Automated detection of obstructive sleep apnoea by single-lead ECG through ELM classification

This study aims to provide automated screening of obstructive sleep apnoea (GSA) by ECG signal processing. Using ECG as an GSA diagnosis tool is an attractive alternative as it is low-cost and the diagnostic test can be performed at home. Single-lead ECG recordings were used to detect apnoeic events through a minute-by-minute analysis. The MIT PhysioNet Apnea-ECG database was used. It contains 70 overnight ECG recordings from normal and obstructive sleep apnoea patients. Thirty-five recordings were used for training data and the other 35 for testing. Time and frequency domain features were obtained. Classification was achieved with an Extreme Learning Machine (ELM) as it provided a flexible non-linear classifier that was fast to train. Classification accuracy was obtained with the hiddenlayer neurons per input (fan-out) varying between 1 and 10. The highest accuracy was 87.7%, at a fan-out of 10, with specificity of 91.7% and sensitivity of 81.3%. Gur results were comparable with other published systems using the Apnea-ECG database. GSA can be diagnosed from a single-lead ECG with a high degree of accuracy.