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End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices
The feature engineering employed in this research catered to optimizing the resource-efficient classifier used in the proposed pipeline, which was able to outperform the best performing standard ML model by $10^5\times$ in terms of memory footprint with a mere trade-off of 2% classification accuracy.
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