Journal
of Electrocardiology
Vol. 25 Supplement
Validation of an Adaptive Software Trigger and Arrhythmia Diagnostic Algorithm
Audrius Polikaitis, MS, and Robert Arzbaecher, PhD
Abstract: The authors have developed an algorithm for the identification of arrhythmias using intracardiac atria1 and ventricular leads. The algorithm is based on the rate of the depolarizations and a measure of the organization of electrical activity in each of the cardiac chambers. The most important requirement of the algorithm is to identify the occurrence of each cardiac event correctly. A robust amplitude-adaptive software trigger is developed, which accurately detects depolarizations in both chambers. With this reliable trigger the authors demonstrate the veracity of the arrhythmia identification algorithm. Key words: adaptive, trigger, arrhythmia, antitachycardia, dual chamber.
Automatic arrhythmia analysis is an essential feature of implantable “tiered therapy” devices. These devices must be able to distinguish among different types of arrhythmias in order to apply corrective therapy. Ventricular rate alone is an inadequate criteria to discriminate classes of arrhythmias because sinus tachycardia, atria1 fibrillation, and supraventricular tachycardia confound stand-alone ventricular rate diagnoses. Morphological descriptors’-5 are likely to demand computational and memory requirements too large to be satisfied by small, low-power implantable processors. We prefer using rate analysis in both chambers” as the basis of a tachycardia recognition algorithm. Since rate analysis demands accurate detection of each cardiac depolarization, a robust trigger is critical to the success of such a diagnostic scheme. The simplest trigger algorithm uses a fixed-ampliFrom the Pritzker Institute of Medical Engineering, of Technology, Chicago, Illinois.
tude threshold. Unfortunately, the threshold must be adjusted to account for the placement and maturation of the implanted leads. More importantly, one cannot determine a priori the ideal threshold amplitude that guarantees accurate and consistent triggering at the onset and during the course of a given arrhythmia. Therefore, accurate detection of events demands rapid adaptation of the threshold to signal amplitude changes, such as those that occur in polymorphic tachycardia, during respiratory induced modulation prevalent in atria1 flutter, or at the sudden onset of fibrillation. In our opinion, earlier attempts at adaptable triggers’ are too sensitive to occasional large amplitude depolarizations, for example, premature ventricular beats (PVBs), and to baseline noise.
Description of Trigger
Illinois Institute
Supported by NIH grants HL35554 and HL32131. Reprint requests: Audrius Polikaitis, MS, Pritzker Institute of Medical Engineering, Illinois Institute of Technology, 10 West 32nd Street, Chicago, IL 60616.
The software triggering mechanism we developed uses a threshold that quickly and automatically adapts to changes in the amplitude and slew rate of
173
174
Journal of Electrocardiology
Vol. 25 Supplement
intracardiac electrograms and simultaneousIy reacts to the noise and artifact present in this signal. Figure 1 is a schematic representation of our trigger. The incoming electrogram is low-pass filtered to smooth the signal and remove high-frequency noise. A rectified first-difference operation is applied to the signal to enhance the depolarizations and to remove baseline variations and direct current offset. The threshold is adaptively maintained at 25% of the peak of this derivative signal. For example, when a depolarization is detected whose peak derivative is less than the peak derivative of the previous event, the threshold is automatically lowered to 25% of this newly detected peak. Upward adaptation is handled in a slightly different manner in order to minimize the influence of a single large deflection (as in PVBs) on the threshold. When a depolarization is detected whose peak derivative is greater than the peak derivative of the previous event, the threshold is raised to 25% of a weighted average of this newly detected peak and the peaks of previously detected events. Weighting of the upwards adaptation prevents a single spurious large amplitude event from setting the threshold to an amplitude at which subsequent events would be undetected. After the threshold is reset, it is allowed to decrease exponentially (time constant of 3 seconds) so that triggering will ultimately be reestablished if a sudden decrease of electrogram signal amplitude of more than 75% occurs. Without this feature, a loss of triggering due to a permanent decrease in amplitude would be an unrecoverable error, because adaptation of the threshold could not continue. After the detec-
based on peak and IBA
Mark detectIon of a csrdlac evmt Threshold
7 1 DetermIne peak value ot detected event 4 120 mr. blanking period
Fig. 1. Schematic representation of the adaptive trigger. The threshold adapts to a level between the peak value of the detected events and the baseline electrical activity. If a depolarization is not detected, triggering is reestablished by exponentially decreasing the threshold. Triggering can occur during the 55 ms calculation of the interbeat activity measure (IBA) .
