Comput. Biol. Med. Vol. 20. NO. 6. pp. 40-413, Printed in Great Britain
1~~93 0
GOlO-4&25/90 s3.00+ .OO 1990 Pergamon Press plc
A SAMPLE COMPUTER SYSTEM FOR PHYSIOLOGICAL DATA ACQUISITION AND ANALYSIS JEROME C. JAMESIII, NICHOLASS. GANTENBERGand GILBERT R. HAGEMAN* Department of Biomedical Engineering and Department of Physiology and Biophysics, University of Alabama at Birmingham, Birmingham, AL 35294, U.S.A. (Received 25 April 1990; received for publication 2 August 1990)
Abstract-This report outlines a sample configuration of a system which records, stores and analyses, graphically and statistically, neurophysioiogical and cardiovascular recordings during an experiment. The system is composed of sensitive physiological amplifiers, an analog to digital signal conversion board, scientific software, a 80286-based computer with 640 Kb of RAM, and a laser printer. Each component of the system is described along with the specific task(s) it performs. Data acquisition Data analysis Analog-digital processing Computers Neural recordings Physiological parameters
INTRODUCTION Most laboratories require data acquisition and analysis and many options exist in choosing the components to perform the tasks at hand. The investigator must first understand the parameters to be measured, how they are recorded and the necessary equipment to provide an analog signal for the A/D conversion and processing. The advent of affordable computer equipment, analog to digital (A/D) converter boards, and scientific software has simplified the configuration of laboratory data acquisition systems. The upper and lower limits of voltage and necessary sampling rate of the analog signal help determine the selection of the A/D board. Sampling constraints must be considered in developing a data acquisition system that meets the needs of the scientific protocol. The system should provide digitized data rapidly for numerical or graphical analysis. Many A/D boards come with software designed for this purpose. In addition, several scientific software packages provide driver support of many A/D boards and provide the processing routines necessary to enable the user to better understand and make conclusions from the sampled data. A customized system for nerve traffic data acquisition has recently been reported [l]. In addition, there exist publications that describe the use of 80286-based computers [2,3] and microcomputers [4] for neurophysiological data acquisition and analysis. The present report outlines a sample configuration of a system which records, stores and analyses, graphically and statistically, neurophysiological and cardiovascular recordings during an in vivo animal experiment. Each component of the system is described with the tasks it performs. Hardware components Two AC amplifiers with high impedance probes (Grass P511), one DC amplifier (Grass 7P122), and a customized heart rate tachometer are interfaced using a screw terminal board to an A/D board (DAP 1200, Microstar International). The A/D board is * Supported in part by National Institutes of Health HL37289. 407
408
JEROME C. JAMES III et al.
Fig. 1.
slotted in a 10 megahertz 286 computer with 40 megabyte Computer printouts are handled using a laser printer Data are recorded continuously on a strip chart recorder a storage oscilloscope (Hewlett Packard 181A). Figure diagram of the system as configured.
hard disk and 640 Kb of RAM. (Hewlett Packard Laserjet II). (MT-9500, Astromed, Inc.) and 1 illustrates a schematic block
Data pathway
Raw signals are amplified by the AC amplifiers and, then, sampled by the A/D board. Nerve activity is measured from intrathoracic cardiac autonomic nerves with bipolar electrodes, but the system is not limited to cardiac nerves. Each nerve probe is connected to an amplifier for generation of the voltage signal that is sampled by the A/D board. Central aortic blood pressure data is collected via a cannula placed in the femoral artery with a pressure transducer (Gould P23db). The pressure transducer is driven by a DC amplifier set to produce a mean signal output. Heart rate is derived from the R wave of a standard limb lead electrocardiogram. The tachometer outputs a voltage proportional to HR in beats per minute. The computer is programmed using a commercially available scientific programming language (Asyst Software Technologies, Inc.) to sample, store, display, and analyse data from two nerve probe electrode channels and to sample and display mean arterial pressure (MAP) in mmHg and heart rate (HR) in beats per minute. Scientific software
The system is controlled through a multilevel menu driven program. The main menu is encountered on entry to the system and has options to set the hardware variables for an experiment, creates files for data storage, and to enter the data collection menu. The most important option of this menu allows separate adjustment of a voltage trigger for counting multifiber nerve impulses on each channel. Nerve impulse counting is the main task for this nerve activity analysis system (See analysis methods below). When the trigger setting option is selected, raw nerve activity data (in volts) is collected and displayed on the screen for each channel along with the latest trigger setting (see Fig. 2). The ability to adjust the trigger voltage, coupled with the graphical display of raw data, enables the investigator to detect increases or decreases in neural activity throughout the experiment. The data menu allows the user to sample, save, and to statistically analyse the data. The data collection menu is displayed on the screen with three graphics windows (see Fig. 3). Window 1 displays MAP and HR from the latest sampling period. Windows 2 and 3 display nerve impulse counts from the latest sampling period. The start of a sampling period is controlled by the user. A period of data sampling is called a set. Sets of data are saved in runs. There are usually three sets in a run corresponding to control,
