Electrogram Compressed Sensing Using Electrode Array Correlation
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Solution Overview
Problem
Existing methods struggle to efficiently compress and transmit electrophysiological data, such as electrograms, especially during arrhythmias, due to high data rates and power consumption constraints, leading to potential loss of vulnerable data.
Innovation Solution
The use of spatiotemporally-correlated compressed sensing (CS) and rakeness processing techniques to achieve higher compression ratios (CR) while maintaining signal quality, by exploiting the geometry of electrode arrays and applying pseudo-random codes to increase sparsity and correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression methods are used on electrograms, then data transmission bandwidth is reduced, but signal quality and reliability deteriorate during arrhythmias
Solution Approach 1:
The patent applies preliminary action by performing compressed sensing transformation on electrogram signals before transmission. The raw electrogram data is transformed into a compressed domain representation that preserves essential diagnostic information while reducing data volume. This preprocessing step ensures that even during arrhythmias, the compressed data maintains sufficient signal quality for reliable remote diagnosis.
Solution Approach 2:
The patent changes parameters by transforming the electrogram signal from time-domain to a compressed sensing domain using mathematical transformations. By adjusting compression ratios and selecting appropriate basis functions, the system optimizes the balance between data reduction and signal preservation, maintaining diagnostic reliability while significantly reducing transmission requirements.
2Use of energy by moving object
If high compression ratios are applied to electrograms, then power consumption and transmission bandwidth are reduced, but data loss increases
Solution Approach 1:
The system performs preliminary compressed sensing transformation to create a compact representation of electrogram data before wireless transmission. This preprocessing reduces the amount of data that needs to be transmitted and stored, thereby lowering power consumption of the implantable device while preserving critical diagnostic information through optimized compression algorithms.
Solution Approach 2:
The patent replaces traditional mechanical/digital compression methods with compressed sensing mathematical transformations. This substitution enables higher compression ratios with better information preservation by exploiting the sparse representation of physiological signals in appropriate bases, reducing both power consumption and information loss simultaneously.
3Reliability
If raw electrogram data is transmitted without compression, then signal quality is maintained, but transmission bandwidth and power consumption increase significantly
Solution Approach 1:
The patent applies preliminary compressed sensing transformation to electrogram signals before transmission. This preprocessing step creates a compact domain representation that requires significantly less power for wireless transmission while preserving diagnostic signal quality. The transformation is performed locally in the implantable device, reducing the energy burden of data transmission.
Solution Approach 2:
The system changes parameters by transforming electrogram data from raw time-domain format to a compressed sensing representation. This parameter transformation reduces the data volume by several orders of magnitude, thereby dramatically reducing transmission power requirements while maintaining signal fidelity through optimized reconstruction algorithms.
4Measurement precision
If high sampling rates are used for electrograms, then diagnostic accuracy is improved, but data volume and processing complexity increase
Solution Approach 1:
The patent performs preliminary compressed sensing transformation on high-rate electrogram samples before further processing or transmission. This preprocessing step exploits the temporal and spatial correlations in the signal to create a compact representation, reducing processing complexity downstream while preserving the diagnostic accuracy achieved through high sampling rates.
Solution Approach 2:
The system changes parameters by transforming the electrogram signal from high-rate time-domain samples to a compressed sensing domain representation. This transformation reduces the data dimensionality while preserving essential diagnostic features, thereby reducing processing complexity without sacrificing measurement precision or diagnostic accuracy.
Data Source
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AI summary
An apparatus includes data acquisition circuitry and a processor. The data acquisition circuitry is configured to acquire multiple signals using multiple respective electrodes of an array of electrodes coupled to one of an organ of a patient and tissue or a cell culture. The processor is configured to hold a definition of a mixed-norm that is defined as a function of relative positions of the electrodes in the array, and jointly compress the multiple signals in a compressed-sensing (CS) process that minimizes the mixed-norm.