A RAPID CYCLE LENGTH VARIABILITY DETECTION TECHNIQUE OF ATRIAL ELECTROGRAMS IN ATRIAL FIBRILLATION

Transcription

1 A RAPID CYCLE LENGTH VARIABILITY DETECTION TECHNIQUE OF ATRIAL ELECTROGRAMS IN ATRIAL FIBRILLATION by SEUNGYUP LEE Submitted in partial fulfillment of the requirements for the degree of Master of Science Thesis Advisor: Dr. Albert L. Waldo Department of Biomedical Engineering CASE WESTERN RESERVE UNIVERSITY May, 2008

2 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of candidate for the degree *. (signed) (chair of the committee) (date) *We also certify that written approval has been obtained for any proprietary material contained therein.

3 Copyright 2008 by Seungyup Lee All rights reserved

4 To my family,

5 TABLE OF CONTENTS List of Tables.3 List of Figures...4 Acknowledgements 5 Abstract Chapter 1 Introduction Definition.. 13 WORKS CITED Chapter 2 A RAPID CYCLE LENGTH VARIABILITY DETECTION TECHNIQUE OF ATRIAL ELECTROGRAMS IN ATRIAL FIBRILLATION. 16 Introduction. 17 Method 19 Experimental preparation Creation of the Sterile Pericarditis Model.. 19 Studies in the Open-Chest State. 20 Induction of Atrial Arrhythmias. 21 Simultaneous Multisite Mapping and analysis 21 Data Acquisition.. 21 Manual data Analysis. 22 Data analysis: Cycle Length Variability Detection (CLVD) analysis. 23 Method 1: Power Envelope Method using Root Mean Square (RMS) 23 Method 2: Direct Method Using Band-Pass Filter (BPF).. 24 Method 3: Morphology Method using Cross-Correlation (CC) 25 Statistical Analysis for Reducing Errors 26 Verification of the power envelope method 27 Results. 27 Comparison CLVD analysis with Classical analysis (AF due to LA reentrant circuit)

6 . Discussions. 32 Clinical Implications.. 34 Limitations.. 34 Conclusion.. 35 Figures WORKS CITED 54 Chapter 3 Summary/Future Direction 56 Appendix A Software Algorithm.. 60 Appendix B Hardware Design of Cardiac Mapping System 69 Introduction 70 Cardiac Mapping System Data Flow.. 70 ACU Timing Diagram 73 Computer Interface PCI Digital I/O Card 76 DMA Transfer. 78 Data Transfer Conclusion.. 82 BIBLIOGRAPHY 83 2

7 LIST OF TABLES Table 1 Numbers and successful percentages of regular area found in left atria (real-time)...47 Table 2 Numbers and successful percentages of regular area found in left atria (off-line).48 3

8 LIST OF FIGURES Figure 1 Example of the limitation of FFT analysis about cycle length variability during sustained AF Figure 2 Electrode arrays in atrium Figure 3 Example of the verification between manual and automatic measurement of the cycle length.. 42 Figure 4 Examples of positive-peak signal in three methods Figure 5 Examples of negative-peak signal in three methods Figure 6 Examples of variable-peak signal in three methods Figure 7 Examples of the problem in the morphology method Figure 8 Example of sequence of activation maps Figure 9 Cycle length variability analysis of an AF episode Figure 10 Original AEGs from sites from figure Figure 11 Example of compare between figure 8 and figure

9 ACKNOWLEDGEMENTS First of all, I thank my God and grand parents watching out for me from the above. They have been always there for me regardless. I am very grateful to Dr. Waldo for providing me an opportunity to work in his laboratory. I would also like to thank Dr. Durand, my academic advisor, and Dr. Jayakumar Sahadevan for their guidance and support throughout my graduate studies. Their encouragement, endless support, trust and confidence in my ability to work had provided me the motivation to explore my research and step up to higher level of me. I would like to thank Celeen Khrestian and Dr. Paul for their moral support and family-like relationship all the time. They are my family to me. I would also like to thank my other labmates, Sergey Vitebskiy, Tomoo Yasuda, Ivan Cakulev, and Anselma Intini for their useful discussion and a friendly relationship. How can I forget about my Korean Gang in Cleveland? I am grateful Dongchul Lee, Sungwook Jeon, Jaeyun Kim, Hyunjoo Park, Hyunsoo Kim, Sejung Kim, and for our memories and friendship which will last forever. MY FAMILY!!! I would like to say, I love you, all. My parents, Kill Lee and Youngsook Lee, my brother family, Kwangdeok Lee, Yusun Jung and Cieun Lee, my sister, Gilyeong Lee, and my fiancé, Sunah Song and her family had 5

10 provided me endless love and support with strong belief and confidence in me. Their moral support helped me always to achieve my life goal and to focus in my life track. 6

11 A RAPID CYCLE LENGTH VARIABILITY DETECTION TECHNIQUE OF ATRIAL ELECTROGRAMS IN ATRIAL FIBRILLATION Abstract by Seungyup Lee The Cycle Length Variability Detection (CLVD) analysis using power envelop algorithm was developed to rapidly characterize the cycle length (CL) of atrial electrograms (AEGs) on real time and off line. The objective was that the CLVD analysis will be able to detect individual CLs correctly regardless of changing morphology, amplitude, and/or CL of AEGs.. The 4-second segments of each bipolar AEG were subjected to 1) direct, 2) morphology, and 3) power envelop methods using a LabView algorithm for CLVD analysis. The CLVD analysis using the power envelope method of three developed methods in this study provided to be best method. It is a powerful and useful tool for determining regular and irregular patterns of atrial activation in both atria. This new method could lead to a new way of accurately and rapidly analyzing AF in real time, and aid in understanding mechanism(s) and increasing the efficacy of treatment in patients. 7

