Cardiac Activity Detection Using Multi-Descriptor ECG Analysis
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Solution Overview
Problem
Current methods for analyzing cardiac electrical activity using surface electrodes are limited, focusing on a few indicators which can be insufficient to characterize cardiac activity and fail to detect characteristic electrophysiological activities, often masking singular electrical activities within noise or larger patterns.
Innovation Solution
A method involving the selection of a subset of electrophysiological descriptors from predefined ones, based on measurement context, using multiple surface electrodes to record cardiac electrical activities, estimating these descriptors, and comparing them to threshold values defined by statistical distributions of a reference group to calculate a comprehensive electrophysiological parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a limited number of electrophysiological indicators are used for analysis, then the measurement method remains simple and easy to operate, but the characterization of cardiac activity becomes insufficient and cannot detect characteristic electrophysiological activities
Solution Approach 1:
The patent segments the cardiac activity analysis into multiple independent electrophysiological descriptors (e.g., QRS duration, ST segment morphology, T wave characteristics, fragmentation indices) that can be calculated separately from ECG signals. Each descriptor captures a specific aspect of cardiac electrophysiology, allowing comprehensive characterization while maintaining modular processing that preserves operational simplicity.
Solution Approach 2:
The patent transitions from traditional single-parameter analysis to multi-dimensional electrophysiological characterization by introducing numerous descriptors that operate in different analytical dimensions (temporal, morphological, spectral). This dimensional expansion enables detection of subtle cardiac abnormalities that would be invisible in conventional limited-parameter analysis.
2Measurement precision
If multiple electrophysiological descriptors are calculated and analyzed, then the characterization of cardiac activity becomes comprehensive and characteristic activities can be detected, but the complexity of the measurement and analysis method increases
Solution Approach 1:
The patent implements a universal analysis framework that can calculate multiple electrophysiological descriptors from the same ECG signal data using standardized processing algorithms. This multi-functional approach allows comprehensive cardiac characterization through a single integrated system that handles various descriptors (temporal, morphological, spectral) uniformly, reducing the practical complexity despite the increased analytical depth.
Solution Approach 2:
The patent systematically varies analytical parameters by introducing multiple descriptors with different calculation methods and thresholds tailored to specific cardiac conditions. Each descriptor optimizes detection sensitivity for particular electrophysiological abnormalities, enabling precise measurement without requiring complex custom analysis for each condition.
3Productivity
If traditional ECG analysis methods are used, then the measurement process remains quick and efficient, but singular electrical activities are drowned in noise or masked by larger electrical patterns
Solution Approach 1:
The patent applies preliminary signal processing actions including filtering, baseline correction, and QRS complex detection before calculating electrophysiological descriptors. These preparatory steps enhance signal quality and isolate singular electrical activities from noise and larger patterns, ensuring reliable detection while maintaining efficient processing that preserves measurement speed.
Solution Approach 2:
The patent introduces intermediate processing steps and computational descriptors that act as mediators between raw ECG signals and final diagnostic conclusions. These intermediate descriptors (e.g., fragmentation indices, morphological features) extract and amplify subtle singular activities, making them detectable despite being masked in traditional analysis, without significantly increasing overall measurement time.
Data Source
AI summary
A method for detecting a representative cardiac activity includes the arrangement of a plurality of electrodes, the measurement of the value of electrophysiological descriptors, and the calculation (CALC) of a score.