Periodicity-Aware Coding Architectures for Raw ECG Analysis
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Solution Overview
Problem
Conventional techniques for analyzing physiological waveforms, such as ECG data, are limited by their reliance on pre-aligned single ECG beat processing, loss of valuable information through filtering and segmentation, and inability to leverage signal periodicity, leading to inaccurate diagnosis and increased false alarms, especially in conditions like AFib and sepsis.
Innovation Solution
A computer-implemented method using a trained coding architecture with encoding layers that processes raw input signals to generate a reduced dimensionality representation, extracting time and phase features without preprocessing, enabling robust analysis of cardiac arrhythmias and other conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional autoencoders are used for feature extraction, then dimensionality reduction is achieved, but information from the initial data set is discarded and features specific to physiological waveforms are not leveraged
Solution Approach 1:
The patent changes the parameters of the autoencoder architecture by incorporating signal periodicity awareness and waveform-specific feature extraction capabilities, transforming a generic image-analysis autoencoder into one suited for physiological waveform analysis while preserving information
Solution Approach 2:
The patent segments the waveform analysis into distinct phases (e.g., P-wave, QRS complex, T-wave) and extracts features from each segment separately, allowing comprehensive information retention while adapting to the specific structure of physiological signals
2Quantity of substance
If conventional techniques compress voluminous data by mapping to lower dimensional space, then data compression is achieved, but valuable information is discarded
Solution Approach 1:
The patent introduces an intermediary layer that preserves the relationship between the original high-dimensional data and the compressed low-dimensional representation, allowing information to be retained in the transformation process rather than discarded
Solution Approach 2:
The patent transforms the data compression approach by utilizing additional dimensions in the feature space that capture waveform-specific characteristics, allowing compression while preserving medically relevant information through enhanced dimensional representation
3Device complexity
If existing approaches filter and segment raw input signals before analysis, then processing complexity is reduced, but a full range of features cannot be generated
Solution Approach 1:
The patent performs preliminary organization of the raw signal data into structured formats that preserve all necessary information for comprehensive feature extraction, preparing the data in advance so that no valuable features are lost during subsequent analysis
Solution Approach 2:
The patent creates a multi-functional processing framework that can extract diverse features (temporal, spectral, morphological) from the raw signals without requiring separate filtering and segmentation steps, achieving comprehensive feature extraction while maintaining manageable complexity
Data Source
AI summary
A method includes receiving raw input signals, analyzing the raw signals using a trained coding architecture including an encoding layer; and displaying an output. A computing system includes a processor and a memory storing instructions that when executed by the processor, cause the computing system to receive raw input signals, analyzing the raw signals using a trained coding architecture and display an output. A non-transitory computer readable medium includes program instructions that when executed cause a computer to receive raw input signals, analyze the raw signals using a trained coding architecture and display an output.