12 Chapter 1. INTRODUCTION 8

13 Atrial fibrillation (AF) is a common clinical problem, affecting more than one million people in the Unite States alone, including up to 10% of those over It is the leading cause of cardiogenic embolization and, in the setting of hypertension and/or organic heart disease, is associated with a 44% yearly incidence of stroke In spite of a growing recognition of its prevalence and associated morbidity and mortality, the mechanism(s) involved in the initiation, maintenance, and termination of AF remain one of the incompletely understood areas of cardiac electrophysiology. AF is characterized by disorganized, high rate ( bpm) atrial electrical activity with variability of the beat-to-beat cycle length (CL), polarity, morphology, and/or amplitude of recorded bipolar electrograms. AF is generally thought to be initiated by one or more ectopic foci (i.e. a focal generating source or sources other than the sinus node) or by one or more reentrant circuits (i.e. activation going around an anatomic or functional obstacle). Understanding of the mechanism(s) of AF helps us to characterize the disorganized electrical activation of the atria leading to the development of appropriate therapeutic techniques to treat patients with a high degree of efficacy and thereby cure AF and its associated adverse effects. During the last decade, with improvements in technology and the development of several animal models 4-11, three-dimensional computer simulation models of the atria and continuous studies in humans 10, 15, 16, several mechanisms of AF have 9

14 been proposed. Based on insights from animal and human studies, AF is believed to result from from several mechanisms; 1) multiple re-entrant wavelets 4,16-18, 2) a single focus or multiple foci, presumably automatic or triggered, firing rapidly 7,8, 3) unstable re-entrant circuits of short cycle length 4,10 or 4) a relatively stable re-entrant circuit of short cycle length 11,19 producing fibrillatory conduction in most of the atria. Cardiac mapping techniques have been widely used in both clinical and research laboratories to study conduction abnormalities in the heart. Cardiac mapping has provided a useful tool for understanding the mechanisms of cardiac arrhythmias 4, studying the electrophysiologic effects of antiarrhythmic drugs 20, and assisting in clinical diagnosis of rhythm disorders 26. Activation sequence maps 4,19 of 100ms duration help visualize the activation pathways to determine the mechanism of AF. On both patients and animal model areas of beat-to-beat CL regularity over prolonged duration have been found but manually producing the activation sequence maps is a labor intensive process requiring many hours. This thesis will focus on the development of the rapid and reliable analysis method of atrial electrograms (AEGs) to characterize the regular areas of AF by analyzing the cycle length variability detection (CLVD) of each recorded AEGs. 10

15 Definitions Atrial fibrillation (AF) is characterized as disorganized, high rate ( bpm) atrial electrical activity with variability of the beat-to-beat CL, polarity, morphology, and/or amplitude of the recorded bipolar electrograms. Sustained AF was defined as lasting > 5 minutes and non-sustained AF was defined as lasting > 10 seconds but < 5 minutes in AF without any interruption. A line of functional block was defined as a region of block not associated with an anatomic obstacle and not present during sinus rhythm but present only during a rapid atrial rhythm. Double potentials are defined as sequential activation on both sides of the line of block. Fibrillatory conduction is defined as conduction disturbance due to heterogeneities in repolarization of cardiac tissue, therefore, not following 1:1 activation pattern. We had applied to epicardial mapping signal to show the organization of atrial activation pattern in the left atrium and the right atrium in AF models of human or dog. Our mapping technique supports simultaneous recording of both atria with 512 recording sites and, therefore, enables us to study the advantage of the CLVD analysis to firmly determine the regular areas of AF. The objective of this study was to test our hypothesis that cycle length variability detection (CLVD) analysis will be able to detect individual CLs correctly regardless of changing morphology, 11

16 amplitude, and/or CL of AEGs. Another objective is to see if CLVD analysis will rapidly (in second) and reliably identify areas of regular or irregular activation. An objective will be to see if this new method will help to identify areas which are continuously regular from areas which are intermittently regular. If these objectives are met, this new method could lead to a new way of accurately and rapidly analyzing AF in real time, and aid in understanding mechanism(s) and increasing the efficacy of treatment in patients. To test our hypothesis we incorporated the findings from previously analyzed data of different AF mechanisms using the classical analysis of activation sequence to the findings from the CLVD analysis of the same data. 12

17 WORKS CITED 1. J. L. Halpem and R. G. Hart, Atrial fibrillation and stroke: New ideas, persisting dilemmas, Stroke, vol. 19, pp , W. M. Abbott, R. D. Maloney, C. C. McCabe, C. E. Lee, and L. S. Wirthlin, Arterial embolism: A 44 year perspective, Amer. J. Surg., vol. 1243, pp , P. A. Wolf, T. R. Dawber, and H. E. Thomas, Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: The Framingbam study, Neurology, vol. 28, pp , Kumagai K, Khrestian C, Waldo AL. Simultaneous multisite mapping studies during induced atrial fibrillation in the sterile pericarditis model. Insights into the mechanism of its maintenance. Circulation. 1997;95: Allessie M, Lammer W, Smeets J, et al. Total mapping of atrial excitation during acetylcholine-induced atrial flutter and fibrillation in the isolated canine model. In: Kulbertus HE, Olsson SB, Schlepper M, eds. Atrial Fibrillation. Molndal, Sweden: A.B. Hassle; 1982: Wijffels MC, Kirchhof CJ, Dorland R, et al. Atrial fibrillation begets atrial fibrillation. A study in awake chronically instrumented goats. Circulation. 1995;92: Wu TJ, Ong JJ, Chang CM, et al. Pulmonary veins and ligament of marshall as sources of rapid activations in a canine model of sustained atrial fibrillation. Circulation. 2001;103: Scherf D, Romano F, Terranova R. Experimental studies on auricular flutter and auricular fibrillation. Am Heart J. 1958;36: Morillo CA, Klein GJ, Jones DL, et al. Chronic rapid atrial pacing. Structural, functional, and electrophysiological characteristics of a new model of sustained atrial fibrillation. Circulation. 1995;91:

