Toward Sleep Apnea Detection with Lightweight Multi-scaled Fusion Network
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Updated
Aug 22, 2024 - Python
Toward Sleep Apnea Detection with Lightweight Multi-scaled Fusion Network
Docker Image for Open Source CPAP Analysis Reporter (OSCAR)
A compact U-Net-inspired convolutional neural network with 740,551 parameters, designed to predict non-apnea sleep arousals from full-length multi-channel polysomnographic recordings at 5-millisecond resolution. Achieves similar performance to DeepSleep with lower computational cost.
Screening Solution for Obstructive Sleep Apnea.
Repository for the Machine Learning for Smart Health System course offered by Dr. Juber Rahman at Omdena School platform. Join the course here https://omdena.com/omdena-school/
Tool to import .edf files (particularly from CPAP machines) to influxdb or victoriametrics.
Master MVA - Parsimonious Representations Project
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