Seismocardiography Autoencoders for Reliable Heart Failure Indication
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Solution Overview
Problem
Existing seismocardiography methods struggle to reliably identify heart failure due to varying amplitude measures caused by different underlying conditions, making it challenging to diagnose heart failure accurately.
Innovation Solution
A method using a trained autoencoder to analyze chest wall vibrations measured by an accelerometer, comparing reconstructed signal intervals with original intervals to determine heart failure indication based on correlation differences between healthy and unhealthy subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If amplitude measures are used to identify heart failure in SCG signals, then diagnostic capability is improved, but reliability deteriorates due to varying amplitude measures caused by different underlying conditions
Solution Approach 1:
The patent transforms the SCG signal through multiple parameter changes: filtering to isolate specific frequency ranges, computing derivatives to extract rate-of-change information, and calculating temporal intervals between fiducial points. These transformations convert the unreliable amplitude measures into stable temporal interval measures that maintain diagnostic capability while improving reliability across different heart failure conditions
Solution Approach 2:
The patent introduces temporal intervals between fiducial points as an intermediary parameter that mediates between the raw SCG signal and the final diagnostic indicator. These intervals serve as stable intermediate representations that are less sensitive to the varying amplitude measures caused by different underlying conditions, thereby improving reliability while preserving diagnostic information
2Loss of information
If both low frequency SCG components and audible components are sampled simultaneously using an accelerometer, then comprehensive cardiovascular information is obtained, but diagnostic complexity increases due to different approaches needed for different components
Solution Approach 1:
The patent segments the simultaneously sampled SCG signal into distinct components based on frequency characteristics. By applying frequency filtering, the signal is divided into low-frequency SCG components (below 5 Hz) and audible heart sound components (above 40-60 Hz), allowing each component to be analyzed with appropriate diagnostic methods while maintaining comprehensive cardiovascular information
Solution Approach 2:
The patent dynamically processes the segmented signal components through derivative computations and temporal interval measurements. This dynamic transformation converts the complex multi-component signal into standardized temporal metrics that simplify diagnostic interpretation while preserving the comprehensive information from both SCG and heart sound components
Data Source
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AI summary
A technology for determining an indication of heart failure of a subject (18) is proposed. It comprises: obtaining (100a) a first signal interval (36) from a source signal recorded with an accelerometer (14) placed on the chest of a subject (18), wherein the first signal interval (36) corresponds to a first subinterval of a heart cycle; inputting (200a) the first signal interval (36) into a first autoencoder, wherein the first autoencoder is trained on the corresponding first signal intervals obtained from healthy subjects and outputs a reconstructed first signal interval (44), determining (300a) a first correlation between the first signal interval (36) and the reconstructed first signal interval (44), and determining (400) the indication of heart failure based on the first correlation.