18 10. Cox JL, Canavan TE, Schuessler RB, et al. The surgical treatment of atrial fibrillation. II. Intraoperative electrophysiologic mapping and description of the electrophysiologic basis of atrial flutter and atrial fibrillation. J Thorac Cardiovasc Surg. 1991;101: Skanes AC, Mandapati R, Berenfeld O, et al. Spatiotemporal periodicity during atrial fibrillation in the isolated sheep heart. Circulation. 1998;98: Blanc O, Virag N, Vesin JM, et al. A computer model of human atria with reasonable computation load and realistic anatomical properties. IEEE Trans Biomed Eng. 2001;48: Vigmond EJ, Ruckdeschel R, Trayanova N. Reentry in a morphologically realistic atrial model. J Cardiovasc Electrophysiol. 2001;12: Harrild D, Henriquez C. A computer model of normal conduction in the human atria. Circ Res. 2000;87:E Wells JL, Jr., Karp RB, Kouchoukos NT, et al. Characterization of atrial fibrillation in man: studies following open heart surgery. Pacing Clin Electrophysiol. 1978;1: Konings KT, Kirchhof CJ, Smeets JR, et al. High-density mapping of electrically induced atrial fibrillation in humans. Circulation. 1994;89: Moe G. On the multiple wavelet hypothesis of atrial fibrillation. Arch Int Pharmacodyn. 1962;140: Allessie M, Lammer W, Bonke F, et al. Experimental evaluation of Moe's multiple wavelet hypothesis of atrial fibrillation. In: Zipes DaJJ, ed. Cardiac Electrophysiology and Arrhythmias. Orlando, FL: Grune & Stratton; 1985: Matsuo K, Tomita Y, Sahadevan J, et al. Atrial fibrillation due to a single reentrant circuit. Circulation;submitted copy. 14

19 20. Shimizu A, Nozaki A, Rudy Y, et al. Characterization of double potentials in a functionally determined reentrant circuit. Multiplexing studies during interruption of atrial flutter in the canine pericarditis model. J Am Coll Cardiol. 1993;22: Ortiz J, Niwano S, Abe H, et al. Mapping the conversion of atrial flutter to atrial fibrillation and atrial fibrillation to atrial flutter. Insights into mechanisms. Circ Res. 1994;74: Waldo AL. Pathogenesis of atrial flutter. J Cardiovasc Electrophysiol. 1998;9:S Uno K, Kumagai K, Khrestian CM, et al. New insights regarding the atrial flutter reentrant circuit : studies in the canine sterile pericarditis model. Circulation. 1999;100: Haissaguerre M, Jais P, Shah DC, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med. 1998;339: Laurita K, Sun G, Thomas CW, et al. Interactive cardiac mapping. I. Data acquisition. In: IEEE EMBS 11th Annual International Conference: Proc IEEE Eng Med & Biol Soc; 1989: Kimber S, Downar E, Masse S, Sevaptsidis E, Chen T, Mickleborough L, Parsons I. A comparison of unipolar and bipolar electrodes during cardiac mapping studies. Pacing & Clinical Electrophysiology. 1996; 19:

20 Chapter 2. A RAPID CYCLE LENGTH VARIABILITY DETECTION TECHNIQUE OF ATRIAL ELECTROGRAMS IN ATRIAL FIBRILLATION 16

21 INTRODUCTION Atrial fibrillation (AF) is the most common, sustained arrhythmia in the western world. The occurrence of AF is associated with structured heart disease and advancing age. AF is associated with increased mortality and morbidity. The mechanism of AF is not well understood. It has been thought that AF is a random or disorganized rhythm due to multiple reentrant wavelets 1-3. However, recent studies 4-7 of AF sterile pericarditis model and chronic AF in patients have demonstrated that a rapid and stable driver of very short cycle length in the left atrium (LA) can cause fibrillatory conduction in the rest of the atria causing AF. If the area with the driver is located rapidly in patient, ablation can be performed in real time. Several techniques of AF, including the mean-squared error (MSE) technique 9, the coherence function 10, the cross-correlation techniques 11, the signal averaging techniques 12, FFT analysis 8, have shown to distinguish AF from organized rhythm such as AFL, focal AT or sinus rhythm. However, as Botteron G W 13 stated in their recent work, these methods defined organization in terms of the degree of linearity using new cross-correlation method between two electrograms, not measuring in global level. In our laboratory, cardiac mapping techniques 15 have been implemented for recording electrical potentials generated by the heart in patients and animal models. 17

22 Our cardiac mapping system consists of 406 monopolar cardiac amplifiers to record cardiac electrical potentials simultaneously from the epicardial and endocardial surfaces of the beating heart. With this mapping system recordings of AF in the canine sterile pericarditis model and in patients have been previously analyzed. The work presented in this thesis deals with data collected through the epicardial surface of the beating heart in canine model. Recently, our group 7,8 and others 1,12 have shown that Fast Fourier Transform (FFT) analysis of atrial electrograms (AEGs) during AF in the sterile pericarditis model and in patients is a valid method in the rapid characterization of the regularity in CL seen in AEGs. Although the FFT analysis of AEGs illustrates the dominant frequency, it can not show the cycle length variability with precision. Figure 1 shows an example of the limitation of FFT analysis for determining cycle length variability during sustained AF. The top panel shows AEGs with a mean CL of 180±14ms (5.37Hz of the wide dominant frequency). The bottom panel shows AEGs with a mean CL of 115±0.5ms (8.54Hz of the narrow dominant frequency). Even though both AEGs have very different standard deviations (0.5ms and 14ms), the FFT analysis can not distinguish cycle length variability using single dominant frequency. In our laboratory, the CLVD analysis was developed and used to rapidly identify area of the regular and irregular AF in both atria in the canine sterile 18