409
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intervention, and recovery periods. After at least two sets of data have been sampled, the sturistics menu can be executed for comparisons between sets. The statistics menu includes options for the user to analyse data as it is collected. Options available includes set comparison or run summary calculation, screen display of comparisons or run summary, and printing of each. An additional ancillary statistics program has been written to permit analysis of the data off-line. This program includes routines for a tabular summary of the experiment, comparisons of sets from different runs, and a graphical summary of the nerve activity, as a percent of control, throughout the experiment. METHODS
OF ANALYSIS
Nerve activity data collection Spontaneous multifiber nerve activity contains a noise level that should not be included when determining impulse counts. A voltage trigger set at just above the noise level is used to eliminate the baseline noise when making nerve count determinations. The trigger is a voltage threshold used to count positive-going action potentials of nerve Select lction Fl Samplr Tone F2 Samplr hltrrurn F3
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410
fibers. In this system, the voltage trigger is implemented with software of the A/D board. Sampling time period and the number of bins for counting nerve impulses in the time period are selected as appropriate to the experimental protocol. Bins are simply arbitrary divisions of the sampling time. Sample times should be sufficiently adjustable to delineate changes in nerve activity over long experiments or short perturbations. Sampling periods are begun with the touch of one function key, and are controlled by the operator. After graphical display of the sampled data, further runs can be made or the user may save the data, start a new run, change menus to perform statistical operations or return to the main menu and edit sampling parameters. Data summary
Each run of a data set is summarized in a tabular form as shown below. The summary for each set includes: type of sample (1 = tone versus 2 = intervention); total counts of nerve activity; the mean bin count; the standard deviation (STD) of the bin counts; the largest count in one bin (Max count); the time of the maximum count (Max time); and the percent of control of the total counts for each channel. The total count is determined by the sum of the individual bin counts. Run summary Total count
Mean count
STD
Max count
Max time
Percent control
1 1 1
Type 1 2 1
1265.0 1639.0 1343.0
12.6 16.4 13,4
3.4 4.1 4.2
23.0 24.0 26.0
9.0 3.8 3.8
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2 2 2
1 2 1
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6.8 12.8 7.8
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Data comparison
Individual sets in a run may be statistically compared to determine if significant changes in nerve activity had occurred. The comparison is made on the bin level to determine the number of significantly different bins, the fraction of time of the sampling period at which bins are significantly different, a Student’s t statistic and a P value. The number of significantly different bins is calculated as the number of bins in the objective set greater than the mean plus two standard deviations of the reference set. Beginning and ending times at which significant differences existed between two sets are recorded during this comparison. The fraction of time of the sampling period at which the bins are significantly different is calculated as the number of significant bins divided by the number of bins. A Student’s t statistic and a P value are calculated from the mean bin counts of each set. A P value of 0.05 or less is considered significant. During an experiment comparisons can be made only on sets within a particular run, but the ancillary off-line customized (Asyst) program allows comparison of individual sets between runs. Data normalization
Control periods represent nerve activity steady state. The control value of total counts could be adjusted at any time from the data collection menu. This allows normalization of nerve counts if several drugs or interventions are performed in the same experiment. In the off-line analysis program, the change in percent control could be graphed for the entire experiment. Points at which an adjustment to control (100%) of neural counts has occurred are marked with asterisks on the summary graphs. SAMPLE
RESULTS
To better illustrate the functions of the system as described above, the nerve data acquisition system was used to evaluate changes in nerve activity with changes in blood
Physiological data acquisition and analysis
411
