are displayed in their corresponding calibration factors text boxes. The text boxes also allow for the manual entry of calibration factors. All signal calibration factors are saved when the profile is saved. Alternately, one can save the calibration factors to a separate file by selecting the “Save Calibration Factors” button in the calibration window. This will create a file with an extension of “*.cal” that can then be applied to additional data sets by choosing it as the calibration file in the profile window.

2.4. Data Preparation: Pick Beats

In order to analyze hemodynamic data on a beat-to-beat basis, beginning and ending beat indices must be identified and verified. Therefore, two windows were designed to perform these tasks: 1) Beat Pick: Initial Selection and 2) Beat Pick: Beat Editor.

The “Beat Pick: Initial Selection” window (Figure 5) allows the user to select beginning and ending beat boundries manually or automatically using beat detection algorithms. The manual method allows one to add a beat by clicking on the “Add Beat” button and then selecting the beginning and ending points of a beat by using a cross-hair cursor. After choosing a beat, the beginning of beat boundary will be marked with a circle and the end of the beat boundary will be marked by an ‘x’. Beats can be deleted by selecting the “Delete Beat” button and then clicking on the beat beginning marker. Beat selections can be saved by selecting the “Save Beats” button.

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[ Figure 5 ]

There are several automatic beat detection algorithms for the user to select. The automatic selection of beat indices is a two-step process. First, the subroutine uses an initial picking algorithm to get a rough approximate of the beat indices. Then a second algorithm is used to refine the initial selection of beat indices. The user selects an algorithm pair from a popup menu. The beat picking algorithms for the left atrium, left ventricle, and systemic vasculature are as follows:

AoF LVPAoF AoPAoF dLVP/dtLVP LVP LVP AoPLVP dLVP/dt,

where AoF is aortic flow, LVP is left ventricular pressure, AoP is aortic pressure, and dLVP/dt is the first derivative of LVP. Similar beat picking routines and detection algorithms are available for right atrial, right ventricle, and pulmonary vasculature hemodynamic waveforms. Prior to running the beat picking algorithm, the user must select which signal to associate or save the beat indices to by selecting a signal in the “Beat Type to View/Save to” popup menu. This is important since the analysis routines access specific beat indices according to the name under which they were saved. Additionally, the beat indices can be viewed atop any signal of choice by selecting a signal in the “Beat Reference Signal” drop down box. Next, the user must enter various threshold values that the beat picking algorithms use to select beats. The first two threshold values, “Upper Threshold” and “Lower Threshold”, are based on the first algorithm signal of the beat picking method selected. Values should be entered so that the upper threshold is greater than the lower threshold and such that neither threshold exceeds any one beat’s extrema. The third threshold, “Bad Beat Threshold”, utilizes the first derivative of the ventricular pressure to indicate bad beats. This removes bad beats due to anomalies such as the aortic valve hitting the transducer. The derivative can be viewed by checking the “View Ventricular dP/dt” box. A value above the typical diastolic noise level should be entered into this threshold box. If a beat is found with noise in the relaxation phase exceeding this threshold level, it is automatically marked as a bad beat. The last threshold, “End-Diastolic Threshold”, is used to select the end-diastolic ventricular pressure when the left ventricular pressure is the final beat picking algorithm. This is again based on the first derivative of the ventricular pressure. The threshold value, generally between 0 and 150, should indicate the derivative at the point of end-diastolic ventricular pressure. These threshold values can be revised until the correctly picked end-diastolic point is

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selected, which can vary according to the level of noise in the left ventricular waveform and heart rate.