23 pericarditis model. In this section, we tested the hypothesis that the CLVD analysis using power envelop method of recorded AEGs during AF will more rapidly and reliably identify area of the regular and irregular AF than other methods (direct and morphology methods). METHODS We studied the activation sequence of 10 episodes of induced sustained AF in both atria and Bachmann s bundle after creation of sterile pericarditis in 10 adult mongrel dogs weighing 18kg to 25kg. We tested three methods for CLVD analysis of recorded AEGs; direct, morphologic, and the power envelope methods and compare them to the standard manual method. All studies were performed in accordance with guidelines specified by our Institutional Animal Care and Use Committee, the American Heart Association Policy on Research Animal Use, the United State Public Health Service Policy on Use of Laboratory Animals, and Guiding Principles in the Care and Use of Animals of the American Physiological Society. Experimental Preparation Creation of the Sterile Pericarditis Model 19

24 The canine sterile pericarditis model was created as previously described 3,4. At the time of surgery, pairs of stainless steel wire electrodes (interelectrode distance 3-5 mm) coated with Teflon except at the tip were sutured on the right atrial appendage (RAA), Bachmann s bundle (BB), and the posterior-inferior left atrium (PLA) near the proximal portion of the coronary sinus in all dogs. Also, another pair was sutured on the RV apex to be used for pacing, principally after radiofrequency His bundle ablation as part of studies in the open-chest state. At the completion of surgery, the dogs were given antibiotics and analgesics and then were allowed to recover. Studies in the Open-Chest State At two - four days after the initial surgery, the previously sutured pairs of wire electrodes were used for pacing, recording, and/or monitoring from their respective sites. The heart was exposed under general anesthesia and mechanically ventilated as previously described 3,4,14. The dogs were anesthetized with pentobarbital (20mg/kg~30mg/kg intravenously), and maintained on isoflurane 2% gas anesthesia and mechanically ventilated using a Boyle anesthesia machine to deliver 100% oxygen during the experiment. The right femoral vein was cannulated for catheter electrode insertion to perform His bundle ablation. The His bundle ablation was then performed using standard techniques to create complete AV block, followed by 20

25 ventricular pacing at a rate of bpm using a Medtronic 5375 programmable battery powered stimulator (Medtronic Inc., Minneapolis, MN). The body temperature of the dogs was kept within the normal physiological range throughout the study by using a heating pad and a warm saline drip. During multisite mapping, the pacing rate was decreased to 60 bpm to further decrease the temporal superimposition of atrial and ventricular events. Induction of AF AF was induced by rapid pacing for at least 20 consecutive beats from one of the atrial epicardial electrodes (right atrial appendage, Bachmann's bundle or posterior-inferior left atrium). Pacing was initiated at a rate of 120ms and decremented by 10ms until a rate of 75ms was reached or AF was induced. Only episodes of sustained AF lasting 5 min were recorded using both the Prucka system (Prucka Engineering, Houston, TX) and the custom designed cardiac mapping system 1,17. Simultaneous Multisite Mapping and Analysis: Electrograms and ECG Data Acquisition 21

26 An electrode array (figure 2) containing 406 unipolar electrodes arranged in 203 bipolar pairs (95 pairs for the right atrium and 96 pairs for the left atrium and 12 pairs placed separately on BB) was used for studies of the sequence of right and left atrial activation. The interelectrode distance of each bipolar electrode in the array was 1.2 mm, and the distance between the center of each bipolar electrode pair and its neighbor was 6 mm perpendicularly and 4.2 mm diagonally. AEGs from both atria along with ECG lead II were simultaneously recorded during the period of AF 3,4,14. Our cardiac mapping system can record continuously over 60 minutes. Data were recorded and processed with two cardiac mapping systems (that is, one for the right atrium and another for the left atrium, BB, and the atrial septum) designed at Case Western Reserve University (Cleveland, OH) and described previously 15. All signals were individually amplified, filtered between a bandwidth of 1 and 500 Hz, sampled at 1000 Hz, and digitized with a 12-bit resolution. The data were then transferred to PC with 16 megabytes of memory via optoisolators for offline analysis. For time alignment of the two mapping systems, a common marker channel was used through which a marker was introduced manually at deliberate intervals throughout the study. Markers were then numbered consecutively to permit temporal lining up of the data for analysis. Manual Data Analysis: Activation Sequence Analysis 22

27 Data were analyzed as previously described 3,4,14. Analysis was based on sequential time windows (about 100ms) which depend on CLs of the arrhythmia. Data in both their raw unipolar format and computer-processed bipolar format (obtained by subtracting raw unipolar data from a bipolar pair) were available to assist in the selection of activation times. For each episode, data from 12 consecutive time windows from one or more select portion of the episode was analyzed, and the activation sequences were depicted by activation sequence maps with isochronal lines. Data analysis: Cycle length variability detection (CLVD) analyses Method 1: Power Envelope Method using Root Mean Square (RMS) The CLVD analyses was performed on all bipolar AEGs (4.096 sec duration), converted from a pair of unipolar AEGs, recorded on both atria. DC offset was removed by subtracting the mean of the original data from the original data. In order to prevent the leakage effect in the first and last section of each 4 seconds of recording, a COSINE TAPERED window was applied before signal processing. All bipolar AEGs were band-passed filtered using a digital, zero-phase, third-order Butterworth filter with cutoffs of Hz. It was applied to obtain high-frequency components, and reduce respiration-induced fluctuation in the baseline and electrical noise. The absolute value of the output of the band-pass filter was a rectifier which is 23

28 the total energy of high components. To get a power envelope signal, the absolute value was then low-pass filtered using a similar third-order Butterworth filter with a 20-Hz cut-off. All signals were shifted about 20ms due to a cut-off frequency of the low-pass filter. However, shifting is not important when obtaining CLs because the CLs are not affected by the shift. This process was applied to extract a time-varying waveform proportional to the local maximum amplitude of the high-frequency components (40-250Hz) in the original AEGs. The locations of each peak were calculated using a peak detector. The peaks of the low-pass filtered signal were taken to represent the local maximum amplitudes that are related to each of the CLs. Since CLs are assumed to be longer than 80ms, if the value of the time between peak detections is less than half of 80ms, this is assumed to be an error, and therefore is ignored. The peak detector with a fixed threshold of 10% of the maximum amplitude in real time and automatically 0~50% variable threshold in off line was applied to obtain every peak. A flow chart of the signal processing procedure is shown in chart 1 with corresponding signals for each processing level on the right. The threshold line of the peak detector is shown as a red line. The CLs were calculated automatically by the subtraction of the original from the 1-digit right shift of the peak locations. This calculates the CLs of the total data set. The first and last CLs of the calculation are discarded. The remainders are the CL values. 24