1SeC
Fig. 2. Example of adaptive triggering during large amplitude changes commonly seen in atria1flutter. The top trace is the bipolar atria1ECG, the bottom is the filtered, differentiated, and rectified ECG with the adapting threshold. Every cardiac event is correctly detected.
tion of an event, a 120 ms blanking period is introduced to avoid multiple triggering on a single multiphasic complex. Once the blanking period expires, the trigger is again enabled for further detection of cardiac events. To avoid triggering on noise and far-field artifacts, the trigger algorithm then computes the average of the 55 samples immediately following the blanking period (still within the usual period of repolarization) and adds this measure of interbeat activity to the threshold. This amplitude-adapted and interbeat activity adjusted threshold is used to detect the next cardiac event. Figure 2 shows the adaptation of the threshold during a typical episode of atria1 flutter.
Trigger Accuracy The trigger was developed using a training set of 44 recordings made during provocative electrophysiology studies at the University of Chicago Hospitals (Chicago, Illinois). The training set included 8 episodes of normal rhythm and 36 episodes of arrhythmias, including 1 atria1 tachycardia, 4 atria1 flutters, 8 atria1 fibrillations, 7 1: 1 tachycardias, 8 ventricular tachycardias, and 8 ventricular fibrillations. All signals were derived from 1 cm apart bipolar electrodes on catheters in the right atrium and ventricle. The recordings were digitized at 1,000 samples per second using the Dataq Instruments Inc. (Akron, OH) Codas system in a PC/AT 386. All episodes were chosen to be 10 seconds in length. A test set of 67 episodes was then chosen from a different set of recordings and processed by the trigger algorithm. The trigger performed with 100% detection accuracy in all organized rhythms, including sinus rhythm, atria1 tachycardia, atria1 flutter, 1: 1 tachycardia, and ventricular tachycardia. With
Arrhythmia
Identification
Table 1. Results of Trigger Episodes No tachycardia Atria1 tachycardia Atria1 flutter 1: 1 tachycardia Ventricular tachycardia Atria1 fibrillation, 1 Atria1 fibrillation, 2 Atria1 fibrillation, 3 Atria1 fibrillation, 4 Atrial fibrillation, 5 Atrial fibrillation. 6 Ventricular fibrillation, Ventricular fibrillation, Ventricular fibrillation, Ventricular fibrillation,
23 4 8 12 9 1
1 1 1 1
1 1 2 3 4
1 1 1 1
Correct Beat Count
Missed Beats
297 143 381 333 295 58 62 56 54 50 53 58 60 54 63
0 0 0 0 0 0 0 0 0 0 0 0 1 I 0
Polikaitis and Arzbaecher
l
175
Accuracy Overcounted Beats
% Error
0 0 0 0 0 2 2 1 1 2 0 0 0 0 9
0 0 0 0 0 + 3.4 + 3.2 + 1.8 + 1.9 + 4.0 0 0 - 1.7 - 1.9 + 14.3
Computer
Diagnosis
No tachycardia Atria1 tachycardia Atria1 flutter I : 1 tachycardia Ventricular tachycardia Atria1 fibrillation Atria1 fibrillation Atria1 fibrillation Atrial flutter Atria1 flutter Atrial flutter Ventricular fibrillation Ventricular fibrillation Ventricular fibrillation Ventricular iibrillation
Triggering of organized rhythms is performed without error, while fibrillatory rhythms produce trigger error. A diagnosis is achieved using rate and IBA criteria. Three episodes of atrial fibrillation are misdiagnosed as atrial flutter.
