Quasi-Periodic Waveform Decomposition for Wide-Range Frequency Tracking
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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 bandwidth due to repeated sampling of unremarkable data, particularly in capturing the underlying waveform of complex signals like ECGs or seismic signals.
Innovation Solution
A method and apparatus using a plurality of overlapping filter banks with closely spaced center frequencies and wavelet transforms to track the strongest frequency components, allowing for efficient tracking of frequency changes over a wide range, such as heartbeats from 30 to 300 BPM, and reconstructing quasi-periodic waveforms with phase-adjusted decompositions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity digital representation is used to capture waveforms, then waveform integrity is improved, but storage requirements and data quantity increase significantly
Solution Approach 1:
The patent extracts only the essential frequency components (fundamental and harmonics) from the complex waveform signal using filter banks, rather than storing the entire high-fidelity signal. This selective extraction maintains waveform integrity while dramatically reducing data quantity by discarding redundant information.
Solution Approach 2:
The patent creates a simplified digital representation (copy) of the waveform using only the essential frequency components captured by the filter banks. This copy preserves the critical waveform characteristics needed for analysis while requiring minimal storage space compared to the original high-fidelity signal.
2Measurement precision
If high-fidelity digital representation is used, then waveform accuracy is improved, but processing bandwidth and power consumption increase
Solution Approach 1:
The patent extracts only the essential frequency components needed for accurate waveform representation, processing merely these extracted components rather than the entire high-fidelity signal. This selective processing maintains waveform accuracy while significantly reducing computational power requirements.
3Measurement precision
If conventional filter banks are used for signal decomposition, then frequency analysis is achieved, but tracking frequency components over wide ranges is difficult
Solution Approach 1:
The patent implements a dynamic filter bank system where the center frequencies and bandwidths of the filters are not fixed but can be adjusted to track frequency components across a wide range. This dynamic adaptation allows the system to maintain accurate frequency analysis regardless of whether the signal components are in the low-frequency or high-frequency range.
Solution Approach 2:
The patent designs the filter bank with overlapping frequency ranges and adjustable parameters, creating a universal system that can effectively analyze and track frequency components across the entire spectrum from low to high frequencies using the same structural framework.
4Measurement precision
If sampling rate is increased to capture waveform details, then measurement precision is improved, but data bandwidth and storage requirements increase
Solution Approach 1:
The patent extracts the essential waveform information contained in the fundamental frequency and harmonic components through filter banks, rather than storing all the detailed samples. This extraction approach preserves the critical waveform characteristics while eliminating redundant data, achieving efficient storage without sacrificing measurement precision.
Data Source
AI summary
A system and method for representing quasi-periodic (“qp”) waveforms, for example, representing a plurality of limited decompositions of the qp waveform. Each decomposition includes a first and second amplitude value and at least one time value. In some embodiments, each of the decompositions is phase adjusted such that the arithmetic sum of the plurality of limited decompositions reconstructs the qp waveform. Data-structure attributes are created and used to reconstruct the qp waveform. Features of the qp wave are tracked using pattern-ecognition techniques. The fundamental rate of the signal (e.g., heartbeat) can vary widely, for example by a factor of 2-3 or more from the lowest to highest frequency. To get quarter-phase representations of a component (e.g., lowest frequency “rate” component) that varies over time (by a factor of two to three) many overlapping filters use bandpass and overlap parameters that allow tracking the component's frequency version on changing quarter-phase basis.