29 Method 2: Direct Method Using Band-Pass Filter (BPF) This direct method was performed on all bipolar AEGs (4.096 sec duration), DC offset, and a COSINE TAPERED window. All bipolar AEGs were band-pass filtered using a digital, zero-phase, third-order Butterworth filter with low/high cutoff of 5Hz/100Hz. It was applied to remove high-frequency components (over 100Hz) and low-frequency components (below 5Hz) for using peak detector directly because of the high-frequency noise and drift in signals. The locations of each peak were calculated using a peak detector. The value of the time between peak detections was assumed to be an error if it was less than 40ms. The setting of the threshold of the peak detector was the same as the power envelope method in order. Flow chart of the signal processing procedure is shown in chart 2. Method 3: Morphology Method using Cross-Correlation (CC) This morphology method was also applied to all bipolar AEGs (4.096 sec duration) and DC offset. However, the COSINE TAPERED window was not applied because this method needs to cut the first 200ms of data to generate a template. In signal processing, CC is a measure of the similarity of two signals, and is commonly used to find features in an unknown signal by comparing it to a known one. So, the CC analysis can detect local peaks using signal morphology. This method uses the 25

30 first 200ms of signals as a template and then compares the remaining signals with the template signals using CC. If the signal morphology of the template is close to the location of the remaining signals, the output of the CC will be at a maximum peak in each of the locations. The setting of the threshold of the peak detector was the same as that used in the previous method to obtain every peak and CL. A flow chart of the signal processing procedure is shown in chart 3 with corresponding signals for each processing level on the right. The threshold line of the peak detector is shown as a red line. Statistical Analysis for Reducing Errors Since our group was only recording AEGs in the presence of atrio-ventricular dissociation caused by ablating the His bundle as described above, the ventricles were paced at a slower rate than the atrial site. However, the ventricular signal still produced errors in CL determination. Therefore, statistical analysis to reduce errors was required in the signal processing of the three methods used. A flow chart of statistical analysis is shown in chart 4. Both the mean and standard deviation (SD) values were calculated using the calculated CLs from each of the peak locations. The range of approvable CLs was selected within a range between the mean-(2*sd) and the mean+(2*sd). The CLs out of range were removed 26

31 automatically. This step was repeated infinitely until the current range was the same as the previous calculated range. The final mean and SD was calculated after the previous statistical analysis was done. This method of statistical analysis reduces errors of signal processing due to ventricular complexes as well as misdetection of local amplitude, double potential electrograms, and missed detection of low amplitude potentials. Verification of the power envelope method Unlike the direct and morphology methods, the power envelope method converts the original signal to a power signal. So, a comparison between the manual and automatic measurement of the CLs was required to verify the power envelope method. Both offline analysis using manual analysis in custom designed software using MATLAB 6.5 and the power envelope method in LabVeiw 7.1 were tested for verification. Figure 3 shows an example of the verification between the manual and automatic measurements in one of the sustained AF examples. The top of the green line is the original data, and the next three signals are enlargement of the second, third, and fourth CLs. The cursors to measure the CL are located at each of the local maximum amplitudes. The manual measurements of each respective CL (218, 214, and 177ms) are shown between areas of the CL. The shape of the signal processing is 27

32 shown in the threshold line. The values of the power envelope method are represented in an array of CLs on the bottom. The results of the power envelope method are the same as the results of the manual measurement. RESULTS After studying the 10 episodes of AF, we identified three different morphologies of signals in areas with a regular CL each of which were caused by a rapid and regular diver; positive-peak, negative-peak, and variable-peak signals. Figure 4, 5, and 6 shows representative examples of these three different morphologies along with the modified signals produced using the three CLVD signal analysis methods in this study. In figure 4 (positive-peak signals), each of the three methods of analysis found the location of the peak value, and every peak value s was correct. However, we found a problem using the morphology method depending on the morphology of the first cut data used as the template. Figure 7 shows the example of this problem. The top signal is the original signal, and the bottom signal is the result of using the morphology method. The red circle is the template data which has a very different morphology from rest of the data. As a result, the morphology method incorrectly detected each CL. 28

33 In analysis of the negative-peak signals (figure 5), the modified signal using the power envelope and morphology methods found the location of the peak values, and every peak value s was correct. Using the direct method, on the other hand, the analysis had difficulty finding the correct CL because all of the information of the signal was located in the negative area. Although we can remove the DC offset, we can not distinguish between positive-peak and negative-peak signals because the starting point value of the signal is unknown. Figure 6 shows a variable-peak signal with results from using three methods for analysis. All three analyses found the location of the peak values, and every peak value was correct. Due to the different amplitudes and morphologies, the accuracy of the direct and morphology methods is critically dependent on the threshold setting. However, the problem using the morphology method related to the first cut data still exists. Table 1 compares the number of electrode sites in the left atrium demonstrating a regular CL analyzed by each of the above methods using a fixed threshold of 10% of the maximum peak in real time with the number of sites with regular CLs using a manual method as the gold standard. The percentage of accurately identifying the regular area using the power envelop method is 85%, and the other two are 58% (direct method) and 57% (morphology method). Respectively the envelope method 29