or heart rate. During the classical baroreceptor reflex [5,6], cardiac sympathetic nerve activities increase as arterial pressure is lowered. The raw data for this blood pressure lowering intervention is presented above in the run summary example. Figure 4(A) shows mean arterial pressure, heart rate, and two nerve activity histograms for a 15 s interval named “control” or “tone”. The pressure during control is approximately 95 mmHg. Figure 4(B) illustrates blood pressure, heart rate, and the nerve activity histograms for a 15 s interval at a time when blood pressure had been lowered to approximately 80 mmHg, using nitroglycerin, a well known vasodilating agent [7]. The nerve firings are greater during this period and therefore, a greater number of counts occurs in various bins. The neural data in Fig. 4(A) and (B) are significantly different from each other, that is, there is a significant increase in nerve activities as a result of the drop in blood pressure. Summary print-outs, when done, provide raw counts and percent control values for each set as shown above. The design of the system allows the user to evaluate the rapid adjustments in heart rate and nerve activities. Having repeatedly perfomed a certain intervention or protocol the investigator can create data files for a large group of experiments. The files can then be imported into a spreadsheet program for more detailed stastistical analysis and graphing of the group data. pressure
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DISCUSSION There are other data acquisition systems being used to accomplish data collection and analysis using microprocessor-based systems similar in nature to the one presented here [l-4]. The system reported here has several advantageous features. Data of a multitude of frequency ranges (O-150 kHz) can be handled by the A/D board. Much of the control of data acquisition and processing of raw data (averaging, etc.) is done by a microprocessor located on the A/D board prior to providing it to the computer. This allows parallel collection and displaying of data which is important for near real-time analysis of a high frequency signal such as nerve activity. The scientific software language that was used allows the investigator to tailor a system by choosing his own parameters to be analysed, view ports for screen information and printouts and graphics functions that can be sent to a printer or plotter. The system allows convenient on-line data analysis and display. In addition, data can also be stored on disk for further analysis and graphing. A unique advantage of the system is the simultaneous recording and comparison of two neural activities. These comparisons are especially important during certain cardiovascular pathologies such as, acute myocardial ischemia [8] or arrhythmogenesis during drug abuse [9]. Another particular advantage of this system is the visual interaction between the user and the collection process, when setting the voltage trigger for the spike counting routine (see Fig. 2). The ease of selecting starting times for sampling, defining control conditions and printing analysed data are among a few of the other advantages of this system. The ancillary off-line customized analysis program allows the user to make comparisons between runs at a more convenient time after the completion of the experiment. It also permits a complete printout of the entire experiment in either, a detailed or a brief form. The neural data of the entire experiment can be graphically depicted in one graph for each nerve channel, which allows the user to assess the time course of a protocol, etc. Finally, the system is quite easy to use from start to finish. A disadvantage of the system is the lack of a display and storage of the continuous sampling of raw data by the A/D board and computer. Because a near real-time analysis is desired this system displays and stores processed data only. In addition, data can only be collected in sample periods ranging from 1 to 30 s as currently configured. Collection of digital data at faster speeds than used by this system (greater than 10 kHz) will quickly use up memory and storage space. Currently, storage of the raw nerve signals has to be done with the use of tape recorder using magnetic tape, as well as on an eight channel polygraph. To “reanalyse” a past experiment the user must replay the tape and allow the system to analyse the data at that time. SUMMARY In summary, a simple and reasonably economical analog to digital data acquisition and analysis system is described. All hardware described is available commercially. The use of the system to collect and analyse neural recordings and cardiovascular parameters is highlighted. Raw signals are amplified and then sent to an A/D conversion board. Through the use of three convenient menus the user sets acquisition parameters and performs the collection. The A/D board processes the sample data prior to the analysis procedures performed by the host computer’s software. Data are sent immediately after sampling to the screen in tabular and graphical forms. The user chooses to save or statistically analyse the data. A new sampling period is begun by a key stroke. After completion of a protocol, summary tables and graphs can be printed. The stored data can be converted to a form for use by a spreadsheet package. The system is designed to be user friendly and provide flexibility in the collection and analysis processes. Figures are provided to illustrate capabilities of the system software. This system could be enhanced to accommodate more variables and it could be modified for different types of variables in physiological or pharmacological experimentation. Acknowledgements-Our the manuscript.
