ECG Signal Encoding and Denoising for Sample-Level Waveform Labeling
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Solution Overview
Problem
Existing ECG signal analysis methods face challenges in effectively denoising and accurately labeling ECG waveforms, which can impact the diagnosis and treatment of cardiac issues.
Innovation Solution
A method involving a trained denoising model with an encoder, residual vector quantizer, and decoder is used to process ECG signals, followed by encoding, quantizing, and decoding to achieve denoising, and a trained sample-level ECG classifier is employed for labeling ECG waveforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECG signal processing methods are used, then the analysis process is simple and fast, but the denoising effectiveness and labeling accuracy are insufficient
Solution Approach 1:
The patent introduces an encoder as an intermediary component that transforms the original ECG signal into a latent representation space. This encoder serves as a mediator between the raw signal and the denoising/labeling processes, enabling more accurate processing by operating on the encoded representation rather than the raw signal directly.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with a neural network-based denoising model. This model uses learned representations and transformations to achieve superior denoising and labeling accuracy compared to conventional filtering and analysis techniques.
2Reliability
If a neural network denoising model with encoder and decoder is used, then the denoising effectiveness is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs encoding of the ECG signal as a preliminary action before denoising and labeling. By transforming the signal into a compact latent representation first, the subsequent processing operations become more efficient and effective, reducing the overall processing time despite the added encoding step.
Solution Approach 2:
The patent divides the ECG signal processing into distinct segments: encoding, denoising, and labeling. This segmentation allows each component to be optimized independently, with the encoder creating a compressed representation that speeds up subsequent denoising and labeling operations compared to processing the full-resolution signal.
3Loss of information
If sample-level ECG labeling is performed, then the diagnostic information is enhanced, but the analysis complexity and computational requirements increase
Solution Approach 1:
The patent transitions from traditional waveform-based ECG analysis to sample-level labeling by adding a temporal dimension to the analysis. Each sample point in the ECG signal is independently labeled, creating a fine-grained representation that captures detailed diagnostic information while using the encoded latent space to manage computational complexity.
Data Source
AI summary
Described herein are techniques for analyzing at least one electrocardiogram (ECG) signal. In some embodiments, the techniques include: receiving at least one ECG signal; encoding the at least one ECG signal using the encoder to obtain a numeric encoding of the at least one ECG signal; and processing the numeric encoding of the at least one ECG signal using at least one trained machine learning model to obtain: (i) at least one denoised ECG signal corresponding to the at least one ECG signal, and/or (ii) characteristics of the at least one ECG signal, the characteristics comprising: (i) rhythm types including a respective rhythm type for each of at least some segments of the at least one ECG signal, and/or (ii) sample-level ECG labels including a respective sample-level ECG label for each of at least some of the plurality of samples.