slightly disorganized rhythms (6 atria1 fibrillations, 3 ventricular fibrillations), accuracy varied between 96% and lOO%, while an episode of highly disorganized ventricular fibrillation resulted in an accuracy of 86.7%. The correct event count was determined by an experienced reader who was blinded to the computer results. The first five columns of Table 1 show the total number of beats in all episodes of organized rhythms, the individual number of beats in each episode of disorganized rhythms, and the number of beats the trigger either missed or overcounted.
Arrhythmia Diagnosis The results of trigger accuracy shown in Table 1 are not surprising: organized rhythms exhibit welldefined and easily countable depolarizations due to the unified activation of the heart, while disorganized rhythms are a product of individual heart muscle fibers depolarizing in a more random fashion. Obtaining consistent counts of cardiac events in disorganized rhythms is a difficult task for both human cardiologists and automated systems. Therefore, measures of rate alone have not been effective in identifying disorganized rhythms. On the other hand, the interbeat activity measure (IBA) that we developed to keep the triggering threshold well above the baseline artifact can also serve to distinguish between atria1 and ventricular fibrillation and organized rhythms. For organized rhythms the activity between events consists only of baseline noise and artifacts, and yields consistently low IBA values. But atria1 and ventricular fibrillation are not character-
ized by well-separated discrete events, and both yield unusually large IBA values. Furthermore, these IBA values are characterized by very large changes over a IO-second period. Thus, IBA mean value and deviation from the mean, both normalized to the amplitude of the ECG, are valid criteria to distinguish between organized rhythms and fibrillation. We have chosen the boundary between fibrillatory and nonfibrillatory rhythms to be the straight line: IBA mean + IBA deviation
= 0.1
Figure 3 outlines our diagnostic logic. The algorithm first checks the IBA statistics of both chambers. If the above criterion is exceeded, a diagnosis of fibrillation in that chamber is announced. If not, the rate of both chambers is calculated via the timing of atria1
A
or V statl8tlca fibrlllaflon
lndlcate 7
y
N NO TACH N+ I:1 TACH
v rate 133433
BPM 7
VFIB
+
VTACH
ATACH
AFLUT
Fig. 3. Schematic
representation of the arrhythmia diagnosis algorithm. The algorithm checks IBA statistics for the presence of fibrillation. If the rhythm is organized, rate criteria are applied to reach a diagnostic conclusion.
176
Journal of Electrocardiology
Vol. 25 Supplement
Discussion
1 set -
I
I
“’
’
I I
Fig. 4. Example as in Figure 2,
but showing a disorganized rhythm (atria1 fibrillation). Rate of atria1 arrhythmia (3 16 beats/min) does not meet the rate criterion for atria1 fibrillation. The disorganized nature of the atria1 arrhythmia produces IBA statistics, which satisfy the criteria for atria1 fibrillation.
and ventricular depolarizations as determined by the trigger algorithm. If the rate in both chambers is less than 13 3 beats/min a diagnosis of no tachycardia is announced. If the rate in either chamber exceeds this rate boundary, the 1: 1 nature of the tachycardia is explored. If the atria1 and ventricular rates are not within 5%, the chamber with the faster rate is investigated. Using commonly accepted rate decision boundaries (atria1 tachycardia, 13 3-250 beats/min; atria1 flutter, 250-333 beats/min; ventricular tachycardia, 13 3-3 3 3 beats/min; atria1 and ventricular iibrillation, >33 3 beats/min) an arrhythmia diagnosis is determined. Figure 4 is an example of a disorganized rhythm (atria1 fibrillation) for which the adaptive trigger yields a rate (3 16 beats/min) too slow to satisfy the required rate criterion for atria1 fibrillation. However, the disorganization present in this arrhythmia yields IBA statistics (mean, 0.084; absolute deviation, 0.056) that satisfy the stated IBA criterion for atria1 fibrillation.