After these initial steps, beat picking can be performed by clicking on the “Run Beat Pick” button. Beat selection is carried out according to the pair of algorithms selected. The first beat picking algorithm uses the “Upper Threshold” and “Lower Threshold” values of either aortic flow (AoF) or left ventricular pressure (LVP) to roughly determine the location of the beat boundaries. The use of the two thresholds can help reduce beat selection errors due to noise. The final beat picking algorithm first establishes a window, or range, around the resultant boundaries of the first algorithm. The final beat boundary is selected within the window according to the particular algorithm selected as follows: AoP – minimum aortic pressure value, LVP – backward search until the “End-Diastolic Threshold” is crossed, and dLVP/dt – maximum first derivative of the left ventricular pressure, calculated using a 5-point derivative [22]. This will result in the selection of beat boundaries as either the minimum diastolic aortic pressure, the end-diastolic left ventricular pressure, or the maximum value of the left ventricular first derivative, respectively.

Upon completion, the user is shown the number of selected “good” and “bad” beats and queried on whether to save the beat indices. If the beats are not saved at that time, they are stored in memory until they are saved in the “Beat Editor” window or at the time of closing the profile. The beat indices will be immediately plotted in the display window on the chosen beat reference signal. The beginning of each beat is indicated by a circle and the end of the beat by an ‘x’. By default, these symbols are color coded green for a good beat and red for a bad beat, as shown in Figure 5. Again, if the beat picking routine does not select the beat indices satisfactorily, the user can alter the threshold values and repeat the beat picking process.

Upon satisfactory automatic beat selection, the selected beat indices can be fine adjusted by the user in a “Beat Editor” window. This window allows one to move the start or end position of a beat by selecting the desired action button, clicking the original position of the index to be moved, and then clicking the newly desired position of the index. Also, one can convert previously picked beats to either “good” or “bad” by selecting the corresponding action button and then clicking on the start of the beat to be changed. The beat indices can be saved at any time by clicking the “Save Beats” button. Additionally, all beat indices can be removed by pressing the “Clear Beats” button.

2.5. Data Preparation: Manipulate data

The HEART program data manipulation window allows the user to mathematically manipulate and plot the loaded data. This is done by entering equations as one would do at the Matlab® command line. Simple expressions

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Mark Schroeder, 01/03/-1,

doesn’t this just delete the indices, not ‘reset’ them?


such as addition and subtraction can be calculated and graphically displayed as well as more complex Matlab® functions such as derivative, integral, and Fast Fourier Transform (FFT). After entering the desired expression, pressing the “Plot Signal” button will evaluate and plot the expression as well as display the expression in the panel next to the plot window. Repeating the process will add signals to the plot, while pressing the “Clear Plot“ button will clear and reset the plot window.

2.6. Data analysis

The power of the HEART program lies in the speed of automatically picking and verifying beat indices, efficiency of analysis, and its adaptability. The analysis window provides a list of analysis routines and check boxes from which to choose the analyses programs to execute. Additionally, indicator boxes exist to show which routines have previously been executed. The user can either save the selected analysis routine selections or execute the selected routines for the indicated profile by selecting either “Save Selections” or “Analyze Selected File”, respectively. All of the checked analysis routines will be analyzed in order and a window will be displayed to indicate completion of the analysis. The results of each analysis routine are saved to the profile where they can be accessed via the “View Output Data” window.

Currently, several analysis options have been incorporated into the HEART program. Of these, two provide beat-to-beat analysis of basic cardiovascular parameters most commonly used by investigators. An analysis routine called “Cardiac: Left Heart” calculates up to twenty-five parameters that can be derived from left ventricular pressure, aortic pressure and flow, left atrial pressure, and central venous pressure. These include systolic, diastolic, and mean values, derivatives, integrals, and mechanical properties of the left ventricle (i.e. stroke work, elastance, source loss, etc.). A complete list of beat-to-beat calculated parameters for the left heart program is listed in Table 1. Parameters for the right atrium, right ventricle, and pulmonary vasculature can be determined by selecting the “Cardiac: Right Heart” analysis routine. These routines use beat indices based on the end-diastolic right ventricular pressure. After execution of the analysis, the results are immediately saved to the output file.