34 identified AEGs with regular CLs (>81%), mean 85% of the time for all morphologies. However, using the direct and morphology methods, the successful percentages were >15% (mean 58%) and >31%(mean 56%). This difference on detection is explained by the fact that the morphology method depends on the morphology of the first cut data which causes problems, and the direct method can not reliably detect negative-peaks or variable-peak signals. Table 2 shows the number of electrode sites with regular CLs found in the left atrium using the same above methods and their percentage of correctly identifying the electrode sites with regular CLs when automatically checking in off-line using thresholds of 0-50% of the maximum amplitude. The percentages of finding the regular area using the three methods are 100% (power envelope method), 59% (direct method), and 71% (morphology method). The percentage of the power envelope and morphology methods finding a regular area increased comparing off-line to real time. Both analyses improved when there was time to find the optimum threshold for each site. However, the results using the direct method are almost the same because this analysis can not detect negative-peak and some of the variable-peak signals regardless of threshold changes. Comparison CLVD analysis with Classical analysis (AF due to LA reentrant circuit) 30

35 Recent studies by our group using the canine sterile pericarditis model have shown that a stable reentrant circuit with a consistent, but very short CL in the LA around the pulmonary veins generated daughter waves to the rest of the atria. However, because of the very short CL, most of the RA and parts of the LA could not follow with 1:1 conduction, resulting in fibrillatory conduction. Analysis of the activation sequence was obtained by creating classical activation sequence maps. We detected the activation times from all bipolar recording sites in consecutive 100 msec windows for up to 1.2 seconds in each sustained AF episode to identify the location and the course of wavefront propagation (example: figure 8) 4. CLVD analysis using the power envelope technique was applied to these same sustained AF episodes to rapidly identify and characterize areas with either regular or irregular activation CLs in both atria. These analyses were also used to identify reentrant pathways from the driver to the rest of the atria, especially the RA, during AF. Comparison of theses analyses was done to show the efficacy of this new method, the power envelop method, of CLVD analysis of AEGs. This analysis provided new insights by proving rapid detection of regular and irregular activation patterns, and conduction pathways. As shown in figure 9, the CLVD analysis of the same episode as figure 8 also identified an area of a rapid, stable activation in the LA with fibrillatory conduction in the RA. The red color (SD < 4ms) seen in the LA, shows this area. For SD > 10ms, no 31

36 color was assigned. The latter areas were seen in the RA, identifying an irregular and unstable activation pattern. Note that in the RA, in the region of sulcus termination, the CLVD analysis showed a region on the RA with the same (SD < 4ms) regularity found in the LA. This indicates that the activation wavefronts from the LA invaded the RA via this route s (i.e. fibrillatory conduction) but the rest of the RA could not follow 1:1, resulting in irregular activation. Figure 10 also shows the original AEGs from sites a through f on the atria with the corresponding modified signals of the CLVD analysis using the power envelop method and a single threshold line. The corresponding range of CLs, mean, and SD value of the signals using each analysis process are shown on the right. The modified signals are shown with positive amplitude, and easily identify the CL regardless of the quality and morphology of the AEG at those same sites. Figure 11 shows the results of the CLVD analysis and LA reentrant circuit (figure 8) with AEGs. A high pass filter (cutoff frequency 10 Hz, Butterworth second order) was applied to the original AEGs to remove drift. From the epicardial breakthrough at BB (site e), the reentrant circuit propagates to the left side of BB (site f), down the LA free wall (sites g-i) and back to the BB breakthrough site (site e). Parts of the LA, especially in the posterior inferior region (sites k-m), demonstrated an irregular activation with continuous beat-to-beat variation in AEG morphology and CL indicative of fibrillatory conduction. 32

37 DISCUSSION To our knowledge, this is the first study to demonstrate and compare the spatial organization of the atrial activation pattern in both atria determined by CLVD analysis with the classical analysis of the activation sequence. We showed that the CLVD analysis was able to rapidly characterize regular and irregular patterns of atrial activation, and identify more regular areas from less regular areas, as well as reentrant pathways in both atria without going through the process of generating the activation sequence maps during AF. The distribution of the SD values and color map during each AF episode corresponded with the revealed activation pattern during AF using classical activation sequence analysis. In this study, the CLVD analysis provided a quantitative measure of regularity of recorded AEGs throughout the atria. A small SD (SD < 4ms; the red color) was found in areas with a stable, regular activation with a consistent CL, and a large SD (SD > 10ms; no color) was found in areas with an unstable and irregular activation. The regular activation is identified as the area of small SD value and red color in 2D map. This suggests that the activation pattern is homogeneous throughout the region with 1:1 conduction from the activation wavefronts coming in to the region. The irregular activation is identified as the area of large SD without color in 2D map in the region. Sometimes, blue colors (5ms < SD < 10ms) were present in these regions. This suggests that the activation pattern is not 33

38 homogeneous throughout the region and the fibrillatory conduction is present. The RA is more vulnerable to fibrillatory conduction because its ERP (Effective Refectory Period) is longer and there are repolarization heterogeneities. CLINICAL APPLICATIONS CLVD analysis can be performed rapidly to characterize areas with regular and stable CLs visualized in 2D color map from large numbers of recording sites in both atria. These maps also allow rapid and reliable identification of the left to right atrial conduction pathways when a rapid stable generating source with consistent CL located in the LA was causing fibrillatory conduction in the RA. Epicardial conduction pathways indicated by the CLVD analysis were compared with the activation sequence maps and found to be consistent with the results. We expect this to aid clinically where fast detection of location of the ectopic foci and/or the conduction paths in AF may increase the efficacy of therapeutic interventions such as radiofrequency ablation to treat AF. LIMITATIONS This study did not consider the activation patterns in the endocardium since this study only investigates epicardial activation. However, placement of electrodes on the endocardial surface should identify endocardial activation patterns just as they 34

