Continuous analysis of atrial Electrograms in DDD pacemakers and bipolar atrial leads using an automated signal Processing System

F. Eberhardt¹, U.G. Hofmann², M. Lipphardt, U.K.H. Wiegand^1
1University Hospital Luebeck, Medizinische Klinik II (Cardiology),
²Medical University of Luebeck, Institute for Signal Processing,
Ratzeburger Allee 160, 23538 Lübeck, GERMANY

Abstract: Bipolar ventricular far field oversensing is a common problem with high atrial sensitivity settings warranted for tachyarrhythmia detection and mode switching. Atrial signals change with alterations of posture and during exercise. Furthermore atrial signal morphology is dependent on the right atrial lead position, electrode surface and tip-to-ring distance of the atrial bipole. We describe a method to continuously measure unfiltered atrial signals at rest and during exercise and analyse these signals in terms of absolute signal amplitude, signal width and far-field-QRS-amplitude. Furthermore p-wave templates are created. Signals were automatically identified using a Matlab routine and classified as either p-wave (near-field) or QRS (far-field) signal. The feasibility of our approach was investigated in eleven patients with different tip-to-ring spacing of the atrial bipole at rest and during exercise. Further investigations to quantify the influence of these factors at rest and during exercise are still pending.

INTRODUCTION

Sophisticated monitoring of atrial activity is a prerequisite for modern pacemaker therapy which includes mode switching and atrial tachyarrhythmia detection. The atrial electrogram (EGM) recorded from pacemaker electrodes reflects various signals: the atrial depolarization itself, ventricular far-field signals, myopotentials and electromagnetic interference. The atrial sensing performance of bipolar electrodes is dependent on lead-design, i.e the electrode surface, electrode material, interelectrode distance of the atrial bipole and lead position [1]. A decrease in signal amplitude has been described in unipolar and bipolar electrodes during exercise [2,3]. Although registration of low amplitude atrial signals is reliably achieved by bipolar atrial electrodes, oversensing of ventricular far-fields remains a problem [1]. Studies published so far are limited by two aspects. Most studies have analyzed continuous EGM traces manually with a limited temporal resolution. Secondly, analysis is usually performed after the signals have been processed by the atrial sensing circuit. Thus, we intended to develop a simple computer-based routine using the Matlab software to quantitatively analyze unfiltered atrial pacemaker signals at rest.

METHODS

Eleven patients with a standard DDD-Pacemaker indication and chronotropic competence of the sinus node received a DR 353 device (Medtronic Inc, Minneapolis, U.S.A) and either The Medtronic 5068 lead, an active fixation lead with a tip-to-ring spacing of 17.8 mm or the Medtronic 6940 lead, an active-fixation lead with 9,0 mm tip-to ring spacing. The atrial EGMs were then recorded at a three-month follow-up. Pacemakers were programmed to either DDI or DDD 30/min to allow for continuous atrial sensing. Atrial sensitivity was programmed at 0.5 mV and the AV-interval as wide as possible within reasonable limits (maximum 300ms) to allow for spontaneous AV-conduction. The programming head of the Medtronic 9370 Programmer was applied to the patient and the atrial EGM recorded at rest in a supine position, at rest standing, during treadmill-exercise using the CAEP-protocol and post-exercise. The sampling rate was 256 Hz with an 8 bit resolution. Digital unfiltered atrial signals were delivered in a 0.5 to 256 Hz frequency band (Fig. 1a) to the programmer (Fig 1b). Analogue signals were then sent via a 1V output to a National Instruments data acquisition board and sampled using the Lab View software (Fig 1c) (National Instrument Corp., Austin, TX,  U.S.A.).

Figure 1. Unfiltered atrial signals were acquired using the setup above (see text)

Approximately 100000 signal points were analyzed at each stage (supine, standing, different stages of exercise and post-exerecise). Signal analysis was performed using the Matlab software (The Mathworks Inc., Natick, MA, U.S.A.) according to the following routine: Briefly, only acquired data points which were above the noise level were analyzed. This resulted in different populations with defined temporal distance (near-field to far-field, far-field to the following near-field, near-field to the following near-field). Due to the defined distance each individual data point could be attributed as belonging to either a near-field (p-wave) or far-field (QRS-complex) signal.

Figure 2. Signals above noise level attributed to the near-field or far-field signal according to their defined temporal distance.  

Atrial signals were characterized according to near-field and far-field amplitude. and the ratio of near-field to far-field signal. Furhermore, a template for near-field signals could be defined.

Figure 3. Amplitude of atrial near-field in mV during maximal exercise.

Figure 4. Amplitude of the near-field (p-wave) / amplitude of the far-field (QRS-complex)

RESULTS

The signal processing routine described above resulting in a satisfying discrimination and characterization of atrial near-field and far-field signals. Examples of one patient are represented in the following figures. Signals could be characterized according to the amplitude of the near-field (Fig. 3), amplitude and the ratio of near-field to far-field (Fig. 4). An atrial near-field template at rest and during exercise is shown in Fig. 5.

DISCUSSION

In the current study, we present a simple and reliable way to analyze unfiltered atrial signals in Medtronic-DDD pacemakers with bipolar sensing in a Matlab-based system.            

 Figure 5. Template of the atrial near-field at rest (left) and during exercise (right)

Previous studies have manually analyzed signals processed by the atrial sensing circuit, which limits the findings in certain aspects as signals are generally filtered and atrial sensing of paced QRS-complexes is influenced by the postventricular atrial blanking period [1,3]. However, through filtering important information may be missed as it has been described that the frequency content of p-waves changes during exercise [2]. Our system permits automatic analysis of signals without investigator bias and through template creation an analysis of signal morphology at different stages of activity (Fig. 5). The mechanism for the altered signal morphology remains during exercise speculative [2]. A possible limitation of our study is that individual signal points could be attributed as belonging to the wrong signal population, for example if atrial repolarization increases the width of the near-field signal in a limited number of patients. However, through template analysis, it is possible to attribute data points as belonging to the near-field or far-field in a post-hoc analysis, respectively. Due to the limited number of patients included in the study and the heterogeneous character of the patients with regard to lead position (lateral versus atrial appendage) and tip-to-ring spacing, statistical analysis of the data was not performed at this point. However, studies investigating a larger number of patients are currently under way. In conclusion, we present a reliable and easily reproducible way to analyze unfiltered atrial EGMs in Medtronic DDD-pacemakers.

Acknowledgments: Work supported by Medtronic GmbH, Duesseldorf, Germany

REFERENCES

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[2]  G. Froehlig, H. Schwerdt,  H. Schieffer et al.. “Atrial signal variations and pacemaker malsensing during exercise: a study in the time and frequency domain” J Am Coll Cardiol, vol. 11, pp. 806-813, 1988

[3] C.C. Chan, C.P. Lau, S.K. Leung, et al.. “Comparative evaluation of bipolar atrial electrogram amplitude during everyday activities: atrial active fixation versus two types of single pass VDD/R leads” PACE, vol. 17, pp. 1873-1877, 1994