[Table 1]

Another commonly used analysis routine is the four-element Burattini arterial model [23-24]. This lumped parameter electrical model uses a parallel inductor and resistor in series with a parallel capacitor and resistor to characterize the systemic vasculature. Previously described methods are used to estimate these

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four parameters [25-26]. Briefly, the routine estimates the optimal lumped parameter values by iteratively adjusting initial parameter estimates until the least sum squared error between the model and experimental impedance is minimized. This routine uses beat indices that are based on the end-diastolic aortic pressure. Again, the results are immediately saved upon completion. This analysis routine possesses the flexibility to be easily adapted to other electrical analog models, and can also be applied to characterize the pulmonary vasculature.

Additional programs exist to analyze the impedance spectrum of the systemic and pulmonic arterial systems, coronary circulation, and other investigator study specific parameters (e.g. ventricular pressure-volume relationship). Combined, the aforementioned analysis programs provide the investigator with a powerful means to obtain commonly used hemodynamic information quickly and easily. Additionally, advanced users of Matlab® can edit the HEART program analysis files or create entirely new files to perform operations that are tailored to the investigator’s needs.

2.7. Post-processing Output: View output data

The “View Output Data” window (Figure 6) allows one to view the analyzed results for every beat from the previously executed analyses. By using the provided popup menus and the “Plot” button, one can add a desired parameter to the plot window. This will plot the parameter’s value for every beat that was analyzed. Beats that were marked as bad are not analyzed and a break in the plotted line at the point of its occurrence will be present. Additional parameters can be added to the plot by following the same steps. One can even plot the same parameter from different profiles together. Another feature is the ability to plot the mean, maximum, minimum, and standard deviation of the last plotted variable by selecting a desired statistic calculation from a popup menu and clicking on “Plot Statistic”.

[ Figure 6 ]

2.8. Post-processing Output: Export Data

It is often necessary for the user to be able to access the physiologic data, profile data, or the analysis output data. The post-processing output menu option of “Export Data” allows one to do just that. Three options are presented within the window, as listed in Table 2. Selecting any of the options will send the desired information to an ascii file that can be loaded via a number of text editors and spreadsheets for viewing and further manipulation.

[ Table 2 ]

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2.9. Edit Preferences

In order to provide the user with both functional and stylistic options, the option to edit preferences is available under the “Edit” menu selection. Realizing that signals other than those provided in the Profile Signal Name list may be required, the Preferences window allows additional signal names and corresponding abbreviations to be entered and saved. Additionally, for esthetic purposes, stylistic changes that can be altered include the following:

Window, panel, and text colorsPlot window and plot line colorsGood and bad beat marker color, size, and styleCalibration zone marker color and styleMeasurement line color and style

All preference alterations can be saved as well as be restored from either the last saved values or from the default values.

3. Verification

Calculation of analysis output parameters, as previously described (Table 1), have been verified using qualitative and quantitative comparison methods. For example, calculated parameters such as systolic, diastolic, and end-diastolic pressures are compared against calibrated pressure waveforms from experimental data sets as well as simulated data sets (Patient Simulator, Dynatek Nevada, Carson City, NV). Minimal error is encountered in determining these parameters aside from the presence of aberrant beats. However, aberrant beats should be removed by the user in the beat boundary and data integrity verification steps.

Each calculated hemodynamic parameter has been verified by comparing the result to that determined from visual inspection of the plotted data or by mathematical calculation of the parameter. In all cases, verification-plotting features have been added to the analysis code so that the programmer can plot a specific resultant value or indice superimposed on the hemodynamic data. This has proven valuable in verifying the integrity of the automatically selected beat boundaries and other indices that are used to calculate hemodynamic parameters.

