Waveform Coding Architecture for Raw ECG Feature Analysis
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Solution Overview
Problem
Conventional techniques for analyzing physiological waveforms are limited by their reliance on pre-aligned single ECG beat processing, loss of valuable information through filtering and segmentation, inability to leverage signal periodicity, and inefficiency in handling multiple conditions, leading to inaccurate diagnosis and increased false alarms.
Innovation Solution
A computer-implemented method using coding architectures that analyze raw input physiological signals to generate embedded features through a reduced dimensionality representation, enabling visualization and noise detection, and integrating with electronic health records for robust modeling of various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional techniques use pre-aligned single ECG beat processing, then the analysis can be simplified, but valuable information is lost and measurement precision deteriorates
Solution Approach 1:
The patent segments the ECG signal into multiple individual beats rather than requiring pre-alignment of a single beat. Each beat is processed independently through the coding architecture, allowing comprehensive analysis of multiple cardiac cycles while maintaining manageable computational complexity through modular processing.
Solution Approach 2:
The patent transforms the ECG analysis from traditional time-domain single-beat processing to a multi-dimensional approach using coding architectures that process multiple beats simultaneously. This dimensional transformation enables extraction of embedded features across multiple beats, improving diagnostic precision without proportionally increasing system complexity.
2Ease of operation
If filtering and segmentation routines are applied to ECG data, then data processing becomes easier, but information is discarded and measurement precision deteriorates
Solution Approach 1:
The patent applies minimal preliminary filtering and segmentation only when necessary, rather than applying extensive preprocessing routines. The coding architecture is designed to handle raw or minimally processed ECG data, preserving waveform information while maintaining operational ease through the robustness of the embedded feature extraction process.
3Device complexity
If conventional autoencoders are used for feature extraction, then dimensionality reduction is achieved, but features specific to physiological waveforms are not leveraged and measurement precision deteriorates
Solution Approach 1:
The patent implements a specialized coding architecture with dedicated components for processing different aspects of ECG waveforms. The encoder and decoder are designed with specific structures that leverage physiological waveform characteristics, such as periodicity and morphological features, rather than using generic autoencoder architectures. This localized optimization improves arrhythmia analysis precision while maintaining manageable complexity.
4Power
If existing approaches analyze processed rather than raw input signals, then computation is reduced, but a full range of features cannot be generated and measurement precision deteriorates
Solution Approach 1:
The patent applies minimal preprocessing (partial action) rather than extensive filtering and segmentation, preserving most of the raw signal information. The coding architecture then processes this minimally processed signal to extract comprehensive features, achieving both computational efficiency and complete feature generation by avoiding excessive preprocessing that would discard valuable information.
Data Source
AI summary
A method includes receiving raw input signals corresponding to a duration of time, analyzing the signals using a trained coding architecture to generate embedded features, and displaying, via a graphical user interface, a visualization of the embedded features corresponding to the patient conditions. Another method includes receiving raw input signals, automatically generating embedded features corresponding to the signals' reduced dimensionality representation, and displaying multi-dimensional visualizations of the features to allow diagnosticians to analyze meaningful visual separation of conditions. A method also identifies noise in signals by receiving signals, generating embedded features, reconstructing signals from embedded features using a decoder, calculating an error between received and reconstructed signals, and determining noise levels by analyzing the error, with larger discrepancies indicating higher noise levels.