ECG Onset Identification via Segmented Processing
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Solution Overview
Problem
Current cardiac monitoring systems face challenges in accurately identifying the true onset of cardiac events from electrocardiogram (ECG) data, leading to inefficient processing, unnecessary reclassification, and potential missed events due to limited computing resources and sensitivity in mobile devices.
Innovation Solution
A method and system that utilize a computer processor to receive and analyze ECG data from remote monitoring devices, identifying critical rhythms and determining the true onset of cardiac events, which involves modifying the ECG data to facilitate accurate treatment and reduce unnecessary processing by distinguishing the initial classification from the server or mobile device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cardiac monitoring systems process all ECG data with high sensitivity to identify true onset events, then diagnostic accuracy improves, but computational resources and processing time are excessively consumed
Solution Approach 1:
The patent segments ECG data processing into multiple stages: initial event detection using simplified criteria, followed by secondary verification only for detected events. This segmentation allows the system to process all data efficiently while applying high-sensitivity analysis only where necessary, resolving the contradiction between comprehensive diagnostic accuracy and processing efficiency
Solution Approach 2:
The system performs preliminary filtering of ECG data using basic detection algorithms before applying more computationally intensive onset identification methods. By performing preliminary action on all data points to identify candidate events, the system reduces the volume of data requiring high-sensitivity processing, thereby maintaining diagnostic accuracy while improving overall processing efficiency
2Reliability
If the system applies multiple classification algorithms to ensure accurate event identification, then reliability improves, but device complexity increases
Solution Approach 1:
The patent implements a dynamic classification system where the complexity and type of algorithms applied adapt based on the detected event characteristics. For clearly identifiable events, simpler algorithms suffice; for ambiguous cases, more complex multi-algorithm verification is triggered. This dynamic approach maintains high reliability while avoiding unnecessary computational complexity in routine cases
Solution Approach 2:
The system introduces an intermediary verification stage between initial detection and final classification. This intermediary layer uses simplified criteria to filter out clearly false positives before subjecting remaining candidates to full multi-algorithm classification. This mediator reduces the number of events requiring complex processing, maintaining reliability while reducing average system complexity
3Reliability
If the monitoring system continuously analyzes ECG data with high sensitivity, then false negatives are reduced, but energy consumption increases
Solution Approach 1:
The patent employs periodic analysis intervals where the system switches between low-power monitoring mode and high-sensitivity analysis mode. During normal operation, simplified detection runs continuously with minimal energy consumption. When potential events are detected, the system transitions to periodic high-sensitivity analysis to confirm true positives and reduce false negatives, thereby balancing reliability with energy efficiency
Solution Approach 2:
The system applies high-sensitivity analysis locally only to specific time windows around detected potential events rather than continuously to all ECG data. This localized application of intensive processing ensures false negatives are reduced at critical moments while consuming minimal energy during normal baseline periods, resolving the contradiction between reliability and energy consumption
Data Source
AI summary
Techniques for identifying onset of a cardiac event. Embodiments receive biometric data measured by at least one remote monitoring device, the biometric data comprising an electrocardiogram (ECG) data relating to a patient. It is determined that the ECG data includes a plurality of critical rhythms, and one of the plurality of critical rhythms is identified as an onset event. The ECG data is modified, based on the identified onset event. The modified ECG data facilitates treatment of the patient related to the identified onset event.