Since parameter accuracy is highly dependent upon correct placement of the selected beat indices, the HEART program was designed to allow the user the freedom to easily reposition beat indices or remove selected beats altogether. For example, heart rate is dependent upon correct selection of beat indices, subsequently, if the beat indices are selected consistently then the heart rate will

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be calculated correctly. The ability to visually inspect and adjust beat indices is a unique feature that provides a stage of user quality assurance. This enables the user to remove corrupted beats and/or intermittently poor data from the analysis procedure without discarding entire data sets. The visual inspection and verification of beat indices process can be performed quickly and reliably by rapid scanning of the entire data set. For example, a one-hour data file containing over 10,000 beats of data can be scanned in less than a minute, if no changes are required. To adjust start and end indices for one beat or mark a good beat as bad, takes but a few seconds to accomplish. Additionally, electrical analog models (i.e. 4-element Windkessel) were used to simulate hemodynamic data with known output parameters [23]. The HEART program output parameters were verified against the simulation parameters. Uncertainty and Monte Carlo analyses were also performed to validate arterial model parameter estimation accuracy [25].

4. Comparison to Existing Programs

Methods used for data analysis have greatly evolved over the last few decades. Digital data analysis methods, as well as data acquisition methods, have matured along with the advancements in computer technology [2-17]. Limited resources of early computers forced acquisition and analysis of data to be performed separately. However, as computer architecture and operating systems continued to improve with time, it was feasible to perform simultaneous real-time data acquisition and analysis. The first program capable of this task was CORDAT II, an academically inspired program capable of real-time DAQA on a DOS-based 486 PC with a 33 MHz microprocessor [21]. In addition to calculating traditional cardiovascular parameters on a beat-to-beat basis, it was also capable of calculating more sophisticated cardiac parameters such as pressure derivatives (dP/dt) and the times at which the parameters occurred. Nowadays, various academic, real-time DAQA programs exist. These programs are, in general, very user-friendly, and provide a great deal of graphic user interface (GUI) technology that allows the user to easily control the program [18-20].

To provide support to the populace, a number of turn-key computer software packages are also now commercially available. As previously mentioned, these include BioBench, PowerLab, ARIA-1, DADiSP, and PO-NE-MAH. These real-time DAQA programs are user-friendly and capable of calculating a large number of cardiovascular parameters and their related time indices. As the need to acquire and process hemodynamic data in the science fields continues to grow, the use of real-time DAQA programs continues to flourish. Although these programs provide a necessary service to investigators who want fast results, it is sometimes done at the expense of quality assurance and flexibility.

Investigators often have the need to calculate unique, study-specific parameters that are not available from these “canned” data analysis packages. This forces

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Mark Schroeder, 01/03/-1,

Isn’t this essentially the same as the previous sentence?


the investigator to either devise another means for calculating the parameters or discourages the investigator from studying the desired parameters altogether. Additionally, these programs do not provide visual verification steps required to assure data integrity. Although the thought of acquiring and analyzing data with only the push of a button, i.e. no additional time requirements for data analysis or quality assurance, is initially attractive, one can easily realize the drawbacks of accepting the program’s output parameters at face value. This practice can lead to the inclusion of aberrant data, misinterpretation of results, and formulation of incorrect study conclusions.

To remedy these problems, we contend that data analysis must be performed off-line. In selecting appropriate software to perform the tasks of acquisition and analysis, our group selected two industry-standard software packages, LabVIEW™ and Matlab®, respectively. Each of these programs is considered among the best in their respective fields. Although either program is now capable of performing both DAQ and DA, we prefer to use them to perform the tasks in which they were originally intended. Reasons for this include LabVIEW’s™ compatibility with National Instrument’s hardware and Matlab’s® flexibility, numerous mathematical functions, and superior ability to handle large amounts of matrix data.

Proponents of real-time DAQA correctly point out the need for monitoring the physiological condition of the test subject during the experimental protocol. In order to meet this requirement, our 16-channel real-time data acquisition program calculates and displays basic clinical parameters such as heart rate, systolic/diastolic/mean pressure, and cardiac output [27]. These data are strictly used to monitor the test subject, but due to possible inaccuracies, are not included in the final data analysis.

