A Novel Wearable Real-Time Sleep Apnea Detection System Based on the Acceleration Sensor
Özet
Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that, it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. Methods: In this study, a device is developed to detect apnea events to alert the patient. The device records continuously accelerations on the diaphragm by using an acceleration sensor, which is placed on the patient's diaphragm. When the apnea is detected by the accelerometer-based system, a signal is sent to the wristband, and then the vibration motor on the wristband vibrates until the patient starts breathing again. The force of the vibration motor can be adjusted according to the patients' sleep debt, especially for elderly, disabled, or pediatric patients. There is no need for a sleeping room to see the patients' breathing properties since those parameters can be stored by using the developed device on a secure digital memory card (SD) at patients' homes during sleeping. Results: A study group were formed of 10 patients (4 males (40%) and 6 females (60%)) with different characteristics ((mean] age, 36.3; height, 169.6 cm; and body weight, 81.4 kg). The patients in the study group have sleep apnea (SA). All the apnea events were detected, and all the patients were successfully alerted. Also, the lying position, which is a significant issue, is performed in this study. Conclusions: This work proposed using an acceleration sensor as a reliable method of sleep apnea screening, detection of an apnea event, sending alert to the patient, and detection of the patient lying position. The developed device is more economical, comfortable, and convenient than existing systems for not only the patients but also the doctors. The patients can easily use this device in their home environment. (C) 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.
