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Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation
S Shkurovich1, A V Sahakian, S Swiryn
1Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208-3107, USA.
Insights
Detecting atrial fibrillation (AFib) using implantable cardioverter-defibrillator (ICD) high-voltage leads is possible. While promising, the algorithm
Area of Science:
- Biomedical Engineering
- Cardiac Electrophysiology
- Medical Device Technology
Background:
- Implantable cardioverter-defibrillators (ICDs) struggle to differentiate tachycardias due to limited atrial activity detection.
- This limitation can lead to inappropriate therapies delivered by ICDs.
Purpose of the Study:
- To detect atrial activity using high-voltage (HV) lead signals during ICD implantation.
- To develop an algorithm for atrial fibrillation (AFib) detection from these HV lead signals.
Main Methods:
- Utilized a signal processing method to cancel ventricular activity and correlate atrial activity from HV lead signals.
- Analyzed frequency and amplitude distribution to distinguish between sinus rhythm (SR) and AFib.
Main Results:
- The algorithm achieved 78% sensitivity and 92.65% specificity for AFib detection in analyzed segments.
- Positive and negative predictive values were 79.59% and 91.97%, respectively.
- Atrial activity was confirmed in HV lead signals, enabling AFib detection in a significant portion of cases.
Conclusions:
- Atrial activity is detectable in ICD HV lead signals, offering potential for AFib detection.
- The developed algorithm shows promise but requires further refinement for clinical application due to insufficient specificity.
Abstract:
The inability to detect atrial activity limits implantable ventricular cardioverter defibrillators (ICD) in discriminating tachycardias and can result in inappropriate therapy. This study attempted to detect atrial activity on the wide-spaced bipole signals formed by the high-voltage (HV) leads of the ICD during device implantation and to develop an algorithm for the detection of atrial fibrillation (AFib) from these signals. We used a method that canceled ventricular and correlated atrial activity from the HV lead signals and measured frequency and amplitude distribution information to discriminate sinus rhythm (SR) and AFib segments. We analyzed 186 data segments from 21 patients (six AFib, 14 SR, one AFib and SR). For individual segments in this data set, the sensitivity of the algorithm was 78%, specificity 92.65%, positive and negative predictive values 79.59 and 91.97%, respectively. These results demonstrate that atrial activity is present in the HV lead signals, and AFib detection can be achieved in many, but not all cases, using information currently available to ICD's. Prior work from surface electrocardiograms suggests that this algorithm can function during ventricular tachycardias. However, specificity of the algorithm is not high enough for clinical use.