The principle reason for performing post-processing data analysis is to maintain the highest-standards of data accuracy and quality assurance throughout the analysis process. In order to maintain the highest degree of accuracy, pre- and post-calibrations of all transducers are performed and compared during the off-line data analysis procedure. This is particularly important in long acute (i.e. > 4 hrs) and chronic (i.e. days) experiments when catheter drift can occur, which could mask physiological changes if only pre-calibrations are used. Quality assurance steps such as this are practiced and maintained throughout the data analysis process by allowing the investigator to monitor each step, including calibration, beat boundary selection, data analysis, and data reduction. For example, this approach allows the investigator to focus on sections of the data where artifact may be introduced, such as PVCs, catheter whip, noise, and transducer drift, that can dramatically influence the selection of beat boundries and calculation of cardiovascular parameters.

As shown in Table 1 and Figure 1, the HEART program offers most, if not all, of the hemodynamic parameters and analysis tools that other on-line and off-line

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data analysis packages offer. We hope to incorporate any remaining commonly used parameters and tools in the near future. HEART also provides additional analysis methods not generally seen in other packages. These include calculation of arterial input impedance over numerous harmonics and estimation of lumped parameter arterial model parameters. Additionally, we have incorporated other study-specific programs used to support various ongoing research projects in our lab.

Finally, an important long-range goal is the DAQA of experimental data during testing of medical devices. In order to meet strict FDA regulations, the DAQA software must be GLP compliant. In separating the data acquisition and analysis programs and providing quality assurance throughout the analysis process, it is believed a proper framework is in place toward achieving meeting FDA requirements.

5. Summary

As cardiovascular data acquisition techniques continue to shift towards digital data collection and archiving methods, there is an increasing need for user-friendly, yet flexible, data analysis software. There are a number of advantages to using a digitally-based analysis program, including the HEART program, compared to earlier methods of analyzing data from strip chart recordings. Primarily, it allows anyone from a student to a clinician to quickly and accurately reduce large amounts of data, enabling investigators to rapidly assess and disseminate results.

The HEART program provides many attractive features required by rigorous data analyzers, primarily the ability to analyze data with a high degree of control and accuracy throughout the analysis process and provides investigators the flexibility to modify or add new features. The HEART program has the ability to efficiently handle both ascii and binary formatted digital data, and allows the user to load, view, calibrate, manipulate, and analyze hemodynamic data on a beat-to-beat basis in an easy to learn, user-friendly format. It also has ability to quickly determine beginning and ending beat indices that can then be used to accurately calculate hemodynamic parameters on a beat-to-beat basis. The calibration and beat-to-beat analysis provide a high level of accuracy and quality assurance through visual verification in calculating hemodynamic parameters at a level difficult to obtain using pre-digital analysis methods. This can be beneficial in achieving statistical significance and reducing error. The power and flexibility of this program are derived from the Matlab® platform upon which it was built. Matlab® provides numerous powerful functions that can be used for analyzing, manipulating, and viewing data. Additionally, custom, study-specific functions and analysis routines can easily be added to the HEART program. Unlike commercially-available turnkey systems, this provides tremendous adaptability as well as a means to perform quality control at each step.

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*Anyone interested in learning more about the HEART program and/or obtaining a beta test version are encouraged to contact the authors. Currently, the HEART program is being upgraded to include additional functions, error trapping, and error checking routines. The authors are also working toward making the HEART program compliant with Good Laboratory Practice (GLP) guidelines defined by the Food and Drug Administration (FDA) for testing of medical devices.

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ReferencesL. Maloy and R.M. Gardner, Monitoring systemic arterial blood pressure: Strip chart recording versus digital display, Heart Lung 15, 627-635 (1986).

