Key result
AI-MMA improves TWA and PWA accuracy over unsupervised methods, matching expert overreading.
Why the study?
Modified moving average analysis dynamically tracks alternans, prompting development of convolutional neural network algorithms to achieve rapid, efficient, and accurate assessment of PWA, RWA, and TWA.
Does an AI-enabled CNN algorithm improve the accuracy of modified moving average analysis for P- and T-wave alternans compared to unsupervised automated output?
Population
Simulated ECGs and clinical ECGs (n = 5 for TWA, n = 45 for PWA)
Comparison
AI-MMA vs unsupervised automated MMA and conventional MMA with expert overreading
Design
Algorithm development and validation study
Authors
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May facilitate automated alternans analysis in ECG systems; leaves open prospective validation for arrhythmia risk stratification.
Does an AI-enabled CNN algorithm improve the accuracy of modified moving average analysis for P- and T-wave alternans compared to unsupervised automated output?
AI-enabled CNN algorithms for MMA analysis of ECG alternans achieve accuracy comparable to expert overreading, potentially enabling automated arrhythmia risk tracking in diverse monitoring systems.
Nearing et al. (2023) studied Atrial and ventricular arrhythmias (n=50). AI-enabled modified moving average (MMA) analysis using convolutional neural networks vs. Unsupervised automated MMA output and conventional MMA with expert overreading was evaluated on Accuracy of AI-MMA algorithms in TWA and PWA analysis. AI-enabled modified moving average analysis using convolutional neural networks significantly improved accuracy over unsupervised output for TWA (p=0.036) and PWA (p<0.005), matching expert overreading.
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