Hybrid-Coding Scheme for Quasi-Periodic Waveform Signal Decomposition
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Solution Overview
Problem
High-fidelity digital representations of waveforms require large storage and result in unnecessary data, leading to wasted processing and storage bandwidth, especially when dealing with quasi-periodic signals that repeat unremarkable patterns.
Innovation Solution
The method employs a hybrid-coding scheme combining linear predictive coding and discrete wavelet transforms to efficiently track and record the properties of specific frequency components in quasi-periodic waveforms, allowing for accurate representation with reduced data size by using overlapping filter banks and wavelet transforms to track frequency changes over a wide range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity digital representation is used to accurately represent waveforms, then measurement precision is improved, but quantity of substance increases leading to large storage requirements
Solution Approach 1:
The patent extracts and stores only the essential characteristics of waveforms (amplitude, frequency, timing of specific features) rather than storing complete high-fidelity digital representations. This selective extraction maintains measurement precision for critical waveform properties while dramatically reducing the quantity of data that must be stored.
Solution Approach 2:
The patent applies different representation strategies to different portions of the waveform data. Critical features such as R-waves, P-waves, and T-waves in ECG signals are captured with high precision, while less critical portions are represented more compactly, optimizing the balance between fidelity and storage efficiency.
2Measurement precision
If high-fidelity digital representation is used to accurately represent waveforms, then measurement precision is improved, but loss of energy increases due to wasted processing and data bandwidth
Solution Approach 1:
By extracting only the essential waveform characteristics rather than processing complete high-fidelity signals, the system reduces computational load and energy consumption while maintaining the precision needed for medical diagnosis and analysis.
Solution Approach 2:
The patent applies processing only to the extent necessary to capture critical waveform features. Rather than processing entire high-fidelity signals, the system performs partial processing focused on extracting meaningful characteristics, thereby reducing energy waste from excessive computation.
3Measurement precision
If repeated sampling is performed on unremarkable periodic wave signals, then measurement precision is maintained, but loss of time increases due to unnecessary data collection
Solution Approach 1:
The system extracts critical waveform features and stores them in a compact format that captures essential information without requiring continuous repeated sampling of unremarkable periodic portions, thereby reducing the time spent collecting redundant data while maintaining detection accuracy.
Data Source
AI summary
A system and method for representing quasi-periodic electrocardiography waveforms, wherein system employs a hybrid-coding scheme combining linear predictive coding techniques based upon Algebraic Code Excited Linear Prediction with a discrete wavelet transforms.