D.M. Wilkison, K.C. Preuss, and D.C. Warltier, A microcomputer-based package for determination of regional and global cardiac function and coronary Hemodynamics, J. Pharmacol. Methods 12, 59-67 (1984).

A.P. Farrell and F. Bruce, Data acquisition and analysis of pulsatile signals using a personal computer: an application in cardiovascular physiology. Comput. Biol. Med. 17, 151-159 (1987)

H.J. Mass, J.T. Gean, and P.A. Gwirtz, Computer analysis of cardiovascular parameters. Comput. Biol. Med. 17, 75-84 (1987)

J. Weissenburger, P. Zitoun, and C. Louis, Data acquisition and analysis of hemodynamic signals using a standard microcomputer, J. Pharmacol. Methods 14, 89-98 (1985).

H.D. Swanson, A comparison, for teaching purposes, of three data-acquisition systems for the Macintosh, Am. J. Physiol. 258 (6 pt 3) S17-23 (1990).

P.L. Schiffman, A computer program for the interpretation of hemodynamic pressures. Comput. Biol. Med. 15, 355-359 (1985)

J.Y. Kresh and S.K. Brockman, An interactive microcomputer-based graphics system for analysis of cardiodynamic function. Comput. Biol. Med. 14, 419-427 (1984)

J. Kedem, E. Tzikoni, S. Feldman, and J. Sonn, Real time data acquisition and analysis of cardiovascular experiments in dogs. Comput. Biol. Med. 14, 21-28 (1982)

J.E. Axenborg and I. Hirsch, A PC-based on-line system for physiological in vivo and in vitro experiments. Comput. Methods Programs Biomed. 41, 55-67 (1993)

J.E. Axenborg, BIOLAB – a computerized on-line system for physiological measurements in experimental animals. Comput. Methods Programs Biomed. 28, 75-85 (1989)

C.R. Benedict, R.H. Gayden, and J.T. Gean, Microcomputer-based analysis of cardiovascular indices. Comput. Biol. Med. 22, 1-22 (1992)

K.F. Pitsillides, J.D. Symons, and J.C. Longhurst, Analysis of beat-to-beat cardiovascular hemodynamic variables obtained during long-term biotelemetry. Comput. Methods Programs Biomed. 37, 167-177 (1992)S.E. Greenhut, J.W. Lee, J.C. Monsur, and J.M. Nicklas, Computer analysis of physiological signals in a cardiovascular research laboratory. Comput. Biol. Med. 21, 79-96 (1991)

C.P. Herbst, J. Diedericks, N.J. Uys, J. Brummer, and M.G. Lotter, Use of a personal computer for fast acquisition of cardiovascular data over an extended period. Comput. Biol. Med. 21, 407-415 (1991)

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J.C. James III, N.S. Gatenberg, and G.R. Hageman, A sample computer system for physiological data acquisition and analysis. Comput. Biol. Med. 20, 407-413 (1990)

R.A. Mustard, A. Cosolo, J. Fisher, T. Pike, B.D. Schouten, and H.T. Swanson, PC-based system for collection and analysis of physiological data. Comput. Biol. Med. 20, 65-74 (1990)L.S.S. Wong, S.C.U. Marsch, S. Dalmas, and P. Foex, An IBM PC-based system for experimental hemodynamic studies. Med. Eng. & Phys. 22, 419-424 (2000).

W.C. Hu and B.J. Leone, A graphic oriented data analysis system for hemodynamic research. Comput. Methods Programs Biomed. 59, 197-214 (1999)

J.K. Van den Saffele, F.D.G.D. Schoonjans, C. Gao, and J.M. Kaufman, The Multi Unit Activity analyzer: a WindowsTM based hardware-software system for low cost, high speed analog to digital data conversation, data acquisition and window discrimination, Comput. Methods Programs Biomed. 56, 273-275 (1998).

