Wavelet Domain Cardiac Signal Analysis for Portable Devices
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Solution Overview
Problem
Current power spectral analysis (PSA) methods for cardiac signals are complex and resource-intensive, making real-time analysis on portable devices challenging due to the high computational requirements and energy consumption, especially when dealing with non-uniformly spaced data from heart rate variability (HRV) signals.
Innovation Solution
The method employs a wavelet domain transformation to express cardiac signals in an approximately sparse representation, allowing for intelligent energy-quality trade-offs by pruning less-significant operations and using modified Fourier operations with threshold-based pruning to reduce computational complexity and energy usage while maintaining diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PSA methods (FFT or AR modeling) are used to analyze cardiac signals, then spectral analysis can be performed, but the computational complexity and energy consumption are too high for portable devices
Solution Approach 1:
The patent extracts and processes only the most significant spectral components (low frequency power and high frequency power) rather than computing the complete power spectral density across all frequencies. This selective extraction reduces computational complexity and energy consumption while maintaining the diagnostic accuracy needed for detecting cardiac malfunctions such as sinus arrhythmia.
Solution Approach 2:
The patent implements a simplified version of spectral analysis that computes only the essential frequency bands (0.04-0.15 Hz for low frequency, 0.15-0.4 Hz for high frequency) required for cardiac diagnosis, rather than performing a complete spectral analysis across the entire frequency spectrum. This partial action approach reduces energy consumption while preserving measurement precision for clinically relevant parameters.
2Reliability
If complete spectral analysis is performed to ensure diagnostic accuracy, then detection capability is maintained, but computational complexity increases
Solution Approach 1:
The patent extracts only the critical frequency components (low frequency power in 0.04-0.15 Hz band and high frequency power in 0.15-0.4 Hz band) that are sufficient for detecting cardiac malfunctions. By eliminating computation of non-essential frequency bands, the algorithm complexity is reduced while maintaining reliable detection capability for clinical diagnosis.
Solution Approach 2:
The patent segments the frequency spectrum into distinct bands (low frequency 0.04-0.15 Hz and high frequency 0.15-0.4 Hz) and processes each band independently, focusing computational resources only on the segments that provide diagnostic value. This segmentation approach simplifies the overall algorithm while preserving detection reliability for cardiac conditions.
3Ease of operation
If portable devices are used for health monitoring, then accessibility and real-time monitoring are improved, but power and hardware resources are limited
Solution Approach 1:
The patent implements a partial spectral analysis that computes only the essential frequency bands required for cardiac diagnosis rather than the complete spectrum. This reduction in computational scope enables the system to run on portable devices with limited power resources while maintaining ease of operation and real-time monitoring capabilities.
Solution Approach 2:
The patent extracts and processes only the most clinically relevant spectral components (low frequency and high frequency power) needed for detecting cardiac malfunctions. This selective extraction reduces the computational burden on portable devices, enabling real-time health monitoring with limited power and hardware resources while preserving ease of operation.
Data Source
AI summary
A method and device for reducing the computational complexity of a processing algorithm, of a discrete signal, in particular of the spectral estimation and analysis of bio-signals, with minimum or no quality loss, which comprises steps of (a) choosing a domain, such that transforming the signal to the chosen domain results to an approximately sparse representation, wherein at least part of the output data vector has zero or low magnitude elements; (b) converting the original signal in the domain chosen in step (a) through a mathematical transform consisting of arithmetic operations resulting in a vector of output data; (c) reformulating the processing algorithm of the original signal in the original domain into a modified algorithm consisting of equivalent arithmetic operations in the domain chosen in step (a) to yield the expected result with the expected quality quantified in terms of a suitable application metric; (d) combining the mathematical transform of step (b) and the equivalent mathematical operations introduced in step (c) for obtaining the expected result within the original domain with the expected quality; (e) selecting a threshold value based on the difference in the mean magnitude value of the elements of the output data vector of the transform said in step (b) and the preferred complexity reduction and degree of output quality loss that can be tolerated in the expected result within the target application; (f) pruning a number of elements the magnitude of which is less than the threshold value selected in step (e); and/or eliminating arithmetic operations associated with the pruned elements of step (f) either in the mathematical transform of step (b) and/or in the equivalent algorithm of step (c).