The depolarizations in the three misdiagnosed episodes of atria1 fibrillation exhibited just enough organization and occurred at a rate just below 333 beats/ min, so as to fail the IBA and rate criteria established for atria1 fibrillation. These results suggest that some modification of the parameters may be necessary. More sensitive IBA parameters would have resulted in the correct diagnoses for these three episodes; however not without risk of misdiagnosing some organized arrhythmias as fibrillation. Regularity of the rate of depolarizations may be a solution to the problem of distinguishing atria1 fibrillation from flutter. It is widely accepted that atria1 flutter is characterized by extremely regular depolarizations, whereas the occurrence of depolarizations in atria1 fibrillation is more random in nature. The arrhythmia diagnosis algorithm has several limitations. Its performance is dependent upon the quality of the electrograms. If far-field artifacts larger than approximately 25% of the amplitude of the depolarizations are present, the trigger will overcount, leading to an improper determination of rate, and resulting in a misdiagnosis of the rhythm. However, because implantable devices use fixed leads, the presence of far-field artifacts is not expected to be as large a source of misdiagnoses as they would be with the temporary leads used in this study. Another limitation of this diagnostic algorithm is its inability to distinguish among different 1: 1 tachycardias. Incorporating the atria1 extrastimulus technique’ into this algorithm would provide the ability to discriminate sinus tachycardia from other 1: 1 tachycardias. Finally, the arrhythmia diagnosis algorithm should be subjected to a greatly expanded test set to determine what further improvements are necessary to increase its sensitivity and specificity to the various types of arrhythmias.
Diagnostic Accuracy References The last column of Table 1 summarizes the performance of the diagnostic algorithm. For organized rhythms the diagnoses were flaivless. The exceptional performance of the trigger on these episodes allowed the rate criteria to separate these organized arrhythmias perfectly. Ventricular fibrillation was diagnosed without error, though some trigger failures did occur; these are discussed below. However the algorithm misdiagnosed three episodes of atria1 fibrillation as atria1 flutter.
Davies DW, Wainwright R, Tooley M et al: Detection of pathological tachycardia by analysis of electrogram morphology. PACE 9:200, 1988 Tomaselli G, Nielsen A, Finke W et al: Morphologic differences of the endocardial electrogram in beats of sinus and ventricular origin. PACE 11:254, 1988 Lin D, DiCarlo L, Jenkins J: Identification of ventricular tachycardia using intracavity ventricular electrograms: analysis of time and frequency domain patterns. PACE 11:1592. 1988
Arrhythmia
4. Ropella K, Baerman J, Sahakian A, Swiryn S: Differentiation of ventricular tachyarrhythmias. Circulation 82: 2035, 1990 5. Throne R, Jenkins J: A comparison of four new timedomain techniques for discriminating monomorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology. IEEE Trans Biomed Eng 38:561, 1991 6. Arzbaecher R, Bump T, Jenkins J et al: Automatic tachycardia recognition. PACE 7: 54 1, 1984
Identification
l
Polikaitis
and Arzbaecher
177
7. MacDonald R, Jenkins J, Arzbaecher R, Throne R: A software trigger for intracardiac waveform detection with automatic threshold adjustment. p. 167. Proceedings from Computers in Cardiology. IEEE Computer Society Press, IEEE Catalog #89CH2932-2, Los Alamitos, CA, 1989 8. Munkenbeck F, Bump T, Arzbaecher R: Differentiation of sinus tachycardia form paroxysmal 1: 1 tachycardias using single late diastolic atria1 extrastimuli. PACE 9: 53, 1986
of Electrocardiology
Vol. 25 Supplement
Validation of an Adaptive Software Trigger and Arrhythmia Diagnostic Algorithm
Audrius Polikaitis, MS, and Robert Arzbaecher, PhD
Abstract: The authors have developed an algorithm for the identification of arrhythmias using intracardiac atria1 and ventricular leads. The algorithm is based on the rate of the depolarizations and a measure of the organization of electrical activity in each of the cardiac chambers. The most important requirement of the algorithm is to identify the occurrence of each cardiac event correctly. A robust amplitude-adaptive software trigger is developed, which accurately detects depolarizations in both chambers. With this reliable trigger the authors demonstrate the veracity of the arrhythmia identification algorithm. Key words: adaptive, trigger, arrhythmia, antitachycardia, dual chamber.