39 do for epicedial activation. The CLVD analysis is not able to distinguish the difference between a single focus firing rapidly and a stable reentrant circuit. The CLVD analysis will show red color (SD < 4ms) in areas with 1:1 activation during both mechanisms because both mechanisms have a regular activation pattern. CONCLUSION The cycle length variability detection (CLVD) analysis using the power envelope method provided to be best method of the three developed methods tested in this study. It is a powerful and useful tool for determining regular and irregular patterns of atrial activation in both atria. The power envelope algorithm of CLVD analysis can determine the CL variability of atrial activation by analyzing the SD value and displaying a color map of the SD range of AEGs, and will rapidly characterize continuously regular areas from intermittently regular areas or completely irregular areas. This color map correlated well with the findings from the classical analysis of atrial activation in the canine models of AF for determining those areas. 35

40 36

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42 Chart 1. Flow chart of signal processing procedure for the CLVD analysis using power envelope method. Corresponding signals for each process are shown on the right. 38

43 Chart 2. Flow chart of signal processing procedure for the CLVD analysis using direct method. Corresponding signals for each process are shown on the right. 39

44 Chart 3. Flow chart of signal processing procedure for the CLVD analysis using morphology method. Corresponding signals for each process are shown on the right. 40

45 Chart 4. Flow chart of mathematical processing procedure for statistical analysis to remove ventricular signal and errors. 41

46 42

47 Figure 4. Representative examples of positive-peak signal that are original and modified signals using power envelope, direct, and morphology methods with the threshold line. 43

48 Figure 5. Representative examples of nagative-peak signal that are original and modified signals using power envelope, direct, and morphology methods with the threshold line. 44

49 Figure 6. Representative examples of variable-peak signal that are original and modified signals using power envelope, direct, and morphology methods with the threshold line. 45

50 Figure 7. Two examples of the problem in the morphology method using CC. The top of signal is original signal and the bottom of signal is modified signal using CC. The red circle is the first cut data as template which has very different morphology from rest of the signal. The result of modified signal using CC is incorrect and can not detect each cycle length. 46

51 Table 1. Numbers and successful percentages of regular area found in left atria are shown in each different method with threshold (10% of the maximum peak) in real time 47

52 Table 2. Numbers and successful percentages of regular area found in left atria are shown in each different method with automatically checking all thresholds in off-line 48

53 Figure 8. Representative examples of sequence of activation maps during 12 consecutive 90-ms windows (total of 1.08 seconds of analysis) from an episode of sustained AF due to a rapid, stable, reentrant circuit around the left pulmonary veins. Orange lines with arrows identify the stable reentrant circuit. Black lines with arrows indicate activation wavefronts that are not part of a reentrant circuit causing fibrillatory conduction on the rest of atria. Dark gray regions indicate atrial areas that were not activated during a 90-ms window. The * represents the epicardial breakthrough site. Isochrones are drawn at 10 ms intervals. Dashed lines indicate lines of functional block. LAA: left atrial appendage; RAA: right atrial appendage 49

54 Figure 9. A cycle length variability analysis of an AF episode due to a rapid, stable reentrant circuit in the LA with fibrillatory conduction to the RA. The mean value was found at 97ms in the LA which corresponded to the 1:1 atrial activation pattern due to the reentrant circuit around the left pulmonary veins. No regular areas except top of IVC were found in the RA which corresponded to fibrillatory conduction caused by the rapid wavefronts from the LA. 50

55 Figure 10. Orignal AEGs from sites a through f on figure 9 with corresponding the CLVD analysis using power envelope method with threshold line. Corresponding range of CL, mean, and SD value signals for each process are shown on the right.circuits. 51

56 52

57 Figure 11. Representative examples of compare between sequence of activation map and the CLVD analysis during sustained AF. Atrial activation map and 2D color map of the CLVD analysis during stable AF (CL 97 ms). Isochrones are drawn at 10 ms intervals, solid arrows represent epicardial activation sequence, asterisk indicates epicardial breakthrough, and dark gray region indicates fibrillatory conduction area. Letters a through m indicate recording sites from which AEGs are displayed on the right. A reentrant circuit around the PVs with fibrillatory conduction was present (e-h). 53

58 WORKS CITED 1. Moe G. On the multiple wavelet hypothesis of atrial fibrillation. Arch Int Pharmacodyn. 1962;140: Allessie M, Lammer W, Bonke F, et al. Experimental evaluation of Moe's multiple wavelet hypothesis of atrial fibrillation. In: Zipes DaJJ, ed. Cardiac Electrophysiology and Arrhythmias. Orlando, FL: Grune & Stratton; 1985: Kumagai K, Khrestian C, Waldo AL. Simultaneous multisite mapping studies during induced atrial fibrillation in the sterile pericarditis model. Insights into the mechanism of its maintenance. Circulation. 1997;95: Matsuo K, Tomita Y, Sahadevan J, et al. Atrial fibrillation due to a single reentrant circuit. Circulation;submitted copy. 5. Skanes AC, Mandapati R, Berenfeld O, et al. Spatiotemporal periodicity during atrial fibrillation in the isolated sheep heart. Circulation. 1998;98: Uno K, Kumagai K, Khrestian CM, et al. New insights regarding the atrial flutter reentrant circuit : studies in the canine sterile pericarditis model. Circulation. 1999;100: Sahadevan J, Ryu K, et al. Epicardial Mapping of Chronic Atrial Fibrillation in Patients. Preliminary Observations. Circulation. 2004;110: Ryu, K., et al. Use of fast fourier transform analysis of atrial electrograms for rapid characterization of atrial activation-implications for delineating possible mechanisms of atrial tachyarrhythmias. Journal of Cardiovascular Electrophysiology, v. 17 issue 2, 2006, p Sih HJ, Sahakian AV, Arentzen CE, et al. A frequency domain analysis of spatial organization of epicardial maps. IEEE Trans Biomed Eng. 1995;42:

