Confidence Analyzer for AED ECG Analysis During CPR
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Solution Overview
Problem
Existing ECG analysis algorithms for defibrillators struggle to accurately determine shockable rhythms during cardiopulmonary resuscitation (CPR) due to noise artifacts, leading to delays in treatment and potential false positives or negatives, especially in the presence of CPR noise, which can reduce the effectiveness of CPR and defibrillation outcomes.
Innovation Solution
The development of an ECG analysis algorithm, Optimized Arrhythmia Recognition Technology (ART), which uses a series of fixed-frequency band pass filters to suppress CPR-related noise, allowing for real-time identification of shockable cardiac rhythms with high sensitivity and specificity, even during CPR, and includes a confidence analyzer to adjust shock decision criteria based on the reliability of the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing ECG analysis algorithms are used during CPR, then the device can provide rhythm analysis, but CPR-related noise artifacts cause false positives or negatives and reduce analysis accuracy
Solution Approach 1:
The ECG signal processing is divided into multiple frequency bands using bandpass filters (e.g., 0.5-40 Hz for ECG, 0.5-10 Hz for CPR artifacts). By segmenting the signal into different frequency components, the algorithm can selectively analyze ECG features while suppressing CPR-related noise, thereby maintaining analysis accuracy during ongoing CPR.
Solution Approach 2:
The algorithm extracts and removes CPR artifact components from the ECG signal by identifying characteristic CPR frequency ranges and subtracting them from the composite signal. This extraction process isolates the true ECG morphology, enabling accurate rhythm detection even when CPR is being performed.
2Measurement precision
If CPR is interrupted for ECG analysis, then analysis accuracy improves, but treatment time is delayed
Solution Approach 1:
The algorithm enables continuous ECG analysis during ongoing CPR by using frequency-domain filtering to suppress artifact noise in real-time. This eliminates the need to interrupt CPR for analysis, maintaining continuous blood flow while providing accurate rhythm detection and enabling immediate defibrillation when indicated.
Solution Approach 2:
The patent replaces the mechanical approach of stopping CPR to perform analysis with a signal processing approach. By using digital filtering and frequency analysis, the system achieves accurate rhythm detection through computational methods rather than mechanical interruption, thereby maintaining treatment continuity.
3Reliability
If dual ECG analysis algorithms are used, then sensitivity and specificity improve, but device complexity increases
Solution Approach 1:
The system dynamically selects and switches between different ECG analysis algorithms based on the detected rhythm type and CPR status. For example, it may use one algorithm optimized for VF detection and another for VT detection, or switch between time-domain and frequency-domain analysis methods depending on signal quality, thereby optimizing performance while managing complexity.
Solution Approach 2:
The patent introduces an intermediary confidence analysis layer that evaluates the output of multiple ECG analysis algorithms. This confidence metric integrates results from different algorithms and determines whether a shockable rhythm is definitively detected, thereby coordinating multiple algorithms while maintaining clear decision-making logic and manageable system complexity.
Data Source
AI summary
A defibrillator and method for using a defibrillator which adopts an ECG analysis algorithm that can detect a cardiac arrhythmia in the presence of noise artifact induced by cardio pulmonary resuscitation (CPR) compressions. The apparatus and method includes a confidence analyzer circuit which determines the confidence level of an electrotherapy shock decision based on the detection. If the confidence level is low, the apparatus adjusts its shock decision criteria.