Real-time sleep apnea detection with one-channel ECG based on edge computing paradigm
Date Issued
2018-09
Author(s)
Stojanovski, Aleksandar
Koceski, Saso
Trajkovikj, Vladmir
Abstract
Sleep apnea is a disorder that causes people to stop breathing multiple
times during their sleep, when untreated. It can be diagnosed trough polysomnography (PSG), which is a time consuming, expensive and must be performed in
special laboratories. Due to its complexity, different alternatives to PSG have been
developed. This paper presents a system based on the edge-computing paradigm
for detection and alerting of sleep apnea events, using data from a single-channel
ECG sensor. A framework for automated feature selection is used for the extraction and selection of the important features. Some ECG signal specific features
were also added to the generic framework. We have evaluated several machine
learning algorithms for sleep apnea detection based on the generic features and
the ECG-specific features on a dataset containing 70 recordings, available in the
PhysioNet database. The obtained results show that the combination of generic
features and ECG-specific features improve the detection accuracy to up to 82%
with a small set of about 20 computationally efficient features.
times during their sleep, when untreated. It can be diagnosed trough polysomnography (PSG), which is a time consuming, expensive and must be performed in
special laboratories. Due to its complexity, different alternatives to PSG have been
developed. This paper presents a system based on the edge-computing paradigm
for detection and alerting of sleep apnea events, using data from a single-channel
ECG sensor. A framework for automated feature selection is used for the extraction and selection of the important features. Some ECG signal specific features
were also added to the generic framework. We have evaluated several machine
learning algorithms for sleep apnea detection based on the generic features and
the ECG-specific features on a dataset containing 70 recordings, available in the
PhysioNet database. The obtained results show that the combination of generic
features and ECG-specific features improve the detection accuracy to up to 82%
with a small set of about 20 computationally efficient features.
Subjects
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