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Development and evaluation of a novel signal processing strategies for the interpretation of pediatric respiratory signals
Respiratory activity is crucial to identify disease in children. However, it is difficult to get reliable readings in the field. In this work we develop a novel, reliable algorithms to assess respiratory rate.
Keywords: respiratory rate, embedded signal processing, medical data,
The Emerging Technologies for Neonates in Africa (ETNA) clinical trial aims to evaluate and optimize selected newborn care devices currently in development, to provide evidence on the accuracy and performance for use in neonates. As part of this evaluation, raw capnography (CO2) data will be captured from the reference device and we require a robust algorithm to identify breaths within this waveform and the signal quality at each breath. Identified breaths can then be used to calculate a respiratory rate to compare to the reported respiratory rate of the new newborn care devices.
The Emerging Technologies for Neonates in Africa (ETNA) clinical trial aims to evaluate and optimize selected newborn care devices currently in development, to provide evidence on the accuracy and performance for use in neonates. As part of this evaluation, raw capnography (CO2) data will be captured from the reference device and we require a robust algorithm to identify breaths within this waveform and the signal quality at each breath. Identified breaths can then be used to calculate a respiratory rate to compare to the reported respiratory rate of the new newborn care devices.
To develop reliable respiratory rate algorithms for wearables and embedded medical systems
To develop reliable respiratory rate algorithms for wearables and embedded medical systems
ETH for Development (ETH4D) aims to develop innovations that are directly relevant to improving the livelihoods of people in low-resource settings and to educate future leaders in sustainable development.