Automatic arrhythmia analysis is an essential feature of implantable “tiered therapy” devices. These devices must be able to distinguish among different types of arrhythmias in order to apply corrective therapy. Ventricular rate alone is an inadequate criteria to discriminate classes of arrhythmias because sinus tachycardia, atria1 fibrillation, and supraventricular tachycardia confound stand-alone ventricular rate diagnoses. Morphological descriptors’-5 are likely to demand computational and memory requirements too large to be satisfied by small, low-power implantable processors. We prefer using rate analysis in both chambers” as the basis of a tachycardia recognition algorithm. Since rate analysis demands accurate detection of each cardiac depolarization, a robust trigger is critical to the success of such a diagnostic scheme. The simplest trigger algorithm uses a fixed-ampliFrom the Pritzker Institute of Medical Engineering, of Technology, Chicago, Illinois.
tude threshold. Unfortunately, the threshold must be adjusted to account for the placement and maturation of the implanted leads. More importantly, one cannot determine a priori the ideal threshold amplitude that guarantees accurate and consistent triggering at the onset and during the course of a given arrhythmia. Therefore, accurate detection of events demands rapid adaptation of the threshold to signal amplitude changes, such as those that occur in polymorphic tachycardia, during respiratory induced modulation prevalent in atria1 flutter, or at the sudden onset of fibrillation. In our opinion, earlier attempts at adaptable triggers’ are too sensitive to occasional large amplitude depolarizations, for example, premature ventricular beats (PVBs), and to baseline noise.
Description of Trigger
Illinois Institute
Supported by NIH grants HL35554 and HL32131. Reprint requests: Audrius Polikaitis, MS, Pritzker Institute of Medical Engineering, Illinois Institute of Technology, 10 West 32nd Street, Chicago, IL 60616.
The software triggering mechanism we developed uses a threshold that quickly and automatically adapts to changes in the amplitude and slew rate of
173
174
Journal of Electrocardiology
Vol. 25 Supplement
intracardiac electrograms and simultaneousIy reacts to the noise and artifact present in this signal. Figure 1 is a schematic representation of our trigger. The incoming electrogram is low-pass filtered to smooth the signal and remove high-frequency noise. A rectified first-difference operation is applied to the signal to enhance the depolarizations and to remove baseline variations and direct current offset. The threshold is adaptively maintained at 25% of the peak of this derivative signal. For example, when a depolarization is detected whose peak derivative is less than the peak derivative of the previous event, the threshold is automatically lowered to 25% of this newly detected peak. Upward adaptation is handled in a slightly different manner in order to minimize the influence of a single large deflection (as in PVBs) on the threshold. When a depolarization is detected whose peak derivative is greater than the peak derivative of the previous event, the threshold is raised to 25% of a weighted average of this newly detected peak and the peaks of previously detected events. Weighting of the upwards adaptation prevents a single spurious large amplitude event from setting the threshold to an amplitude at which subsequent events would be undetected. After the threshold is reset, it is allowed to decrease exponentially (time constant of 3 seconds) so that triggering will ultimately be reestablished if a sudden decrease of electrogram signal amplitude of more than 75% occurs. Without this feature, a loss of triggering due to a permanent decrease in amplitude would be an unrecoverable error, because adaptation of the threshold could not continue. After the detec-
based on peak and IBA
Mark detectIon of a csrdlac evmt Threshold
7 1 DetermIne peak value ot detected event 4 120 mr. blanking period
Fig. 1. Schematic representation of the adaptive trigger. The threshold adapts to a level between the peak value of the detected events and the baseline electrical activity. If a depolarization is not detected, triggering is reestablished by exponentially decreasing the threshold. Triggering can occur during the 55 ms calculation of the interbeat activity measure (IBA) .