A. Skyschally, R. Schulz, and G. Heusch, CORDAT II: A new program for data acquisition and on-line calculation of hemodynamic and regional myocardial dimension parameters. Comput. Biol. Med. 23, 359-367 (1993)

R.L. Burden, J.D. Faires, A.C. Reynolds, Numerical Analysis, Second Edition, Prindle, Weber & Schmidt, Boston, Massachusetts, ISBN 0-534-98020-1, 130 (1981).

N. Stergiopulos, B.E. Westerhof, and N. Westerhof, Total arterial inertance as the fourth element of the windkessel model, Am. J. Physiol. 276, H81-H88 (1999).

R. Burattini and G. Gnudi, Computer identification of models for the arterial tree input impedance: comparison between two new simple models and first experimental results, Med. Biol. Eng. Comput. 20, 134-144 (1982).

S. Essler, M.J. Schroeder, V. Phaniraj, S.C. Koenig, R.D. Latham, and D.L. Ewert, Fast estimation of arterial vascular parameters for transient and steady beats with application to hemodynamic state under variant gravitational conditions, Ann. Biomed. Eng. 27, 486-497 (1999).

D.A. Self, D.L. Ewert, R.D. Swope, R.P. Crisman, and R.D. Latham, A new method for beat-to-beat determination of peripheral resistance and systemic arterial compliance under non-steady-state conditions during +Gz centrifugation, Aviat. Space Environ. Med. 65, 396-403 (1994).

G.A. Drew and S.C. Koenig, Biomedical patient monitoring, data acquisition, and playback with LabVIEW. Chapter 2 (pp 92-98): In LabVIEW for Automative, Telecommunications, Semiconductor, Biomedical, and other Applications. Prentice Hall PTR, Upper Saddle River, NJ (2000).

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List of Tables

Table 1: Output Parameters from ‘Cardiac: Left Heart’ Analysis Routine. LVP is left ventricular pressure, AoP is aortic pressure, LAP is left atrial pressure, CVP is central venous pressure, sys is systolic, dia is diastolic, avg is average, bd is beginning diastolic, and ed is end diastolic.

Table 2: User options within the Export Data window

List of Figures

Figure 1: Flow diagram of the HEART program main functions.

Figure 2: Profile window indicating file, data, and documentation information.

Figure 3: Data Viewer displaying selected hemodynamic signals and their corresponding value at a specific point in time.

Figure 4: Data calibration window showing three calibration zones on calibration data and the calculated calibration factors.

Figure 5: Beat selection window displaying left ventricular pressure beat boundaries. A bad beat is shown with red beat boundary symbols. Note: hemodynamic data are uncalibrated.

Figure 6: Output data viewer showing beat-to-beat results and statistics for two hemodynamic parameters containing one bad beat at point of line break.

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Table 1

Output Parameters Stroke volume Heart Rate Cardiac OutputLVPsys/bd/ed AoPsys/dia/avg LAPsys/dia/avgCVPsys/dia/avg LV dP/dt max/min LV end-systolic and

diastolic elastanceLV external work Total peripheral resistanceLV end-systolic volume

LV end-diastolic volume

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Table 2

Selection Saved Information

Export Profile Header information, signal names, calibration factors, beat indices.

Export Data Raw signal data and signal names. Data is calibrated if option is selected.

Export Output Results of all the executed analysis files.

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Figure 1

PROFILESpecify data information and load data.

DATA PREPARATION

View DataView and save ranges of loaded data.

Calibrate DataCalibrate loaded data to known values by

executing a first or second order fit. Display calibration coefficients.

Pick BeatsSelect beginning and ending indices of each beat

manually or automatically. Also, edit beat indices.

Waveform ManipulationUse Matlab commands to plot

mathematically manipulated data.

DATA ANALYSISAnalyze data according touser-selected algorithms.

DATA POST-PROCESSING

View Output DataPlot chosen parameters fromexecuted analysis algorithms.

Export Output DataSave profile, data, or analysis

outputs to an ascii file.

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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