Why the study?
ECG is inconvenient for long-term remote monitoring, and room-temperature MCG sensor analysis is limited by low-frequency noise.
Does an AI-aided multi-model pipeline improve classification accuracy for arrhythmia detection compared to a conventional moving average filter in MCG sensor data?
Population
Multiple publicly available clinically annotated datasets
Comparison
AI-aided multi-model pipeline vs conventional moving average filter
Design
Algorithm development and validation study
Authors
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AI denoising may enable room-temperature MCG for remote monitoring; leaves open prospective clinical validation.
Does an AI-aided multi-model pipeline improve classification accuracy for arrhythmia detection compared to a conventional moving average filter in MCG sensor data?
An AI-aided multi-model pipeline improves denoising and arrhythmia classification accuracy for ultra-edge medical sensing devices.
Sakib et al. (2021) studied this question.
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