1SeC
Fig. 2. Example of adaptive triggering during large amplitude changes commonly seen in atria1flutter. The top trace is the bipolar atria1ECG, the bottom is the filtered, differentiated, and rectified ECG with the adapting threshold. Every cardiac event is correctly detected.
tion of an event, a 120 ms blanking period is introduced to avoid multiple triggering on a single multiphasic complex. Once the blanking period expires, the trigger is again enabled for further detection of cardiac events. To avoid triggering on noise and far-field artifacts, the trigger algorithm then computes the average of the 55 samples immediately following the blanking period (still within the usual period of repolarization) and adds this measure of interbeat activity to the threshold. This amplitude-adapted and interbeat activity adjusted threshold is used to detect the next cardiac event. Figure 2 shows the adaptation of the threshold during a typical episode of atria1 flutter.
Trigger Accuracy The trigger was developed using a training set of 44 recordings made during provocative electrophysiology studies at the University of Chicago Hospitals (Chicago, Illinois). The training set included 8 episodes of normal rhythm and 36 episodes of arrhythmias, including 1 atria1 tachycardia, 4 atria1 flutters, 8 atria1 fibrillations, 7 1: 1 tachycardias, 8 ventricular tachycardias, and 8 ventricular fibrillations. All signals were derived from 1 cm apart bipolar electrodes on catheters in the right atrium and ventricle. The recordings were digitized at 1,000 samples per second using the Dataq Instruments Inc. (Akron, OH) Codas system in a PC/AT 386. All episodes were chosen to be 10 seconds in length. A test set of 67 episodes was then chosen from a different set of recordings and processed by the trigger algorithm. The trigger performed with 100% detection accuracy in all organized rhythms, including sinus rhythm, atria1 tachycardia, atria1 flutter, 1: 1 tachycardia, and ventricular tachycardia. With
Arrhythmia
Identification
Table 1. Results of Trigger Episodes No tachycardia Atria1 tachycardia Atria1 flutter 1: 1 tachycardia Ventricular tachycardia Atria1 fibrillation, 1 Atria1 fibrillation, 2 Atria1 fibrillation, 3 Atria1 fibrillation, 4 Atrial fibrillation, 5 Atrial fibrillation. 6 Ventricular fibrillation, Ventricular fibrillation, Ventricular fibrillation, Ventricular fibrillation,
23 4 8 12 9 1
1 1 1 1
1 1 2 3 4
1 1 1 1
Correct Beat Count
Missed Beats
297 143 381 333 295 58 62 56 54 50 53 58 60 54 63
0 0 0 0 0 0 0 0 0 0 0 0 1 I 0
Polikaitis and Arzbaecher
l
175
Accuracy Overcounted Beats
% Error
0 0 0 0 0 2 2 1 1 2 0 0 0 0 9
0 0 0 0 0 + 3.4 + 3.2 + 1.8 + 1.9 + 4.0 0 0 - 1.7 - 1.9 + 14.3
Computer
Diagnosis
No tachycardia Atria1 tachycardia Atria1 flutter I : 1 tachycardia Ventricular tachycardia Atria1 fibrillation Atria1 fibrillation Atria1 fibrillation Atrial flutter Atria1 flutter Atrial flutter Ventricular fibrillation Ventricular fibrillation Ventricular fibrillation Ventricular iibrillation
Triggering of organized rhythms is performed without error, while fibrillatory rhythms produce trigger error. A diagnosis is achieved using rate and IBA criteria. Three episodes of atrial fibrillation are misdiagnosed as atrial flutter.
