Detecting Respiratory Events with End-to-End ConvNet

Abstract

Detecting respiratory events in sleep requires much attention and is labor consuming conventionally. With the development of technology, some kinds of software that can automatically detect the respiratory events was designed to help simplify and improve this process. However, in order to ensure its accuracy of the detection, it is necessary to provide appropriate key parameters before using it. After that the interval adjustment also needs to be done manually, which still takes a lot of time and means high demands on the technicians. In this paper, an end-to-end ConvNet was used to detect the respiratory events which does not need to provide any extra parameters. Its performance was further compared with widely used events detection software, Philips Sleepware G3 with Smonolyzer. The results show that ConvNet has higher accuracy than G3 with Smonolyzer in event detection. Such a ConvNet-based analysis system is sufficiently accurate for event detection according to the AASM classification criteria.