appreciation is extended to Dr Brett H. Neely for his suggestions and kind review of
Physiological data acquisition and analysis
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REFERENCES 1. D. R. Brown and D. C. Randall, Microprocessor-based analysis of sympathetic nerve traffic, Am. J. Physiot. 257, R958-R963 (1989). 2. C. Forster and H. 0. Handwerker, Automatic classification and analysis of microneurographic spike data using a PC/AT, J. Neurosci. Met. 31, 109-118 (1990). 3. J. L. Novak and B. C. Wheeler, A high-speed multichannel neural data acquisition system for IBM PC compatibles, J. Neurosci. Met. 26, 239-247 (1988). 4. J. W. Alridge, J. L. Walder, and S. Gilman, Enhancing high-speed digitization of single-unit neuronal activity on a microcomputer using a hybrid software-hardware technique, .I. Neurosci. Met. 28, 205-208 (1989). 5. S. E. Downing. Baroreceptor regulation of the heart, pp. 621-652, Handbook of Physiology,Vol. 1, The Heart, R. M. Beme, ed. The American Physiological Society, Bethesda, MD (1979).
6. G. R. Hageman, B. H. Neely, and F. Urthaler, Cardiac autonomic efferent activity during the baroreflex in the puppies and adult dogs, Am. J. Physiol. 251, H433-H447 (1986). 7. P. Needleman, P. B. Corr, and E. M. Johnson, Drugs used for the treatment of angina: organic nitrates, calcium channel blockers and /?-adrenergic antagonists, pp. 806-816, The Pha&zcolo&al B&s of Therapeutics. 7th edn. A. G. Gilman, L. S. Goodman, T. W. Rag. F. Murad. eds. Macmillan. New York (1985j.
8. B. H. Neely and G. R. Hageman, Differential cardiac sympathetic activity during acute myocardial ischemia, Am. J. Physiol. (in press). 9. N. S. Gantenberg and G. R. Hageman, Intravenous cocaine depresses cardiac sympathetic efferent activity in the dog, J. Aufonom. New. Sys. (in press). About the Author-JEROME C. JAMES, IIIwas born in 1961 in Birmingham, Alabama. He earned the B.S. degree in Chemical Engineering from the University of Alabama, Tuscaloosa, Alabama in 1984 and the M.S. degree in Biomedical Engineering from the University of Alabama at Birmingham in 1986. He is currently employed at the Department of Veterans Affairs Medical Center in Birmingham, Alabama. Mr James is currently pursuing a doctorate in biomedical engineering. His research interests are modelling and control of hemodialysis, rehabilitation engineering, and medical instrumentation. Mr James is a member of the American Institute of Chemical Engineers and Tau Beta Pi and Omega Chi Epsilon Engineering societies. About the Author-NlcHoLAs SEYMOUR GANTENBERG was born in 1962 in Covington, Kentucky. He received the B.A. degree in Biology and the A.A. degree in Chemistry from Thomas More College, Fort Mitchell, Kentucky in 1984 and the Ph.D. degree in Physiology and Biophysics from the University of Alabama at Birmingham in 1990. His doctoral research concentrated on the role of the sympathetic nervous system on atrhythmogenesis. Dr Gantenberg is presently undertaking postdoctoral work in the Laboratory of Physiologic and Pharmacologic Studies at the National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, Alcohol, Drug Abuse and Mental Health Administration, Rockville, Maryland. His research activities concentrate on the regulation of beta adrenergic receptors and central mechanisms of autonomic neural control. Dr Gantenberg is a member of the American Physiological Society and Phi Kappa Phi National Honor Society. About the Author-GILBERT ROBERT HAGEMAN was born in 1947 in Covington , Kentucky. He received the A.B. degree in Biolorrv from Thomas More Colleee. Fort Mitchell. Kentuckv in ’ 1968 and the Ph.D. degree in Phy&logy from Loyola University of Chicago in 1974. Dr Hageman has maintained continued research activity concentrating on autonomic neural activities, cardiac autonomic receptor responses, cardiac electrophysiology and arrhythmogenesis since 1971. He has been a guest referee for many journals including, the American Journal of Physiofogy and Circulation Research. He is Vice-Chairman for Academic Affairs in the Department of Physiology and Biophysics. In 1990 he received the Taft Award for Excellence in Teaching at the University of Alabama at Birmingham, School of Medicine. Dr Hageman is a member of the American Physiological Society, Sigma Xi, American Heart Association and the International Society for Heart Research.
