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Machine learning workflow for edge computed arrhythmia detection in exploration class missions

Medicine and Health

Machine learning workflow for edge computed arrhythmia detection in exploration class missions

C. Mani, T. S. Paul, et al.

This groundbreaking research by Cyril Mani, Tanya S. Paul, Patrick M. Archambault, and Alexandre Marois reveals a machine learning pipeline capable of detecting cardiac arrhythmias during deep-space missions, showcasing a decision tree classifier with impressive accuracy on EC data from wearable devices.

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Playback language: English
Abstract
This work demonstrates a machine learning pipeline for detecting cardiac arrhythmias (Normal Sinus Rhythm (NSR), Atrial Fibrillation (AFIB), and Atrial Flutter (AFL)) on wearable devices during deep-space missions. Using 742 hours of ECG data, a self-optimizing training scheme selected a decision tree classifier achieving a macro F1-score of 0.899. The ONNX-translated pipeline processed data in 9.2 s/sample, enabling edge computing on wearable devices.
Publisher
npj Microgravity
Published On
Jun 12, 2024
Authors
Cyril Mani, Tanya S. Paul, Patrick M. Archambault, Alexandre Marois
Tags
cardiac arrhythmias
wearable devices
machine learning
deep-space missions
ECG data
decision tree classifier
edge computing

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