Multiscale STFT for ECG QRS Complex Detection
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Solution Overview
Problem
Existing methods for detecting QRS complexes in electrocardiogram (ECG) signals require high computational loads, necessitating the development of computationally efficient techniques.
Innovation Solution
A method involving a multiscale short-time Fourier transform (STFT) is applied to ECG signals, followed by refinement processes using Gaussian-modulated complex exponentials to identify QRS complexes by determining peak parameters and solving linear systems, allowing for efficient detection and display of QRS onset and offset times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for detecting QRS complexes, then detection accuracy can be maintained, but computational load becomes excessively high
Solution Approach 1:
The patent segments the QRS complex detection process into multiple stages: (1) performing multiscale STFT to obtain transform array, (2) determining initial peak parameters, (3) refining parameters by computing inner products with windowed sinusoids, and (4) solving linear systems to obtain final parameters. This segmentation allows the system to process information progressively, reducing overall computational burden while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary actions by first determining initial peak parameters from the transform array before performing more computationally intensive refinement operations. The initial peak detection provides a starting point that guides subsequent refinement steps, avoiding the need to perform expensive computations on all possible parameters from scratch.
2Productivity
If computationally efficient methods are used, then processing speed increases, but detection accuracy may be compromised
Solution Approach 1:
The patent employs dynamic refinement where the level of computational processing is adjusted based on the characteristics of detected peaks. By computing inner products with windowed sinusoids and solving linear systems only for peaks that meet certain criteria, the system dynamically allocates computational resources to maintain accuracy where needed while processing speedily where possible.
Solution Approach 2:
The patent changes parameters such as window width, window displacement, and frequency to optimize the detection process. By adjusting these parameters across different scales and resolving linear systems to obtain refined parameters, the system achieves both computational efficiency and accurate QRS complex identification.
Data Source
AI summary
A system, method, and memory medium for operating on an electrocardiogram (ECG) signal. A multiscale short-time Fourier transform (STFT) is perform on a set of ECG samples {s(n)} to obtain a transform array. For each sufficiently energetic peak in the transform array, a refined window width value and a refined window displacement value is generated by: computing an inner product between the set of samples and each of a plurality of functions, where the plurality of functions are sufficiently close to a coarse approximation function given by the peak location; and solving a linear system Av=c for the unknown vector v, where the vector c is determined by the inner products, where the matrix A is determined by the center times of the plurality of functions. After appropriate selection, the refined window width and refined window displacement may be used to represent ECG waveform features.