slightly disorganized rhythms (6 atria1 fibrillations, 3 ventricular fibrillations), accuracy varied between 96% and lOO%, while an episode of highly disorganized ventricular fibrillation resulted in an accuracy of 86.7%. The correct event count was determined by an experienced reader who was blinded to the computer results. The first five columns of Table 1 show the total number of beats in all episodes of organized rhythms, the individual number of beats in each episode of disorganized rhythms, and the number of beats the trigger either missed or overcounted.
Arrhythmia Diagnosis The results of trigger accuracy shown in Table 1 are not surprising: organized rhythms exhibit welldefined and easily countable depolarizations due to the unified activation of the heart, while disorganized rhythms are a product of individual heart muscle fibers depolarizing in a more random fashion. Obtaining consistent counts of cardiac events in disorganized rhythms is a difficult task for both human cardiologists and automated systems. Therefore, measures of rate alone have not been effective in identifying disorganized rhythms. On the other hand, the interbeat activity measure (IBA) that we developed to keep the triggering threshold well above the baseline artifact can also serve to distinguish between atria1 and ventricular fibrillation and organized rhythms. For organized rhythms the activity between events consists only of baseline noise and artifacts, and yields consistently low IBA values. But atria1 and ventricular fibrillation are not character-
ized by well-separated discrete events, and both yield unusually large IBA values. Furthermore, these IBA values are characterized by very large changes over a IO-second period. Thus, IBA mean value and deviation from the mean, both normalized to the amplitude of the ECG, are valid criteria to distinguish between organized rhythms and fibrillation. We have chosen the boundary between fibrillatory and nonfibrillatory rhythms to be the straight line: IBA mean + IBA deviation
= 0.1
Figure 3 outlines our diagnostic logic. The algorithm first checks the IBA statistics of both chambers. If the above criterion is exceeded, a diagnosis of fibrillation in that chamber is announced. If not, the rate of both chambers is calculated via the timing of atria1
A
or V statl8tlca fibrlllaflon
lndlcate 7
y
N NO TACH N+ I:1 TACH
v rate 133433
BPM 7
VFIB
+
VTACH
ATACH
AFLUT
Fig. 3. Schematic
representation of the arrhythmia diagnosis algorithm. The algorithm checks IBA statistics for the presence of fibrillation. If the rhythm is organized, rate criteria are applied to reach a diagnostic conclusion.
176
Journal of Electrocardiology
Vol. 25 Supplement
Discussion
1 set -
I
I
“’
’
I I
Fig. 4. Example as in Figure 2,
but showing a disorganized rhythm (atria1 fibrillation). Rate of atria1 arrhythmia (3 16 beats/min) does not meet the rate criterion for atria1 fibrillation. The disorganized nature of the atria1 arrhythmia produces IBA statistics, which satisfy the criteria for atria1 fibrillation.
and ventricular depolarizations as determined by the trigger algorithm. If the rate in both chambers is less than 13 3 beats/min a diagnosis of no tachycardia is announced. If the rate in either chamber exceeds this rate boundary, the 1: 1 nature of the tachycardia is explored. If the atria1 and ventricular rates are not within 5%, the chamber with the faster rate is investigated. Using commonly accepted rate decision boundaries (atria1 tachycardia, 13 3-250 beats/min; atria1 flutter, 250-333 beats/min; ventricular tachycardia, 13 3-3 3 3 beats/min; atria1 and ventricular iibrillation, >33 3 beats/min) an arrhythmia diagnosis is determined. Figure 4 is an example of a disorganized rhythm (atria1 fibrillation) for which the adaptive trigger yields a rate (3 16 beats/min) too slow to satisfy the required rate criterion for atria1 fibrillation. However, the disorganization present in this arrhythmia yields IBA statistics (mean, 0.084; absolute deviation, 0.056) that satisfy the stated IBA criterion for atria1 fibrillation.
