Computer-Aided Detection with a Portable Electrocardiographic Recorder and Acceleration Sensorsfor Monitoring Obstructive Sleep Apnea

Sensors & Transducers

卷:167

Yoon-Nyun Kim¹  Jong-Ha Lee²  Ji-Won Baek²  Dong Eun Kim³ 

[1] Department of Cardiology, School of Medicine, Keimyung University, Dongsan Medical Center, Daegu, South Korea;

[2] Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea ;

[3] Department of Otolaryngology, School of Medicine, Keimyung University, Dongsan Medical Center, Daegu, South Korea;

关键词: Computer-aided diagnosis;    Obstructive sleep apnea;    Acceleration sensor;    Electrocardiography;    Adaboost;    Machine learning.;   

DOI  :  

来源: DOAJ

【 摘 要 】

Obstructive sleep apnea syndrome is a sleep-related breathing disorder that is caused by obstruction of the upper airway. This condition may be related with many clinical sequelae such as cardiovascular disease, high blood pressure, stroke, diabetes, and clinical depression. To diagnosis obstructive sleep apnea, in-laboratory full polysomnography is considered as a standard test to determine the severity of respiratory disturbance. However, polysomnography is expensive and complicated to perform. In this research, we explore a computer-aided diagnosis system with portable ECG equipment and tri-accelerometer (x, y, and z-axes) that can automatically analyze biosignals and test for OSA. Traditional approaches to sleep apnea data analysis have been criticized; however, there are not enough suggestions to resolve the existing problems. As an effort to resolve this issue, we developed an approach to record ECG signals and abdominal movements induced by breathing by affixing ECG-enabled electrodes onto a triaxial accelerometer. With the two signals simultaneously measured, the apnea data obtained would be more accurate, relative to cases where a single signal is measured. This would be helpful in diagnosing OSA. Moreover, a useful feature point can be extracted from the two signals after applying a signal processing algorithm, and the extracted feature point can be applied in designing a computer-aided diagnosis algorithm using a machine learning technique.

【 授权许可】

Unknown