TY - GEN
T1 - Classification of Sleep Disorder from Single Lead Non-overlapping of ECG-apnea based Non-Linear Analysis using Ensemble Approach
AU - Fahruzi, Iman
AU - Eddy Purnama, I. Ketut
AU - Takahashi, H.
AU - Purnomo, Mauridhi H.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - The most significant determinant of quality of life is sleep quality, with better sleep resulting in a healthier and longer life. Polysomnography, or PSG, is a standardized system to get the medical records from multi-lead ECG recordings. However, PSG is a complicated, expensive and time-consuming procedure. Other alternatives include home sleep centre (HSC) development as a tool for early diagnosis and prevention of sleep disorders while keeping high accuracy. HSC uses low-cost equipment by utilizing single-lead ECG and accompanying applications. ECG is one of the media used in diagnosing and analysis of medical information related to sleep disorders. This study aims to develop a computerized sleep diagnosis application to help experts classify symptoms by investigation and evaluation of QRS morphological, time-frequency characteristics, and nonlinear analysis from single-lead ECG recordings. The classification of non-overlapping of ECG-apnea based non-linear analysis using an ensemble approach. The ensemble learning model approach, using the Boosted Tree test, yielded an accuracy of 94.7%, prediction speed of 120 obs/s and training time of 2.374 s. The QRS morphological characteristic and improved non-overlapping ECG recordings provided satisfactory diagnostic performance in sleep disorder classification for HSC usage.
AB - The most significant determinant of quality of life is sleep quality, with better sleep resulting in a healthier and longer life. Polysomnography, or PSG, is a standardized system to get the medical records from multi-lead ECG recordings. However, PSG is a complicated, expensive and time-consuming procedure. Other alternatives include home sleep centre (HSC) development as a tool for early diagnosis and prevention of sleep disorders while keeping high accuracy. HSC uses low-cost equipment by utilizing single-lead ECG and accompanying applications. ECG is one of the media used in diagnosing and analysis of medical information related to sleep disorders. This study aims to develop a computerized sleep diagnosis application to help experts classify symptoms by investigation and evaluation of QRS morphological, time-frequency characteristics, and nonlinear analysis from single-lead ECG recordings. The classification of non-overlapping of ECG-apnea based non-linear analysis using an ensemble approach. The ensemble learning model approach, using the Boosted Tree test, yielded an accuracy of 94.7%, prediction speed of 120 obs/s and training time of 2.374 s. The QRS morphological characteristic and improved non-overlapping ECG recordings provided satisfactory diagnostic performance in sleep disorder classification for HSC usage.
KW - ECG apnea
KW - computerized sleep disorder
KW - ensemble learning
KW - qrs
KW - time-frequency
UR - http://www.scopus.com/inward/record.url?scp=85077780536&partnerID=8YFLogxK
U2 - 10.1109/ICAwST.2019.8923415
DO - 10.1109/ICAwST.2019.8923415
M3 - Conference contribution
AN - SCOPUS:85077780536
T3 - 2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
BT - 2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Awareness Science and Technology, iCAST 2019
Y2 - 23 October 2019 through 25 October 2019
ER -