1~~93 0
GOlO-4&25/90 s3.00+ .OO 1990 Pergamon Press plc
A SAMPLE COMPUTER SYSTEM FOR PHYSIOLOGICAL DATA ACQUISITION AND ANALYSIS JEROME C. JAMESIII, NICHOLASS. GANTENBERGand GILBERT R. HAGEMAN* Department of Biomedical Engineering and Department of Physiology and Biophysics, University of Alabama at Birmingham, Birmingham, AL 35294, U.S.A. (Received 25 April 1990; received for publication 2 August 1990)
Abstract-This report outlines a sample configuration of a system which records, stores and analyses, graphically and statistically, neurophysioiogical and cardiovascular recordings during an experiment. The system is composed of sensitive physiological amplifiers, an analog to digital signal conversion board, scientific software, a 80286-based computer with 640 Kb of RAM, and a laser printer. Each component of the system is described along with the specific task(s) it performs. Data acquisition Data analysis Analog-digital processing Computers Neural recordings Physiological parameters
INTRODUCTION Most laboratories require data acquisition and analysis and many options exist in choosing the components to perform the tasks at hand. The investigator must first understand the parameters to be measured, how they are recorded and the necessary equipment to provide an analog signal for the A/D conversion and processing. The advent of affordable computer equipment, analog to digital (A/D) converter boards, and scientific software has simplified the configuration of laboratory data acquisition systems. The upper and lower limits of voltage and necessary sampling rate of the analog signal help determine the selection of the A/D board. Sampling constraints must be considered in developing a data acquisition system that meets the needs of the scientific protocol. The system should provide digitized data rapidly for numerical or graphical analysis. Many A/D boards come with software designed for this purpose. In addition, several scientific software packages provide driver support of many A/D boards and provide the processing routines necessary to enable the user to better understand and make conclusions from the sampled data. A customized system for nerve traffic data acquisition has recently been reported [l]. In addition, there exist publications that describe the use of 80286-based computers [2,3] and microcomputers [4] for neurophysiological data acquisition and analysis. The present report outlines a sample configuration of a system which records, stores and analyses, graphically and statistically, neurophysiological and cardiovascular recordings during an in vivo animal experiment. Each component of the system is described with the tasks it performs. Hardware components Two AC amplifiers with high impedance probes (Grass P511), one DC amplifier (Grass 7P122), and a customized heart rate tachometer are interfaced using a screw terminal board to an A/D board (DAP 1200, Microstar International). The A/D board is * Supported in part by National Institutes of Health HL37289. 407
408
JEROME C. JAMES III et al.
Fig. 1.
slotted in a 10 megahertz 286 computer with 40 megabyte Computer printouts are handled using a laser printer Data are recorded continuously on a strip chart recorder a storage oscilloscope (Hewlett Packard 181A). Figure diagram of the system as configured.
hard disk and 640 Kb of RAM. (Hewlett Packard Laserjet II). (MT-9500, Astromed, Inc.) and 1 illustrates a schematic block
Data pathway
Raw signals are amplified by the AC amplifiers and, then, sampled by the A/D board. Nerve activity is measured from intrathoracic cardiac autonomic nerves with bipolar electrodes, but the system is not limited to cardiac nerves. Each nerve probe is connected to an amplifier for generation of the voltage signal that is sampled by the A/D board. Central aortic blood pressure data is collected via a cannula placed in the femoral artery with a pressure transducer (Gould P23db). The pressure transducer is driven by a DC amplifier set to produce a mean signal output. Heart rate is derived from the R wave of a standard limb lead electrocardiogram. The tachometer outputs a voltage proportional to HR in beats per minute. The computer is programmed using a commercially available scientific programming language (Asyst Software Technologies, Inc.) to sample, store, display, and analyse data from two nerve probe electrode channels and to sample and display mean arterial pressure (MAP) in mmHg and heart rate (HR) in beats per minute. Scientific software
The system is controlled through a multilevel menu driven program. The main menu is encountered on entry to the system and has options to set the hardware variables for an experiment, creates files for data storage, and to enter the data collection menu. The most important option of this menu allows separate adjustment of a voltage trigger for counting multifiber nerve impulses on each channel. Nerve impulse counting is the main task for this nerve activity analysis system (See analysis methods below). When the trigger setting option is selected, raw nerve activity data (in volts) is collected and displayed on the screen for each channel along with the latest trigger setting (see Fig. 2). The ability to adjust the trigger voltage, coupled with the graphical display of raw data, enables the investigator to detect increases or decreases in neural activity throughout the experiment. The data menu allows the user to sample, save, and to statistically analyse the data. The data collection menu is displayed on the screen with three graphics windows (see Fig. 3). Window 1 displays MAP and HR from the latest sampling period. Windows 2 and 3 display nerve impulse counts from the latest sampling period. The start of a sampling period is controlled by the user. A period of data sampling is called a set. Sets of data are saved in runs. There are usually three sets in a run corresponding to control,
409
Physiological data acquisition and analysis -------------------~---------_______________ SIPfiL NSRUP IRIFPIC
SOLID
LINES
- TRIGGER
LEUPLS
TIME
DIUISIONS
- 8.5
SPCS
Fig. 2.
