Morphology-based Discrimination Algorithm for Cardiac Rhythm Analysis
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Solution Overview
Problem
Existing implantable cardioverter-defibrillators (ICDs) often deliver unnecessary electrical shocks due to difficulties in distinguishing between supraventricular tachycardias (SVTs), monomorphic ventricular tachycardias (VT), and polymorphic VT/VF, which requires computationally expensive shifting and alignment of electrogram signals.
Innovation Solution
An implantable medical device method that normalizes and bins digital values of heart depolarizations, allowing for comparisons without shifting or aligning, to reliably discriminate between SVT and VT or monomorphic VT from polymorphic VT/VF, reducing the number of clock cycles and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wavelet comparison method is used to discriminate heart rhythms, then discrimination accuracy is improved, but computational cost and energy consumption increase
Solution Approach 1:
The patent segments the electrogram signal into discrete amplitude levels (bins) rather than processing the continuous signal. By dividing the amplitude range into discrete segments and counting occurrences in each bin, the method reduces computational complexity while preserving morphological discrimination capability. This segmentation approach eliminates the need for computationally expensive wavelet transforms and signal alignment operations.
Solution Approach 2:
The patent replaces the mechanical signal processing operations (shifting, aligning, wavelet transformation) with a statistical counting approach. Instead of performing complex mathematical transformations and comparisons, the system uses histogram binning and correlation coefficient calculations on binned amplitude data, significantly reducing computational requirements and energy consumption in implantable devices.
2Reliability
If signal shifting and alignment is performed for morphologic discrimination, then discrimination reliability is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent changes the parameter space from continuous time-domain signal values to discrete amplitude bin counts. By transforming the signal representation from raw amplitude-time data to histogram bin frequencies, the method eliminates the need for time-alignment operations while maintaining the ability to discriminate between different rhythm morphologies through correlation analysis of the binned data distributions.
3Measurement precision
If more template beats are used for comparison, then discrimination accuracy is improved, but number of comparisons and processing time increase
Solution Approach 1:
The patent performs preliminary binning of the template beat data before comparison operations. By pre-processing the template beats into binned amplitude histograms and calculating their statistical properties in advance, the system reduces the computational burden during real-time rhythm discrimination. This preliminary action allows for faster comparisons while maintaining high discrimination accuracy through the use of pre-computed correlation coefficients.
Data Source
AI summary
Techniques for morphologic discrimination between beats of a tachyarrhythmia episode are described for selecting delivery of appropriate therapy. An exemplary method comprises nonordered binning of digitized amplitude values of signals associated with cardiac depolarizations. Monomorphic VT is discriminated from polymorphic VT without signal alignment. One exemplary method involves sensing electrical signals associated with depolarizations of a patient's heart during a tachyarrhythmia episode. The sensed electrical signals are converted to digital values and stored. The stored digital values are normalized and binned. At most, 5 pairs of beats or depolarizations are compared for morphologic similarity by determining the similarity between the binned values associated with each pair. The result of the comparison is used to select and deliver therapy to the patient.