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Found 2 papers, 2 papers with code
Scalable Machine Learning Architecture for Neonatal Seizure Detection on Ultra-Edge Devices
Since there is a need for the classification of bio-signals to be computationally inexpensive in the case of seizure detection, this research presents a machine learning (ML) based architecture that operates with comparable predictive performance as previous models but with minimum level configuration.
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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