intervention, and recovery periods. After at least two sets of data have been sampled, the sturistics menu can be executed for comparisons between sets. The statistics menu includes options for the user to analyse data as it is collected. Options available includes set comparison or run summary calculation, screen display of comparisons or run summary, and printing of each. An additional ancillary statistics program has been written to permit analysis of the data off-line. This program includes routines for a tabular summary of the experiment, comparisons of sets from different runs, and a graphical summary of the nerve activity, as a percent of control, throughout the experiment. METHODS
OF ANALYSIS
Nerve activity data collection Spontaneous multifiber nerve activity contains a noise level that should not be included when determining impulse counts. A voltage trigger set at just above the noise level is used to eliminate the baseline noise when making nerve count determinations. The trigger is a voltage threshold used to count positive-going action potentials of nerve Select lction Fl Samplr Tone F2 Samplr hltrrurn F3
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45.8 35.8 25.0
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12 1
#: *:
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SAMPLE:
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:
33
DISPLIIYED: IICL
52
JEROMEC. JAMESIII et al.
410
fibers. In this system, the voltage trigger is implemented with software of the A/D board. Sampling time period and the number of bins for counting nerve impulses in the time period are selected as appropriate to the experimental protocol. Bins are simply arbitrary divisions of the sampling time. Sample times should be sufficiently adjustable to delineate changes in nerve activity over long experiments or short perturbations. Sampling periods are begun with the touch of one function key, and are controlled by the operator. After graphical display of the sampled data, further runs can be made or the user may save the data, start a new run, change menus to perform statistical operations or return to the main menu and edit sampling parameters. Data summary
Each run of a data set is summarized in a tabular form as shown below. The summary for each set includes: type of sample (1 = tone versus 2 = intervention); total counts of nerve activity; the mean bin count; the standard deviation (STD) of the bin counts; the largest count in one bin (Max count); the time of the maximum count (Max time); and the percent of control of the total counts for each channel. The total count is determined by the sum of the individual bin counts. Run summary Total count
Mean count
STD
Max count
Max time
Percent control
1 1 1
Type 1 2 1
1265.0 1639.0 1343.0
12.6 16.4 13,4
3.4 4.1 4.2
23.0 24.0 26.0
9.0 3.8 3.8
100.6 130.3 106.8
2 2 2
1 2 1
678.0 1276.0 780.0
6.8 12.8 7.8
5.1 4.3 5.3
20.0 22.0 21.0
0.6 15.0 3.3
96.3 181.0 110.8
Channel #
Data comparison
Individual sets in a run may be statistically compared to determine if significant changes in nerve activity had occurred. The comparison is made on the bin level to determine the number of significantly different bins, the fraction of time of the sampling period at which bins are significantly different, a Student’s t statistic and a P value. The number of significantly different bins is calculated as the number of bins in the objective set greater than the mean plus two standard deviations of the reference set. Beginning and ending times at which significant differences existed between two sets are recorded during this comparison. The fraction of time of the sampling period at which the bins are significantly different is calculated as the number of significant bins divided by the number of bins. A Student’s t statistic and a P value are calculated from the mean bin counts of each set. A P value of 0.05 or less is considered significant. During an experiment comparisons can be made only on sets within a particular run, but the ancillary off-line customized (Asyst) program allows comparison of individual sets between runs. Data normalization
Control periods represent nerve activity steady state. The control value of total counts could be adjusted at any time from the data collection menu. This allows normalization of nerve counts if several drugs or interventions are performed in the same experiment. In the off-line analysis program, the change in percent control could be graphed for the entire experiment. Points at which an adjustment to control (100%) of neural counts has occurred are marked with asterisks on the summary graphs. SAMPLE
RESULTS
To better illustrate the functions of the system as described above, the nerve data acquisition system was used to evaluate changes in nerve activity with changes in blood
Physiological data acquisition and analysis
411
