Physiological Index Calculation via Windowed Metadata Segmentation
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Solution Overview
Problem
Current methods for analyzing long-term physiological signals, such as ECG, face challenges in efficient data storage and real-time monitoring due to the complexity and computational intensity of multi-scale entropy calculations, limiting their application in clinical monitoring and early warning systems.
Innovation Solution
A system and method that divide physiological data into windows, analyze and update metadata to represent data characteristics, and calculate a physiological index using a combination of systematic data structures and metadata, enabling efficient storage and real-time monitoring of long-term physiological states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If long-term ECG analysis is performed to obtain complete physiological information, then measurement precision is improved, but storage space requirement increases
Solution Approach 1:
The patent divides the long-term ECG data into multiple windows (e.g., 5-minute segments) and processes each window independently. This segmentation allows the system to store only the metadata from each window rather than the complete raw data, significantly reducing storage requirements while maintaining the ability to analyze physiological information comprehensively across the entire recording period.
Solution Approach 2:
The patent extracts essential features and characteristics from the ECG data in each window and stores only these metadata (e.g., statistical parameters, entropy values) rather than the complete raw ECG signals. This extraction approach retains the critical physiological information while discarding redundant data, thereby reducing storage space consumption.
2Measurement precision
If multi-scale entropy calculation is performed to accurately reflect heart function status, then measurement precision is improved, but computation time increases
Solution Approach 1:
The patent segments the ECG data into multiple windows and calculates multi-scale entropy for each window independently. This segmentation allows parallel processing and reduces the computational burden on any single operation, enabling accurate physiological assessment without excessive computation time delays.
Solution Approach 2:
The patent performs preliminary processing by pre-calculating metadata (such as statistical parameters and basic entropy values) for each window before final physiological assessment. This preliminary action prepares the data in advance, reducing the computation time required for subsequent multi-scale entropy calculations and real-time monitoring.
3Measurement precision
If long-term physiological index is calculated to provide long-term physiological state information, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent divides the long-term ECG recording into multiple time windows and processes each window independently to calculate physiological indices. This segmentation enables the system to provide real-time physiological assessment for each window while maintaining the ability to evaluate long-term patterns, thus improving both measurement precision and productivity.
Solution Approach 2:
The patent implements continuous processing by continuously updating metadata and calculating physiological indices as new ECG data arrives in each window. This continuous action allows the system to provide real-time monitoring and early warning capabilities while maintaining comprehensive long-term physiological information through accumulated metadata across all windows.
Data Source
AI summary
A system, a method and a recording medium for calculating a physiological index are provided. The method includes: dividing a physiological data sequence into a plurality of windows; analyzing a data segment in each window to obtain metadata that represents data characteristics of the data segment; updating the metadata including the data characteristics of all data segments in the windows up to a previous window by using the metadata corresponding to one of the windows to obtain the metadata including the data characteristics of all data segments in the windows up to a current window; and finally, calculating the physiological index by using the updated metadata.