59 10. Ropella KM, Sahakian AV, Baerman JM, et al. The coherence spectrum. A quantitative discriminator of fibrillatory and nonfibrillatory cardiac rhythms. Circulation. 1989;80: Govrin O, Sadeh D, Akselrod S, et al. Cross-correlation technique for arrhythmia detection using PR and PP intervals. Comput Biomed Res. 1985;18: Vazquez R, Caref EB, Torres F, et al. Comparison of the new acceleration spectrum analysis with other time- and frequency-domain analyses of the signal-averaged electrocardiogram. Eur Heart J. 1998;19: Botteron G W; Smith J M A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart. IEEE transactions on bio-medical engineering (1995), 42(6), Journal code: ISSN: PubMed ID AN MEDLINE 14. Matsuo K, Uno K, Khrestian CM, et al. Conduction left-to-right and right-toleft across the crista terminalis. Am J Physiol Heart Circ Physiol. 2001;280:H Laurita K, Sun G, Thomas CW, et al. Interactive cardiac mapping. I. Data acquisition. In: IEEE EMBS 11th Annual International Conference: Proc IEEE Eng Med & Biol Soc; 1989:

60 Chapter 3. Summary/Future Direction 56

61 The goal of this thesis study was to develop a method to rapidly and reliably characterize regular and irregular patterns of atrial activation in both atria. Of the three CLVD analysis, the power envelope technique (85% on real time and 100% in off line) provided to be the best method to accomplish this goal. The direct method (58% on real time and 59% in off line) and morphology method (57% on real time and 71% in off line) produced a lesser degree of accuracy and therefore unreliable results. These inaccurate results for detection were explained by the fact that the morphology method depended on the morphology of the first 200ms data template during representative of the rest of the data. The direct method can not reliably detect negative-peaks or variable-peak signals and therefore inaccurately measured CLs. The power envelope algorithm of CLVD analysis can determine the variability of atrial activation by analyzing the SD value and color map of AEGs, and will rapidly characterize continuously regular areas from intermittently regular areas or completely irregular areas. A cardiac mapping system at CWRU with the capability of recording from over 400 channels provided the additional benefits to quantify the organization of atrial activation pattern in both atria using the CLVD analysis of power envelope. We were able to characterize the atrial activation patterns by analyzing the SD value and 2D color map of AEGs in both atria. A red color (SD < 4ms) was present where the 57

62 activation pattern was regular and stable with a consistent CL and no color (SD > 10ms) or blue color (8ms < SD < 10ms) were present where the activation pattern was irregular and unstable. The 2D color map of SD was able to detect the global activation pattern of regularity during AF on real time. When a rapid stable generating source with very short CL in the LA caused fibrillatory conduction in the RA, a red color (SD < 4ms) found in the LA and no color (SD > 10ms) and/or blue color (8ms < SD < 10ms), generally, were found in the RA. However, some sites, near the LA, on the RA contained the LA mean value and SD, suggesting the activation wavefront from the LA was traveling via those sites. These findings from this thesis work will help in clinical environment to improve the efficacy in treating patients. Additional work is needed to find the direction of propagation in the regular site area in order to distinguish the difference between a single focus firing rapidly and a stable reentrant circuit. When we identify the direction of propagation in a regular area during real time, it will not only particularly help in radiofrequency ablation studies and the post-op study on the open-heart surgery patients where the timing is critical but also contribute for understanding the mechanisms of initiation, maintenance, and termination of AF. The next step is to increase the percentage of finding regular sites in real time using the variable threshold setting with decreased calculation time of the CLVD. 58

63 When using variable threshold setting (0-50% of the maximum amplitude in 4 second data) in off line, it requires 10 seconds to calculate every threshold setting because the computer calculates 50 times compare to single calculation in real time. Solving this problem can yield a good result, about 100% percent, for finding regular sites. There are two possible way to solve this problem. First, if the computer can calculate an optimum threshold setting for each channel during a 5 minute setup time, then the computer can possible select a fixed different threshold for every channel automatically. Second way is to have the computer find the regular sites below 20ms of SD using fixed threshold (10% of the maximum amplitude), then the computer calculates a variable threshold setting for only those channels. This should significantly reduce the calculation time. 59

64 Appendix A. Software Algorithm 60

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73 Appendix B. HARDWARE DESIGN OF CARDIAC MAPPING SYSTEM 69

74 INTRODUCTION The cardiac mapping system in Case Western Reserve University (CWRU) has been used for measuring cardiac electrical activities in the epicardial and the endocardial surfaces of the beating heart or torso surface. The cardiac mapping is a widely applied technique for the study of the mechanism(s) of cardiac arrhythmias, erratic heart rhythm. The cardiac mapping technique helps physicians to treat or to prevent cardiac arrhythmias by providing a useful tool for understanding the mechanisms of abnormal cardiac electrical properties. The cardiac mapping system in CWRU is capable to simultaneously measure the cardiac electrical potential from about 400 different locations on the heart using electrode arrays consisting of silversilver chloride wires or a torso electrodes vest. This appendix includes a description of the hardware design of the cardiac mapping system in CWRU. CARDIAC MAPPING SYSTEM DATA FLOW The cardiac mapping system consists of five parts: 1) an electrode array, 2) front-end electronics (cardiac amplifiers, sample and hold circuitry, multiplexers, and analog to digital converters [ADC]), 3) an analog control unit (ACU), 4) a computer interface (PCI 32-bit Digital I/O card) and 5) storages (SCSI hard drive and RAM). 70

75 An electrode array that has 400 electrodes is used to measure cardiac electrical potentials (Fig. B.1). A recorded signal is fed into a low power/noise cardiac amplifier with a sample and hold circuit and 16 cardiac amplifiers are placed in one board with a multiplexer, and a 12-bit ADC (Fig. B.2). After the signals get amplified and digitized, the ACU controls the data BB Right Atrium Left Atrium Figure B.1. An Electrode Array flow and transferring it from 16 amplifier boards to the computer interface. Figure B.3 shows the simple diagram of the data flow from the electrode array to the computer. The ACU sequentially acquires the all digitized data words from one cardiac amplifier from each board at a time and saves it to the temporary buffers in the ACU (16 words in 16-bit) within one sampling period. Since the data that gets saved in the ACU 71