or heart rate. During the classical baroreceptor reflex [5,6], cardiac sympathetic nerve activities increase as arterial pressure is lowered. The raw data for this blood pressure lowering intervention is presented above in the run summary example. Figure 4(A) shows mean arterial pressure, heart rate, and two nerve activity histograms for a 15 s interval named “control” or “tone”. The pressure during control is approximately 95 mmHg. Figure 4(B) illustrates blood pressure, heart rate, and the nerve activity histograms for a 15 s interval at a time when blood pressure had been lowered to approximately 80 mmHg, using nitroglycerin, a well known vasodilating agent [7]. The nerve firings are greater during this period and therefore, a greater number of counts occurs in various bins. The neural data in Fig. 4(A) and (B) are significantly different from each other, that is, there is a significant increase in nerve activities as a result of the drop in blood pressure. Summary print-outs, when done, provide raw counts and percent control values for each set as shown above. The design of the system allows the user to evaluate the rapid adjustments in heart rate and nerve activities. Having repeatedly perfomed a certain intervention or protocol the investigator can create data files for a large group of experiments. The files can then be imported into a spreadsheet program for more detailed stastistical analysis and graphing of the group data. pressure
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JEROME
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DISCUSSION There are other data acquisition systems being used to accomplish data collection and analysis using microprocessor-based systems similar in nature to the one presented here [l-4]. The system reported here has several advantageous features. Data of a multitude of frequency ranges (O-150 kHz) can be handled by the A/D board. Much of the control of data acquisition and processing of raw data (averaging, etc.) is done by a microprocessor located on the A/D board prior to providing it to the computer. This allows parallel collection and displaying of data which is important for near real-time analysis of a high frequency signal such as nerve activity. The scientific software language that was used allows the investigator to tailor a system by choosing his own parameters to be analysed, view ports for screen information and printouts and graphics functions that can be sent to a printer or plotter. The system allows convenient on-line data analysis and display. In addition, data can also be stored on disk for further analysis and graphing. A unique advantage of the system is the simultaneous recording and comparison of two neural activities. These comparisons are especially important during certain cardiovascular pathologies such as, acute myocardial ischemia [8] or arrhythmogenesis during drug abuse [9]. Another particular advantage of this system is the visual interaction between the user and the collection process, when setting the voltage trigger for the spike counting routine (see Fig. 2). The ease of selecting starting times for sampling, defining control conditions and printing analysed data are among a few of the other advantages of this system. The ancillary off-line customized analysis program allows the user to make comparisons between runs at a more convenient time after the completion of the experiment. It also permits a complete printout of the entire experiment in either, a detailed or a brief form. The neural data of the entire experiment can be graphically depicted in one graph for each nerve channel, which allows the user to assess the time course of a protocol, etc. Finally, the system is quite easy to use from start to finish. A disadvantage of the system is the lack of a display and storage of the continuous sampling of raw data by the A/D board and computer. Because a near real-time analysis is desired this system displays and stores processed data only. In addition, data can only be collected in sample periods ranging from 1 to 30 s as currently configured. Collection of digital data at faster speeds than used by this system (greater than 10 kHz) will quickly use up memory and storage space. Currently, storage of the raw nerve signals has to be done with the use of tape recorder using magnetic tape, as well as on an eight channel polygraph. To “reanalyse” a past experiment the user must replay the tape and allow the system to analyse the data at that time. SUMMARY In summary, a simple and reasonably economical analog to digital data acquisition and analysis system is described. All hardware described is available commercially. The use of the system to collect and analyse neural recordings and cardiovascular parameters is highlighted. Raw signals are amplified and then sent to an A/D conversion board. Through the use of three convenient menus the user sets acquisition parameters and performs the collection. The A/D board processes the sample data prior to the analysis procedures performed by the host computer’s software. Data are sent immediately after sampling to the screen in tabular and graphical forms. The user chooses to save or statistically analyse the data. A new sampling period is begun by a key stroke. After completion of a protocol, summary tables and graphs can be printed. The stored data can be converted to a form for use by a spreadsheet package. The system is designed to be user friendly and provide flexibility in the collection and analysis processes. Figures are provided to illustrate capabilities of the system software. This system could be enhanced to accommodate more variables and it could be modified for different types of variables in physiological or pharmacological experimentation. Acknowledgements-Our the manuscript.
