Electrocardiogram-based feature extraction for machine learning classification of obstructive sleep apnea

Imene Mitiche, Gordon Morison, Brian G. Stewart

Research output: Contribution to journalArticlepeer-review

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This paper introduces a new feature extraction technique based on Time Sequence Analysis, combined with machine learning classification technique called Extreme Learning Machine (ELM), for automatic diagnosis of Obstructive Sleep Apnea (OSA) syndrome. The feature was extracted from Electrocardiogram (ECG) signal of patients with and without OSA. The ECG recordings were labelled as “Apnea” or “Normal” by experts’ examination. The data was freely available online from Physionet database. The feature extraction and classification algorithms were implemented on Matlab environment and the performance was evaluated in terms of OSA detection accuracy percentage. The aim of the study is to provide a low computational feature extraction technique for automatic OSA diagnosis. Simulation results show that OSA detection with 80.3% accuracy is possible using one feature only. It is concluded that the proposed technique offers OSA diagnosis with good enough OSA detection while reducing computation.
Original languageEnglish
Pages (from-to)515-518
Number of pages4
JournalInternational Journal of Signal Processing Systems
Issue number6
Publication statusPublished - Dec 2016


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