ECG Signal Analysis for ST Deviation Detection
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Solution Overview
Problem
Existing systems for analyzing electrocardiogram (ECG) signals struggle with accurate detection of ST segment deviation, especially in noisy signals and with baseline variations, leading to inefficiencies in diagnosing cardiac conditions like ischemia.
Innovation Solution
A system that automatically detects and measures ST deviation by identifying key points in the ECG signal, such as the J point, using a signal processor that processes the waveform to determine exponential values, perpendicular distances, and baseline computation, while accommodating noise and baseline variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If Wavelet Transforms are used for ECG signal examination, then signal analysis capability is improved, but processing time increases and accuracy deteriorates in noisy signals
Solution Approach 1:
The patent segments the ECG signal processing into distinct stages: baseline wander removal, QRS detection, ST segment identification, and deviation calculation. Each stage processes specific signal components independently, improving efficiency while maintaining accuracy. The ST segment is further segmented into J-point, ST-point, and T-point for precise measurement.
Solution Approach 2:
The system performs preliminary baseline correction and noise filtering before main signal analysis. The baseline wander is removed first using polynomial fitting, and the QRS complex is detected beforehand to establish reference points. This preliminary processing prepares the signal for more accurate and faster ST segment analysis.
2Productivity
If automated detection is implemented, then productivity is improved, but measurement precision may deteriorate due to noise and baseline variations
Solution Approach 1:
The system incorporates feedback mechanisms where detected QRS complexes provide reference information for subsequent ST segment analysis. The measured ST deviations are fed back to adjust baseline estimates and improve subsequent measurements. This iterative feedback ensures consistent accuracy across multiple heartbeat cycles.
Solution Approach 2:
The patent dynamically adjusts analysis parameters based on signal characteristics. The baseline polynomial order, filtering cutoff frequencies, and detection thresholds are adapted according to the specific ECG signal properties. This parameter optimization maintains high measurement precision across varying signal conditions while preserving automated processing efficiency.
3Reliability
If complex signal processing is applied to accommodate baseline variation, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal baseline correction algorithm using polynomial fitting that handles various types of baseline wander and noise conditions. The same core algorithm adapts to different ECG configurations and noise levels without requiring separate processing paths, thereby improving reliability while controlling system complexity.
Solution Approach 2:
The system introduces an intermediary polynomial baseline model that mediates between the noisy raw ECG signal and the clean signal required for accurate ST deviation measurement. This polynomial baseline acts as a mediator that can be subtracted from the original signal, effectively removing baseline variations without directly modifying the underlying cardiac electrical patterns.
Data Source
AI summary
A system automatically detects and measures ST deviation of a heart wave ECG signal in the presence of noise and accommodates baseline variation of the signal and other artifacts. A system identifies a particular point in an electrophysiological signal representing heart electrical activity using an interface for receiving an electrical signal waveform comprising an R-wave and including an ST segment portion associated with heart electrical activity of a patient over a heart beat cycle. A signal processor processes data representing the electrical signal waveform by identifying an S point and T point in the electrical signal waveform and determining a first candidate J point in the electrical signal waveform having substantially a maximum distance from a line between the identified S and T points, the distance being measured perpendicularly to the line.