appreciation is extended to Dr Brett H. Neely for his suggestions and kind review of
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REFERENCES 1. D. R. Brown and D. C. Randall, Microprocessor-based analysis of sympathetic nerve traffic, Am. J. Physiot. 257, R958-R963 (1989). 2. C. Forster and H. 0. Handwerker, Automatic classification and analysis of microneurographic spike data using a PC/AT, J. Neurosci. Met. 31, 109-118 (1990). 3. J. L. Novak and B. C. Wheeler, A high-speed multichannel neural data acquisition system for IBM PC compatibles, J. Neurosci. Met. 26, 239-247 (1988). 4. J. W. Alridge, J. L. Walder, and S. Gilman, Enhancing high-speed digitization of single-unit neuronal activity on a microcomputer using a hybrid software-hardware technique, .I. Neurosci. Met. 28, 205-208 (1989). 5. S. E. Downing. Baroreceptor regulation of the heart, pp. 621-652, Handbook of Physiology,Vol. 1, The Heart, R. M. Beme, ed. The American Physiological Society, Bethesda, MD (1979).
6. G. R. Hageman, B. H. Neely, and F. Urthaler, Cardiac autonomic efferent activity during the baroreflex in the puppies and adult dogs, Am. J. Physiol. 251, H433-H447 (1986). 7. P. Needleman, P. B. Corr, and E. M. Johnson, Drugs used for the treatment of angina: organic nitrates, calcium channel blockers and /?-adrenergic antagonists, pp. 806-816, The Pha&zcolo&al B&s of Therapeutics. 7th edn. A. G. Gilman, L. S. Goodman, T. W. Rag. F. Murad. eds. Macmillan. New York (1985j.
8. B. H. Neely and G. R. Hageman, Differential cardiac sympathetic activity during acute myocardial ischemia, Am. J. Physiol. (in press). 9. N. S. Gantenberg and G. R. Hageman, Intravenous cocaine depresses cardiac sympathetic efferent activity in the dog, J. Aufonom. New. Sys. (in press). About the Author-JEROME C. JAMES, IIIwas born in 1961 in Birmingham, Alabama. He earned the B.S. degree in Chemical Engineering from the University of Alabama, Tuscaloosa, Alabama in 1984 and the M.S. degree in Biomedical Engineering from the University of Alabama at Birmingham in 1986. He is currently employed at the Department of Veterans Affairs Medical Center in Birmingham, Alabama. Mr James is currently pursuing a doctorate in biomedical engineering. His research interests are modelling and control of hemodialysis, rehabilitation engineering, and medical instrumentation. Mr James is a member of the American Institute of Chemical Engineers and Tau Beta Pi and Omega Chi Epsilon Engineering societies. About the Author-NlcHoLAs SEYMOUR GANTENBERG was born in 1962 in Covington, Kentucky. He received the B.A. degree in Biology and the A.A. degree in Chemistry from Thomas More College, Fort Mitchell, Kentucky in 1984 and the Ph.D. degree in Physiology and Biophysics from the University of Alabama at Birmingham in 1990. His doctoral research concentrated on the role of the sympathetic nervous system on atrhythmogenesis. Dr Gantenberg is presently undertaking postdoctoral work in the Laboratory of Physiologic and Pharmacologic Studies at the National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, Alcohol, Drug Abuse and Mental Health Administration, Rockville, Maryland. His research activities concentrate on the regulation of beta adrenergic receptors and central mechanisms of autonomic neural control. Dr Gantenberg is a member of the American Physiological Society and Phi Kappa Phi National Honor Society. About the Author-GILBERT ROBERT HAGEMAN was born in 1947 in Covington , Kentucky. He received the A.B. degree in Biolorrv from Thomas More Colleee. Fort Mitchell. Kentuckv in ’ 1968 and the Ph.D. degree in Phy&logy from Loyola University of Chicago in 1974. Dr Hageman has maintained continued research activity concentrating on autonomic neural activities, cardiac autonomic receptor responses, cardiac electrophysiology and arrhythmogenesis since 1971. He has been a guest referee for many journals including, the American Journal of Physiofogy and Circulation Research. He is Vice-Chairman for Academic Affairs in the Department of Physiology and Biophysics. In 1990 he received the Taft Award for Excellence in Teaching at the University of Alabama at Birmingham, School of Medicine. Dr Hageman is a member of the American Physiological Society, Sigma Xi, American Heart Association and the International Society for Heart Research.
