Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO₂ signals

Abstract

Sleep apnoea is a potentially serious sleep disorder that is characterised by repetitive episodes of breathing interruptions. Traditionally, sleep apnoea is commonly diagnosed in an attended sleep laboratory setting using polysomnography (PSG). The manual diagnosis of sleep apnoea using PSG is, however complex, and time-consuming, as many physiological variables are usually measured overnight using numerous sensors attached to patients. In PSG sleep laboratories, an expert human observer is required to work overnight, and the diagnosis accuracy is dependent on the physician’s experience. A quantitative and objective method is required to improve the diagnosis efficacy, decrease the complexity and diagnosis time and to ensure a more accurate diagnosis. The purpose of this study was then to develop an automatic sleep apnoea and severity classification using a simultaneously recorded electrocardiograph (ECG) and saturation of oxygen (SpO₂) signals based on a machine learning algorithm. Different ECG and SpO₂ time domain and frequency domain features were extracted for training different machine learning algorithms. For sleep apnoea classification, an accuracy of 99.1%, specificity of 98.1% and sensitivity of 100% were achieved using a support vector machine (SVM) based on combined ECG and SpO₂ features. Similarly, for severity classification, an 88.9% accuracy, 90.9% specificity and 85.7% sensitivity have been obtained. For both apnoea and severity classification, using the combined features was found to be more accurate, and this is typically important when either channel is poor quality, the system can make an analysis based on the other channel and achieve good accuracy.

Keywords:

• Classification
• ECG
• PSG
• SpO₂
• SVM
• severity
• sleep apnoea

Acknowledgements

The resources required for this research were provided by the school of Biomedical Engineering, Jimma institute of Technology, Jimma University and Jimma University Medical Center (JUMC). Our gratitude also goes to Hallelujah General Hospital which allowed us collect data from their sleep laboratory.

Ethics approval and consent to participate

This research has been approved by Jimma University’s institutional review board (IRB) and has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Author contributions

All authors contributed equally in this study. All authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

Resources required for this study were provided by the school of Biomedical Engineering, Jimma institute of Technology, Jimma University, Ethiopia.