@inproceedings{3ebb31d956a744deabc373dfb46867f0,
title = "An electrocardiography system design for obstructive sleep apnea detection based on improved lomb frequency analysis algorithm",
abstract = "In this paper, we present a real-time method for obstructive sleep apnea (OSA) detection of frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV). The method is computationally simple with ECG signals to determine the time interval of OSA. We compare it to a traditional complexly Polysomnography (PSG) which needs several physiological signals measured from patients. For the wearable real-time application, the simplified Lomb Periodogram is proposed to perform the frequency analysis of EDR and HRV. The data from 900 ECG recordings from MIT PhysioNet Sleep Apnea database was utilized in the paper. The approximated method of OSA method obtained the highest Specificity (Sp) 90.1%, Sensitivity (Se) 94.3%, and Accuracy 92.1%.",
author = "Wai-Chi Fang and Chen, {I. Wei} and Fan, {Shu Han} and Lee, {Chih Kuo}",
year = "2018",
month = mar,
day = "23",
doi = "10.1109/BIOCAS.2017.8325151",
language = "English",
series = "2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings",
address = "United States",
note = "2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 ; Conference date: 19-10-2017 Through 21-10-2017",
}