Pre-determination of OSA degree using morphological features of the ECG signal
作者：
Highlights：
• 30 OSA patients were automatically classified using electrocardiogram (ECG) data.
• In total, 29,127 epochs identified as mild, moderate, and severe.
• Fifteen morphological features were extracted from these epochs.
• Success rates of 97.20 ± 2.15% and 90.18 ± 8.11% with the SBFS algorithm were obtained.
• ANN, NB, RF, DT, LOGR and SVM classifiers were used to obtain the best result.
摘要
• 30 OSA patients were automatically classified using electrocardiogram (ECG) data.• In total, 29,127 epochs identified as mild, moderate, and severe.• Fifteen morphological features were extracted from these epochs.• Success rates of 97.20 ± 2.15% and 90.18 ± 8.11% with the SBFS algorithm were obtained.• ANN, NB, RF, DT, LOGR and SVM classifiers were used to obtain the best result.
论文关键词：Artificial neural networks,ECG,morphological features,OSA degree
论文评审过程：Received 12 December 2016, Revised 22 March 2017, Accepted 23 March 2017, Available online 23 March 2017, Version of Record 30 March 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.049