The depolarizations in the three misdiagnosed episodes of atria1 fibrillation exhibited just enough organization and occurred at a rate just below 333 beats/ min, so as to fail the IBA and rate criteria established for atria1 fibrillation. These results suggest that some modification of the parameters may be necessary. More sensitive IBA parameters would have resulted in the correct diagnoses for these three episodes; however not without risk of misdiagnosing some organized arrhythmias as fibrillation. Regularity of the rate of depolarizations may be a solution to the problem of distinguishing atria1 fibrillation from flutter. It is widely accepted that atria1 flutter is characterized by extremely regular depolarizations, whereas the occurrence of depolarizations in atria1 fibrillation is more random in nature. The arrhythmia diagnosis algorithm has several limitations. Its performance is dependent upon the quality of the electrograms. If far-field artifacts larger than approximately 25% of the amplitude of the depolarizations are present, the trigger will overcount, leading to an improper determination of rate, and resulting in a misdiagnosis of the rhythm. However, because implantable devices use fixed leads, the presence of far-field artifacts is not expected to be as large a source of misdiagnoses as they would be with the temporary leads used in this study. Another limitation of this diagnostic algorithm is its inability to distinguish among different 1: 1 tachycardias. Incorporating the atria1 extrastimulus technique’ into this algorithm would provide the ability to discriminate sinus tachycardia from other 1: 1 tachycardias. Finally, the arrhythmia diagnosis algorithm should be subjected to a greatly expanded test set to determine what further improvements are necessary to increase its sensitivity and specificity to the various types of arrhythmias.
Diagnostic Accuracy References The last column of Table 1 summarizes the performance of the diagnostic algorithm. For organized rhythms the diagnoses were flaivless. The exceptional performance of the trigger on these episodes allowed the rate criteria to separate these organized arrhythmias perfectly. Ventricular fibrillation was diagnosed without error, though some trigger failures did occur; these are discussed below. However the algorithm misdiagnosed three episodes of atria1 fibrillation as atria1 flutter.
Davies DW, Wainwright R, Tooley M et al: Detection of pathological tachycardia by analysis of electrogram morphology. PACE 9:200, 1988 Tomaselli G, Nielsen A, Finke W et al: Morphologic differences of the endocardial electrogram in beats of sinus and ventricular origin. PACE 11:254, 1988 Lin D, DiCarlo L, Jenkins J: Identification of ventricular tachycardia using intracavity ventricular electrograms: analysis of time and frequency domain patterns. PACE 11:1592. 1988
Arrhythmia
4. Ropella K, Baerman J, Sahakian A, Swiryn S: Differentiation of ventricular tachyarrhythmias. Circulation 82: 2035, 1990 5. Throne R, Jenkins J: A comparison of four new timedomain techniques for discriminating monomorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology. IEEE Trans Biomed Eng 38:561, 1991 6. Arzbaecher R, Bump T, Jenkins J et al: Automatic tachycardia recognition. PACE 7: 54 1, 1984
Identification
l
Polikaitis
and Arzbaecher
177
7. MacDonald R, Jenkins J, Arzbaecher R, Throne R: A software trigger for intracardiac waveform detection with automatic threshold adjustment. p. 167. Proceedings from Computers in Cardiology. IEEE Computer Society Press, IEEE Catalog #89CH2932-2, Los Alamitos, CA, 1989 8. Munkenbeck F, Bump T, Arzbaecher R: Differentiation of sinus tachycardia form paroxysmal 1: 1 tachycardias using single late diastolic atria1 extrastimuli. PACE 9: 53, 1986
