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A low-complexity intracardiac electrogram compression algorithm
1Department of Electrical Engineering, University of Sydney, NSW, Australia. richardc@sedal.usyd.edu.au
Insights
This study presents a novel data-compression algorithm for implantable cardioverter defibrillators (ICDs). The algorithm optimizes signal recording for low power and high reliability, adapting to patient variations for effective arrhythmia detection.
Area of Science:
- Biomedical Engineering
- Cardiovascular Technology
- Signal Processing
Background:
- Implantable cardioverter defibrillators (ICDs) manage life-threatening heart arrhythmias like bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF).
- Design constraints for ICDs include power consumption, reliability, and size, impacting signal recording capabilities.
- Existing solutions often rely on patient-specific data or lack adaptability.
Purpose of the Study:
- To develop and evaluate a data-compression algorithm for ICDs that enhances signal recording capabilities.
- To optimize the algorithm for low power consumption and high reliability.
- To create a patient-independent compression method adaptable to intracardiac electrogram (ICEG) variations.
Main Methods:
- An adaptive scalar quantization algorithm was developed, adjusting to ICEG amplitude and phase variations.
- The algorithm was tested against other patient-independent compression methods using VT arrhythmia data from 146 patients.
- Performance was evaluated based on data compression rates and root mean square distortion at a 250-Hz sample rate.
Main Results:
- The proposed algorithm achieved an average of 3.5 bits/sample at 5% root mean square distortion, closely approaching the Shannon lower bound.
- It demonstrated effective compression without relying on patient-specific morphology.
- At higher distortion levels, alternative methods like vector quantization and Karhunen-Loeve Transform showed superiority but with increased computational complexity.
Conclusions:
- The developed data-compression algorithm offers an efficient solution for enhancing ICD signal recording capabilities.
- Its adaptive nature and low power consumption make it suitable for implantable devices.
- The algorithm provides a reliable method for managing cardiac arrhythmia data within critical design constraints.
Abstract:
Implantable cardioverter defibrillators (ICD's) detect, diagnose and treat the potentially fatal heart arrhythmias known as bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF) in cases where these arrhythmias are resistant to surgical and drug-based treatments by direct sensing and electrical stimulation of the heart muscle. Since the ICD is implanted, power consumption, reliability, and size are severe design constraints. This paper targets the problems associated with increasing the signal recording capabilities of an ICD. A data-compression algorithm is described which has been optimized for low power consumption and high reliability implementation. Reliance on a patients morphology or that of a population of patients is avoided by adapting to the intracardiac electrogram (ICEG) amplitude and phase variations and by using adaptive scalar quantization. The algorithm is compared to alternative compression algorithms which are also patient independent using a subset of VT arrhythmias from a data base of 146 patients. At low distortion the algorithm is closest to the Shannon lower bound achieving an average of 3.5 b/sample at 5% root mean square distortion for a 250-Hz sample rate. At higher distortion vector quantization and Karhunen-Loeve Transform approaches are superior but at the cost of considerable additional computational complexity.