ECG Analysis Using Multi-Dimensional ST Segment Feature Comparison
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Solution Overview
Problem
Current methods for self-diagnosis and early detection of acute coronary artery occlusion, such as ST-segment elevation myocardial infarction (STEMI), lack sensitivity and specificity, often mistaking normal physiological variations for health risks, and require medical intervention for analysis.
Innovation Solution
A device and method that analyze electrocardiogram (ECG) data using a processing unit to determine multi-dimensional ST segment features, comparing them to personalized distributions from reference samples to generate a notification signal for potential myocardial infarction, allowing for self-assessment without medical intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current self-diagnosis methods are used for detecting myocardial infarction, then the device complexity is reduced, but the measurement precision and reliability are insufficient leading to high false positive rates
Solution Approach 1:
The ECG signal is segmented into multiple leads (I, II, III, aVR, aVF, aVL) and further divided into specific segments (P, QRS, T, ST) for independent analysis. This segmentation allows detailed examination of each segment's morphology and temporal characteristics, improving detection precision while maintaining relatively simple device architecture.
Solution Approach 2:
The invention transitions from single-dimensional amplitude analysis to multi-dimensional analysis by incorporating temporal dynamics, morphological features, and spatial relationships across multiple leads. The system analyzes ST segment elevation in multiple dimensions (amplitude, duration, slope, curvature) and across multiple leads simultaneously, significantly improving detection precision without requiring complex hardware.
2Measurement precision
If traditional ECG analysis methods are used, then the measurement precision can be maintained, but the productivity is reduced due to requirement for medical professional intervention
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing ECG signals and generating diagnostic reports without requiring medical professional intervention. The automated algorithm processes ECG data, identifies abnormal patterns, and provides guidance for potential myocardial infarction detection, enabling users to perform their own health assessment while maintaining high detection precision through sophisticated signal processing.
Solution Approach 2:
The invention replaces the mechanical system of manual medical evaluation with an automated computational system. The processing unit automatically performs signal processing, feature extraction, and diagnostic analysis that would traditionally require medical professionals, thereby increasing productivity and detection throughput while maintaining measurement precision through algorithmic accuracy.
3Reliability
If sensitivity is increased to detect more myocardial infarction cases, then the detection capability improves, but the false positive rate increases due to physiological variations
Solution Approach 1:
The system dynamically adapts its analysis based on the patient's physiological state by continuously monitoring ECG signals and comparing them against established normal variations. The algorithm accounts for physiological variations such as respiration, movement, and baseline drift by using adaptive thresholds and statistical models, allowing high sensitivity detection while minimizing false positives through context-aware analysis.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring ECG signals and comparing detected abnormalities against stored normal variations and diagnostic criteria. When potential abnormalities are detected, the system provides feedback for further analysis and can adjust its detection thresholds, reducing false positives while maintaining high sensitivity through iterative refinement of diagnostic decisions.
Data Source
AI summary
A device 1 is described for analyzing electrocardiogram data. The device comprises processing means for comparing at least one parameter, indicative of a morphological feature comprising a multi-dimensional ST segment feature, derived from a temporal sequence of electrocardiogram data to a previously determined distribution of the at least one parameter. Based on the comparison, a signal representative of a risk of a myocardial infarction occurring in the body of the user is provided. A corresponding method and device are also described. The electrocardiogram data comprises a bipolar measurement between a chest electrode point and the right upper extremity, a bipolar measurement between the left crista iliaca and the right upper extremity and a bipolar measurement between the left and the right upper extremity.